Designing Variational Quantum Algorithms with Tequila

uh we basically as folks arrive we'll uh we basically as folks arrive we'll keep keep uh keep uh inviting them to introduce uh keep uh inviting them to introduce themselves themselves right and depending on you know if we right and depending on you know if we reach 9 30 reach 9 30 or not we can um we can basically or not we can um we can basically so when albert joins then we can we can so when albert joins then we can we can do the talk do the talk right so i see david is joining my david right so i see david is joining my david um so uh basically um so uh basically uh let me start and also we have uh uh let me start and also we have uh jeremiah coleman who is jeremiah coleman who is the one of the stanford quantum folks the one of the stanford quantum folks and and last time i mentioned that we are last time i mentioned that we are welcoming uh welcoming uh new community organizers we really want new community organizers we really want to open this up all right so what to open this up all right so what what i i'm gonna do i'm gonna just you what i i'm gonna do i'm gonna just you know introduce myself know introduce myself and maybe introduce jeremiah and maybe and maybe introduce jeremiah and maybe jeremiah can ask you guys jeremiah can ask you guys kind of to continue let's see how this kind of to continue let's see how this goes so um goes so um uh so i'm alexi krubrov uh i'm the uh so i'm alexi krubrov uh i'm the organizer of organizer of this quantum conversations if you guys this quantum conversations if you guys are joining us for the first time are joining us for the first time um uh i think i mostly see folks who um uh i think i mostly see folks who were here before but basically uh it was were here before but basically uh it was supposed to be a physical supposed to be a physical series of meetings uh originally series of meetings uh originally organized with ibm organized with ibm right and in the bay area so you know right and in the bay area so you know that's why we're so happy to have that's why we're so happy to have stanford quantum with us which is the stanford quantum with us which is the biggest biggest uh i think uh student group at stanford uh i think uh student group at stanford and and probably in the bay area um probably in the bay area um folks interested in this right so the folks interested in this right so the original plan was you know let's do this original plan was you know let's do this meetings meetings uh in san francisco let's host them at uh in san francisco let's host them at berkeley at stanford at berkeley at stanford at uc davis and kind of local places uc davis and kind of local places right and kind of quantum west uh it was right and kind of quantum west uh it was called called but obviously uh the the pandemic struck but obviously uh the the pandemic struck and we and we had to rethink that so we did uh had to rethink that so we did uh meetings online meetings online and uh we continued uh and uh we continued uh to to do that um scene so we we're doing to to do that um scene so we we're doing this every this every last wednesday of the month uh and we last wednesday of the month uh and we are now doing one main talk are now doing one main talk right so we did a couple but for some right so we did a couple but for some focus was too long and uh we're trying focus was too long and uh we're trying to do this in the morning so we can to do this in the morning so we can capture capture some of the european folks who want to some of the european folks who want to join us right so that's join us right so that's and so today we're going to have one and so today we're going to have one talk uh i see album talk uh i see album is joining nice um is joining nice um and so okay so that's kind of you know and so okay so that's kind of you know uh uh me and uh uh uh me and uh i'll let jeremiah introduce himself and i'll let jeremiah introduce himself and after that uh after that uh we'll you know the hardest problem of we'll you know the hardest problem of computing will be how we computing will be how we you know go along the list uh asking you know go along the list uh asking everybody just once so we'll see how everybody just once so we'll see how we can manage that so do everybody take we can manage that so do everybody take it away introduce yourself it away introduce yourself great no yeah thank you alexi um so i'm great no yeah thank you alexi um so i'm jeremiah i'm a current senior jeremiah i'm a current senior at stanford university i study at stanford university i study engineering physics and economics engineering physics and economics with concentration in quantum science with concentration in quantum science and quantum engineering and quantum engineering um as alexis mentioned i'm the president um as alexis mentioned i'm the president of the stanford quantum computing of the stanford quantum computing association association um and my like interest i guess lie in um and my like interest i guess lie in kind of quantum hardware i had an kind of quantum hardware i had an internship internship this past summer with google quantum ai this past summer with google quantum ai as a hardware research intern as a hardware research intern and i'm excited to be here again i'm and i'm excited to be here again i'm glad to help facilitate this um glad to help facilitate this um conversations and hopefully get stanford conversations and hopefully get stanford more involved and bring more of the more involved and bring more of the stanford community stanford community along with it thank you along with it thank you uh so maybe you know me you can kind of uh so maybe you know me you can kind of go down the list and go down the list and and ask folks introduce themselves uh and ask folks introduce themselves uh i'll just start with alba alba is i'll just start with alba alba is our speaker and she is joining us our speaker and she is joining us yourself in a kind of under a minute and yourself in a kind of under a minute and then of course you will have more time then of course you will have more time to uh to uh to uh elaborate with you uh to uh elaborate with you uh uh when you do the talk sorry did you ask to introduce myself or sorry did you ask to introduce myself or yes listen yes listen like we'll do one minute introductions like we'll do one minute introductions as we go down the list and then okay as we go down the list and then okay okay okay nice uh hello everyone i'm malva cervera nice uh hello everyone i'm malva cervera i'm a postdoctoral fellow in the i'm a postdoctoral fellow in the university of toronto university of toronto i'm working in atlanta's pool cruising i'm working in atlanta's pool cruising group mater lab group mater lab and i'm working in new york air quantum and i'm working in new york air quantum computing computing computing tools as i would present today computing tools as i would present today and also new algorithms etc and also new algorithms etc but my my background is also in quantum but my my background is also in quantum information so i have some backgrounds information so i have some backgrounds in foundations of quantum mean for like in foundations of quantum mean for like balinese qualities multi-party balinese qualities multi-party entanglement etc entanglement etc i study physics and also particle i study physics and also particle physics so i also did some works on physics so i also did some works on entanglement in particular phenomena and entanglement in particular phenomena and particle physics phenomena particle physics phenomena and in general i'm interested in and in general i'm interested in anything related with quantum computing anything related with quantum computing but also in quantum information in but also in quantum information in general general thank you and obviously you're the thank you and obviously you're the speaker today so looking forward to your speaker today so looking forward to your talk talk let's see uh amir ibrahimi hi there my name is amir abrahimi i'm hi there my name is amir abrahimi i'm out here in west marin california out here in west marin california um i work at unity technologies in our um i work at unity technologies in our labs group on a labs group on a machine learning deep learning inference machine learning deep learning inference engine called barracuda engine called barracuda and my story is that i'm bootstrapping and my story is that i'm bootstrapping in the realm of quantum computing until in the realm of quantum computing until i become a i become a useful researcher very good useful researcher very good great to have again uh david matsuyoti great to have again uh david matsuyoti hey alexi how you doing today how are hey alexi how you doing today how are you all right good you all right good uh so i'm a professor at university of uh so i'm a professor at university of chicago uh here in chicago today we have chicago uh here in chicago today we have a bit of sunny weather which is good a bit of sunny weather which is good my interest is in quantum chemistry and my interest is in quantum chemistry and quantum information quantum information uh so all things quantum certainly very uh so all things quantum certainly very excited about excited about some of the research happening on some of the research happening on quantum computing and looking forward to quantum computing and looking forward to today's talk today's talk great great coming back i love it great great coming back i love it indranil day indranil day uh hey everyone uh i'm indra nilde i'm uh hey everyone uh i'm indra nilde i'm from from bangalore india so i basically work with bangalore india so i basically work with jp morgan and recently i've started my jp morgan and recently i've started my journey on quantum with journey on quantum with our research team in jp morgan so i am i our research team in jp morgan so i am i think i am just in norway have started think i am just in norway have started and this is and this is the second uh quantum corporations which the second uh quantum corporations which i'm attending i'm attending great welcome uh let's see great welcome uh let's see nicolas sabaya hey alexey hey how's it going hey alexey hey how's it going uh my name is nicholas sawana i finished uh my name is nicholas sawana i finished my phd my phd a couple of years ago in alana spuroguza a couple of years ago in alana spuroguza group also group also so say hi to him when you see him i did so say hi to him when you see him i did a phd in chemical physics a phd in chemical physics and i was after undergrad in mechanical and i was after undergrad in mechanical engineering and chemistry engineering and chemistry i'm now at intel labs in santa clara i'm now at intel labs in santa clara where uh there's two of us working on where uh there's two of us working on quantum algorithms so i focus primarily quantum algorithms so i focus primarily on on uh i guess algorithms related to uh i guess algorithms related to hamiltonian simulation hamiltonian simulation and uh and also new applications in and uh and also new applications in chemistry especially spectroscopy chemistry especially spectroscopy and uh vibrational degrees of freedom and uh vibrational degrees of freedom great yes and i wanted to say we met great yes and i wanted to say we met with nicolas at the simon's institute with nicolas at the simon's institute uh and little we knew this would be one uh and little we knew this would be one of the last physical meetings of the last physical meetings right we could enjoy uh in in right we could enjoy uh in in the area but you know this is thankfully the area but you know this is thankfully we can do this online so great to see we can do this online so great to see you you uh likewise rishi sweetheart uh likewise rishi sweetheart hey all uh my name is rishi and i'm a hey all uh my name is rishi and i'm a master's student at master's student at chalmers university in sweden so i'm chalmers university in sweden so i'm i'm i did my bachelor's in electrical i'm i did my bachelor's in electrical engineering and i'm trying to pivot into engineering and i'm trying to pivot into physics more physics quantum and my physics more physics quantum and my thesis was done using thesis was done using tensor networks to model variational tensor networks to model variational quantum algorithms like uh quantum algorithms like uh so using tensor networks you can sort of so using tensor networks you can sort of study how study how you can play around with entanglement you can play around with entanglement and and study how loss of entanglement affects study how loss of entanglement affects the performance of these algorithms and the performance of these algorithms and so i'm really excited for today's stuff so i'm really excited for today's stuff uh uh yeah great welcome welcome ah yeah great welcome welcome ah sasha okay hello um my name is okay hello um my name is i'm in argentina i did my masters in i'm in argentina i did my masters in physics a few years ago i physics a few years ago i started doing financial work i did started doing financial work i did met this team and through jp morgan the met this team and through jp morgan the defeat the first defeat the first chat i think or the second one and i'm chat i think or the second one and i'm coming here and now i'm doing a lot of coming here and now i'm doing a lot of financial work but i'm still really financial work but i'm still really trying to get involved into the quantum trying to get involved into the quantum things because i i enjoyed a lot of when things because i i enjoyed a lot of when i was doing my master's so i was doing my master's so i tried not to leave that great welcome and you know i i see a great welcome and you know i i see a theme theme actually uh there are several folks who actually uh there are several folks who are are currently um software engineers more currently um software engineers more traditional traditional setups especially in the bay or you know setups especially in the bay or you know other other uh software companies so i and actually uh software companies so i and actually i started receiving i started receiving uh uh requests from my friends for uh uh requests from my friends for software engineers software engineers who want to actually pivot into quantum who want to actually pivot into quantum so maybe we can have so maybe we can have you know we have time maybe today you you know we have time maybe today you know after the main talk know after the main talk uh so i said you're here you right like uh so i said you're here you right like there are several folks who there are several folks who are thinking of kind of pivoting maybe are thinking of kind of pivoting maybe we can have a little bit of a discussion we can have a little bit of a discussion right how folks go about it because i right how folks go about it because i think it's kind of a recurring theme think it's kind of a recurring theme so uh because we have one main problem so uh because we have one main problem but we have you know additional time we but we have you know additional time we can use it can use it whatever we want uh jeremiah do you want whatever we want uh jeremiah do you want to take the to take the like uh you know moderate subsequent like uh you know moderate subsequent introductions introductions all right take it away that's great yeah all right take it away that's great yeah um oh song sang hi uh i'm appearing in berkeley from hi uh i'm appearing in berkeley from bragita valleys group bragita valleys group so my focus is on a quantum feedback so my focus is on a quantum feedback control and quantum error correction so control and quantum error correction so i i just would like to hear about what just would like to hear about what aquila can do aquila can do into this talk thanks great awesome yeah thank you um next we great awesome yeah thank you um next we have uh have uh wim lavish that is wim lavish that is video hi i'm bin laden lawrence's video hi i'm bin laden lawrence's berkeley lab berkeley lab i got this email forwarded from bertie i got this email forwarded from bertie young i work both on the advanced young i work both on the advanced content testbed from doe i work on the content testbed from doe i work on the the software stack the software stack and then in batch group i work on and then in batch group i work on classical optimizers for graphically classical optimizers for graphically qaoa qaoa et cetera awesome et cetera awesome great thank you i think also eddie fary great thank you i think also eddie fary you joined a few minutes ago you joined a few minutes ago you want to introduce yourself real you want to introduce yourself real quick hi my name is eddie and i um i hi my name is eddie and i um i i work on quantum algorithms i just i work on quantum algorithms i just somehow got a random email about this i somehow got a random email about this i thought it'd be interesting thought it'd be interesting i'm home all the time so i thought i'd i'm home all the time so i thought i'd go to a meeting you know go to a meeting you know i work i work for google but i i work i work for google but i have an affiliation still with mit and have an affiliation still with mit and i'm in brookline massachusetts today so i'm interested in what's going on so i'm interested in what's going on welcome welcome welcome yes and then welcome welcome welcome yes and then also uh also uh last but not least i was young oh hi last but not least i was young oh hi everybody uh this is everybody uh this is jung jong and i work at the stafford jung jong and i work at the stafford research community center research community center and i'm a computer computational chemist and i'm a computer computational chemist i'm very interested in algorithms in i'm very interested in algorithms in quantum chemistry quantum chemistry and app and quantum computing and i'm and app and quantum computing and i'm working on some working on some related projects right now and i'm related projects right now and i'm really looking forward to today's talk really looking forward to today's talk thank you all right i guess that's about all right i guess that's about everybody thank you jeremiah everybody thank you jeremiah so uh i think uh uh so uh i think uh uh we'll we'll you know keep adding folks we'll we'll you know keep adding folks uh as they join uh as they join um but uh um but uh i think now we can proceed uh with our i think now we can proceed uh with our main talk um let's see okay diego main talk um let's see okay diego garcia martin is joining right now let's garcia martin is joining right now let's do one more do one more uh hi diego introduce yourself hello i'm diego i'm a phd student at hello i'm diego i'm a phd student at barcelona supercomputing center and i barcelona supercomputing center and i work on work on quantum algorithms great welcome quantum algorithms great welcome so we have barcelona connections awesome so we have barcelona connections awesome really cool really cool we cannot be in this city physically but we cannot be in this city physically but we can remember it we can remember it right through the folks who are there uh right through the folks who are there uh so so uh our main talk is by uh alba cervera uh our main talk is by uh alba cervera lierta uh uh it's really great you know lierta uh uh it's really great you know uh she was introduced uh by uh uh to me uh she was introduced uh by uh uh to me by by by by uh sebastian from my ibm who is again uh uh sebastian from my ibm who is again uh basically a co-founder of the series so basically a co-founder of the series so it's really exciting uh to expand uh it's really exciting uh to expand uh our field uh i'll let you introduce our field uh i'll let you introduce yourself again yourself again uh so the you know the way we do the uh uh so the you know the way we do the uh cunes cunes uh you guys are welcome to mention uh uh you guys are welcome to mention uh them uh to ask questions in the shot them uh to ask questions in the shot right and and everybody can see them and right and and everybody can see them and so all but you can also see the so all but you can also see the questions questions it's really up to you when you want to it's really up to you when you want to take questions and obviously at any take questions and obviously at any point you can just ask point you can just ask questions and folks can can can just you questions and folks can can can just you know know uh ask questions by voice so so it's uh ask questions by voice so so it's really up to you how you want to manage really up to you how you want to manage it you know you it you know you you and obviously in the end we will you and obviously in the end we will have a general q a session have a general q a session um so with that uh please take it away um so with that uh please take it away okay thank you alexey for inviting me to okay thank you alexey for inviting me to these talks and to give me the these talks and to give me the opportunity to present this opportunity to present this this uh this project tequila and yeah this uh this project tequila and yeah i'm almost i'm almost a postdoctoral fellow in university of a postdoctoral fellow in university of toronto under the supervision of alanna toronto under the supervision of alanna spuroguzik spuroguzik and my background is in physics and and my background is in physics and particle physics and particle physics and also quantum information in general like also quantum information in general like bell inequalities multiparted bell inequalities multiparted entanglement etc entanglement etc and the last years i've been working in and the last years i've been working in quantum algorithms especially in quantum algorithms especially in near-term quantum algorithms near-term quantum algorithms at the end of my phd i finished my phd at the end of my phd i finished my phd last year in last year in also in barcelona so i know diablo also in barcelona so i know diablo because of that and because of that and and yeah under the supervision of jose and yeah under the supervision of jose nacional torre nacional torre and now i'm here in toronto the last and now i'm here in toronto the last year and during my postdoc year and during my postdoc so thank you everyone for for joining me so thank you everyone for for joining me in this talk and in this talk and please interrupt me and any at any please interrupt me and any at any moment because i probably will not read moment because i probably will not read the chat the chat while i'm presenting because it will be while i'm presenting because it will be difficult to switch all the time difficult to switch all the time so please just interrupt interrupt me at so please just interrupt interrupt me at any point and ask whatever question you any point and ask whatever question you prefer or in the chat and afterwards we prefer or in the chat and afterwards we can just continue discussing that can just continue discussing that okay so i will share my screen okay so i will share my screen let me let me okay can you see my screen now okay perfect so today i'm gonna present the designing so today i'm gonna present the designing variational quantum algorithms with variational quantum algorithms with tequila tequila is a quantum language tequila tequila is a quantum language that my group has developed during the that my group has developed during the last year last year and it's an open source quantum language and it's an open source quantum language so so everybody can just check the code and everybody can just check the code and contribute if you want so we are more contribute if you want so we are more than happy to accept contributors and than happy to accept contributors and suggestions comments whatever so it will suggestions comments whatever so it will present uh present uh how desi how this package works so first how desi how this package works so first of all why tequila why we need this kind of all why tequila why we need this kind of languages of languages then i will go for and i will describe then i will go for and i will describe in more detail tequila happy in more detail tequila happy and then i will go to the basic usage and then i will go to the basic usage like how we like how we construct quantum circuits etc and then construct quantum circuits etc and then i will move to i will move to things that are in general more things that are in general more interesting which is chemistry examples interesting which is chemistry examples and quantum machine learning examples we and quantum machine learning examples we have have much more examples in our tutorial much more examples in our tutorial sections sections and and that's it and then i will close and and that's it and then i will close with what are the with what are the current projects that use tequila and current projects that use tequila and what are the future features that we what are the future features that we would like to add to the would like to add to the to the code so please interrupt me on to the code so please interrupt me on any planet at any moment and as i said any planet at any moment and as i said but yeah but yeah as i will as you will see i will first as i will as you will see i will first present all the features one by one of present all the features one by one of tequila but at the end i will just tequila but at the end i will just present everything present everything wrapping up so it would be probably more wrapping up so it would be probably more useful to understand how it works with useful to understand how it works with uh concrete examples uh concrete examples so as you know we are in the golden era so as you know we are in the golden era of quantum languages we have of quantum languages we have many of them many are appearing we have many of them many are appearing we have uh kisky we have silk that is used by uh kisky we have silk that is used by google google we have bike wheel used by ricketti kibo we have bike wheel used by ricketti kibo which is which is using for a startup called kilimanjaro using for a startup called kilimanjaro uh q-sharp for microsoft penny lane from uh q-sharp for microsoft penny lane from shanadu and many many more are appearing shanadu and many many more are appearing in the end everybody that has developed in the end everybody that has developed a quantum computer a quantum computer is also developing the language to is also developing the language to program the quantum computer as program the quantum computer as it's natural but not only that we are it's natural but not only that we are also in the also in the quantum server we have many quantum quantum server we have many quantum software tools that help us to software tools that help us to to run these algorithms in quantum to run these algorithms in quantum computers we have orchestra from computers we have orchestra from zapata computing mythic which is for zapata computing mythic which is for error mitigation and error mitigation and qlx which is a nice quantum simulator qlx which is a nice quantum simulator and we also have other tools related and we also have other tools related with quantum simulation with quantum simulation in particular for chemistry like fifo in particular for chemistry like fifo psi4 and open fermion psi4 and open fermion and of course many many more and not and of course many many more and not only that but we also have only that but we also have all the classical tools that are useful all the classical tools that are useful for the near for the near and noise intermediate scale quantum and noise intermediate scale quantum computing era which are like computing era which are like the optimizers and and the analytical the optimizers and and the analytical gradients etc gradients etc that are needed for optimizing these that are needed for optimizing these quantum circuits this variational quantum circuits this variational quantum circuits quantum circuits so in the end we have tons of languages so in the end we have tons of languages that that in principle we have to learn and we in principle we have to learn and we have to implement in our algorithms have to implement in our algorithms so for us in particular for me so for us in particular for me sometimes it's difficult to you know sometimes it's difficult to you know remember how to program and how to use remember how to program and how to use all these languages all these languages especially if you want to for instance especially if you want to for instance compare different quantum computers compare different quantum computers so in the end we never know which so in the end we never know which language should i use language should i use it the first time that i started to it the first time that i started to design my algorithm like should i design my algorithm like should i start with kiskit or should i start with start with kiskit or should i start with circ or with any other circ or with any other because maybe at the in the middle of my because maybe at the in the middle of my of my work i decided to run my algorithm of my work i decided to run my algorithm in a different quantum computer in a different quantum computer so i need to write again the same so i need to write again the same algorithm algorithm which is you know sometimes a waste of which is you know sometimes a waste of time for me as a theoretician time for me as a theoretician so it would be very useful to have so it would be very useful to have something that something that takes into account all possible takes into account all possible languages so i only wrote my code once languages so i only wrote my code once and i can run it in different backends and i can run it in different backends easily easily not only that but imagine that i wrote not only that but imagine that i wrote my code in circ but then i my code in circ but then i started to collaborate with people from started to collaborate with people from ibm and i would like to share my code ibm and i would like to share my code with them with them so i need to have a platform that i can so i need to have a platform that i can easily share my code so they can use easily share my code so they can use my code with their own language and not my code with their own language and not only that but also what happens if i only that but also what happens if i just started to use one language and at just started to use one language and at some point it becomes obsolete because some point it becomes obsolete because nobody is supporting that anymore that nobody is supporting that anymore that could happen also especially in could happen also especially in in at this time that many things are in at this time that many things are happening and are changing all the time happening and are changing all the time so i need i would like to have a code so i need i would like to have a code that will work you know that will work you know forever so i can share it with everyone forever so i can share it with everyone it will be there it will be there and it doesn't matter if the particular and it doesn't matter if the particular backend that i use backend that i use change or have new features or just change or have new features or just becomes obsolete so i can still use my becomes obsolete so i can still use my code code in other backend so in the end that's in other backend so in the end that's the general idea that is behind tequila the general idea that is behind tequila and and our motivation as a as a physi as a our motivation as a as a physi as a theoreticians in quantum inform in theoreticians in quantum inform in quantum computation quantum computation we wanted to develop a language that we wanted to develop a language that works with any possible backend works with any possible backend and so for us it will be easy to test and so for us it will be easy to test different algorithms in different places different algorithms in different places and that's what is tequila so it's and that's what is tequila so it's unification standardization unification standardization and acceleration so we don't have to and acceleration so we don't have to waste time in checking waste time in checking in in translating our code to another in in translating our code to another language language so we just focus on writing the code so we just focus on writing the code once in tequila language once in tequila language and then we just choose whatever vacant and then we just choose whatever vacant we prefer the one that is more suitable we prefer the one that is more suitable for us for us and not only that but we also can add and not only that but we also can add more features to that like noise models more features to that like noise models error mitigation etc and more than we error mitigation etc and more than we are working on are working on and that's the idea of tequila and you and that's the idea of tequila and you can check the code is available and can check the code is available and in our group repository and we are in our group repository and we are working in the working in the in the documentation that will be in the documentation that will be available very soon available very soon and also on the release paper but and also on the release paper but everything is written in the in the everything is written in the in the github repo github repo and in the tutorial section is super and in the tutorial section is super useful to understand how this works useful to understand how this works but anyway i will present how this works but anyway i will present how this works in in more detail and some examples so first of all as you know we are in so first of all as you know we are in the noise intermediate scale quantum the noise intermediate scale quantum computation computation which means that we mix together which means that we mix together a classical algorithm a classical a classical algorithm a classical software tool with a quantum hardware software tool with a quantum hardware tool tool and we are in this era because the and we are in this era because the number of qubits that we have is not number of qubits that we have is not enough to perform error correction enough to perform error correction so our qubits are noisy and we only have so our qubits are noisy and we only have a few of them we have a few of them we have and now currently 70 50 qubits working and now currently 70 50 qubits working in in 100 probably in the next years we will 100 probably in the next years we will have have more than 100 but still is not one more than 100 but still is not one million cubits that are the million cubits that are the the ones that we will need for error the ones that we will need for error correcting uh quantum algorithms correcting uh quantum algorithms so in the end is we have a bunch of so in the end is we have a bunch of things and we will try to put everything things and we will try to put everything together and together and produce something that is useful for us produce something that is useful for us something that we call quantum advantage something that we call quantum advantage something that outperforms classical something that outperforms classical computation computation so that means that we have different so that means that we have different qubit architectures that means that the qubit architectures that means that the topology of the chips is different so topology of the chips is different so not always not always not all qubits are connected and and the not all qubits are connected and and the topology changes topology changes drastically from one chip to the to drastically from one chip to the to another we have few qubits we have noise another we have few qubits we have noise we have the we have the the coherence we have any other other the coherence we have any other other sources of error sources of error like crosstalk etc and on top of that we like crosstalk etc and on top of that we have classical optimizers that help us have classical optimizers that help us with the with the variational algorithms with the with the variational algorithms and are very helpful and they are and are very helpful and they are working towards quantum advantage working towards quantum advantage probably in the probably in the near term so in the end that's the the near term so in the end that's the the big picture of it big picture of it and and we want to generate some and and we want to generate some language that will help us to language that will help us to wrap everything all together to produce wrap everything all together to produce this something useful which is the this something useful which is the quantum advantage experiment quantum advantage experiment or experiment or algorithm or experiment or algorithm so the question is what can we do with a so the question is what can we do with a few qubits and how can we deal with the few qubits and how can we deal with the noise so what can we do with noise so what can we do with with a few cubits that's why i use the with a few cubits that's why i use the macgiver picture because it reminds me macgiver picture because it reminds me sometimes to that series i don't know if sometimes to that series i don't know if some of you some of you are familiarized with that or not but i are familiarized with that or not but i used to so this show when i was a kid used to so this show when i was a kid and the idea of having you know a small and the idea of having you know a small resources anything works perfectly but resources anything works perfectly but still you can do something useful so still you can do something useful so that's what we are trying to do with uh that's what we are trying to do with uh in quantum computing and nowadays for in quantum computing and nowadays for achieving this quantum advantage achieving this quantum advantage so for that we have this hybrid quantum so for that we have this hybrid quantum classical algorithms as i said classical algorithms as i said sometimes also called variational sometimes also called variational algorithms uh which are really useful algorithms uh which are really useful because they mix the best of both worlds because they mix the best of both worlds the quantum the quantum hardware machine and then the classical hardware machine and then the classical optimizer and all classical tools that optimizer and all classical tools that are in the in this classical optimized are in the in this classical optimized optimization part and the application of optimization part and the application of this noise intermediate scale are from this noise intermediate scale are from chemistry to quantum machine learning chemistry to quantum machine learning materials etc and also optimization materials etc and also optimization problems problems and in the end the problem with these and in the end the problem with these applications is we also need to compare applications is we also need to compare and benchmark the results with the and benchmark the results with the classical techniques because we cannot classical techniques because we cannot claim quantum advantage we cannot if we claim quantum advantage we cannot if we don't compare with the current state of don't compare with the current state of the art the art of the of these of these problems so in of the of these of these problems so in the end uh the end uh we have many quantum computers in we have many quantum computers in development and we need to benchmark development and we need to benchmark compare compare and test them so we can really say if and test them so we can really say if uh if the we have achieved quantum uh if the we have achieved quantum advantage of if we are working towards advantage of if we are working towards quantum advantage in a in a clear manner quantum advantage in a in a clear manner so these are the software players that so these are the software players that tequila manages tequila manages uh on one side we have the of the uh on one side we have the of the abstract manipulation of wave functions abstract manipulation of wave functions quantum gate definitions the noise quantum gate definitions the noise models etc models etc on the other hand we have all the on the other hand we have all the classical tools that from optimizers classical tools that from optimizers from gradient methods also computational from gradient methods also computational chemistry methods that are also chemistry methods that are also implemented implemented and then all all these things work on and then all all these things work on top of the quantum buckets top of the quantum buckets that can be both real experiments or that can be both real experiments or just quantum simulators so this is i will start with the tequila so this is i will start with the tequila piece step by step piece step by step and as i said the code is available in and as i said the code is available in in our group repository so you can check in our group repository so you can check all the all the all the modules that i will present now all the modules that i will present now and and this is the the general picture this is the the general picture so in general any quantum algorithm we so in general any quantum algorithm we will start with a hamiltonian which is will start with a hamiltonian which is an operator and this hamiltonian somehow an operator and this hamiltonian somehow will will codify our problem could be a molecule codify our problem could be a molecule could be a could be a an optimization problem could be many an optimization problem could be many things but in the end we need an things but in the end we need an operator operator and then we have a quantum circuit that and then we have a quantum circuit that will generate this will generate this an estate that could be parameterized in an estate that could be parameterized in general will be parameterized for general will be parameterized for variational quantum algorithms variational quantum algorithms so uh what we want is to compute the so uh what we want is to compute the expected value of this operator expected value of this operator uh from the wave function generated by uh from the wave function generated by this quantum circuit this quantum circuit and depending on this expected value we and depending on this expected value we will optimize it etc or we would perform will optimize it etc or we would perform some operations on top of that some operations on top of that in many cases we can generate this in many cases we can generate this quantum circuit using a particular quantum circuit using a particular answer answer that is physically inspired which is the that is physically inspired which is the case in quantum chemistry with the case in quantum chemistry with the unitary couple cluster assets and that unitary couple cluster assets and that will be also useful to have some tool will be also useful to have some tool that translates our hamiltonian and that translates our hamiltonian and generates automatically the proper generates automatically the proper answers answers so uh we tequila also implements the so uh we tequila also implements the indi indi this part and in an automatic way so you this part and in an automatic way so you don't need to know all the don't need to know all the subtleties or how how do you how should subtleties or how how do you how should you construct these answers you construct these answers and as i said these hamiltonians in many and as i said these hamiltonians in many cases if we are working in quantum cases if we are working in quantum chemistry chemistry will be a molecule and we have many will be a molecule and we have many tools to tools to translate the molecule into the proper translate the molecule into the proper hamiltonian by hamiltonian by performing some transformations that performing some transformations that jordan bingler for instance jordan bingler for instance and we have already these tools a and we have already these tools a program in open fermium ci4 and other program in open fermium ci4 and other chemistry backends so this is also chemistry backends so this is also implemented in tequila so it will be implemented in tequila so it will be very easy to call these vaccines to very easy to call these vaccines to generate generate the proper hamiltonian and once we have the proper hamiltonian and once we have that that together with the quantum circuit we together with the quantum circuit we compute the expected value and that compute the expected value and that will be our goal with this expected will be our goal with this expected value we construct the objective value we construct the objective function which is the core of tequila so function which is the core of tequila so tequila works with objective functions tequila works with objective functions which means that we can construct an which means that we can construct an expected value and then expected value and then generate an objective function that is generate an objective function that is much more sophisticated than just an much more sophisticated than just an expectation value of the hamiltonian so expectation value of the hamiltonian so for instance we can construct any kind for instance we can construct any kind of function of function that is compo composed of these that is compo composed of these expectation values expectation values and this objective function is the one and this objective function is the one that will be compiled that will be compiled into the quantum backend that could be into the quantum backend that could be either simulator a simulator or a real either simulator a simulator or a real backend backend currently we support cirque hiskid bike currently we support cirque hiskid bike wheel and hulax wheel and hulax and we are working in new ones so that's and we are working in new ones so that's will be the goal of tequila is to expand will be the goal of tequila is to expand this this uh quantum backends as much as possible uh quantum backends as much as possible in the end in the end so we can easily translate our code to so we can easily translate our code to any possible backend any possible backend depending on you know on on the depending on you know on on the application of your code application of your code and here is where we can if we are and here is where we can if we are dealing with simulations dealing with simulations we can also implement some options like we can also implement some options like noise models noise models and also sampling so we can decide if we and also sampling so we can decide if we want to obtain the results by sampling want to obtain the results by sampling or just by simulating the exact wave or just by simulating the exact wave function function using a simulator like q lakhs for using a simulator like q lakhs for instance that works with instance that works with with the wave functions so then with with the wave functions so then with this backend we will go to the optimizer this backend we will go to the optimizer and and the result of the quantum of the quantum the result of the quantum of the quantum backend uh will be backend uh will be translated in this classical optimizer translated in this classical optimizer and here is where we and here is where we have to decide the method that we want have to decide the method that we want to use for for minimization the method to use for for minimization the method options the gradient options the gradient the initial values etc and here we the initial values etc and here we currently support currently support uh some by easing optimization uh some by easing optimization optimizers but also analytical gradients optimizers but also analytical gradients and and of course sci-fi and well-known and and of course sci-fi and well-known minimization algorithms minimization algorithms and this optimizer which just proposed a and this optimizer which just proposed a new set of new set of variables to our quantum circuit and we variables to our quantum circuit and we close the close the the well-known loop of variational the well-known loop of variational quantum algorithms quantum algorithms primarily we can also primarily we can also simulate wave functions draw the circuit simulate wave functions draw the circuit the fine gates from armenian operators the fine gates from armenian operators so it shouldn't be so it shouldn't be uh control not gates or rotational case uh control not gates or rotational case like well no gates wheels we can also like well no gates wheels we can also generate generate any kind of gate that can be represented any kind of gate that can be represented with an armenian operator this is also with an armenian operator this is also supported in tequila supported in tequila so let me move step by step or all these so let me move step by step or all these parts of this tequila parts of this tequila in more detail so first of all the as i in more detail so first of all the as i said the current quantum backend said the current quantum backend supported supported are culax kiskeet cirque bike wheel and are culax kiskeet cirque bike wheel and also a symbolic one also a symbolic one and you can check the available ones by and you can check the available ones by just typing just typing tequila show available simulators and as tequila show available simulators and as you see you see qlex and all of them qlex and all of them support the wave function simulation support the wave function simulation some of them support sampling some of them support sampling some of them super noise etc so in top some of them super noise etc so in top um besides these quantum backends we um besides these quantum backends we also have also have two chemistry backends which are um open two chemistry backends which are um open fermion upside four fermion upside four that also will be useful for generating that also will be useful for generating the hamiltonians of our problems if they the hamiltonians of our problems if they are chemistry problems are chemistry problems the passive quantum gates that we have the passive quantum gates that we have sorry uh i was wondering sorry uh i was wondering could you elaborate on the symbolic back could you elaborate on the symbolic back end that sounds interesting end that sounds interesting i'm not familiarized in particular with i'm not familiarized in particular with this symbolic back end so it's just not this symbolic back end so it's just not that is that is implemented in tequila but especially implemented in tequila but especially for wave functions will be very useful for wave functions will be very useful so it's the only thing that i can say at so it's the only thing that i can say at the moment i'm not familiar with this the moment i'm not familiar with this particular back-end particular back-end but maybe jacob after we we finished the but maybe jacob after we we finished the the presentation jacob which is the the the presentation jacob which is the the principal um principal um researcher involved in this project can researcher involved in this project can elaborate a little bit more elaborate a little bit more great sorry thank you great sorry thank you so as i said the basic quantum gates so as i said the basic quantum gates that are now implemented are rotational that are now implemented are rotational gates gates as you well know the rotational y x etc as you well know the rotational y x etc etc etc and then we also have the face gates and then we also have the face gates paulie and hadamard and paulie and hadamard and swap gate and many of these gates swap gate and many of these gates accepts the control and target which accepts the control and target which means that we can just call the means that we can just call the rotational rotational x-gate for instance and can be a single x-gate for instance and can be a single cubic gate or can be a control uh cubic gate or can be a control uh qubit gate by any controls we can just qubit gate by any controls we can just select as many controls as we prefer select as many controls as we prefer but we should take into account that if but we should take into account that if we want to run this in a real backend we want to run this in a real backend probably this gate will not be supported probably this gate will not be supported so this is useful especially for quantum so this is useful especially for quantum simulator simulator simulation and some gates also accept simulation and some gates also accept the power the power which means that we can uh just call the which means that we can uh just call the x-gate for instance the publix gate and x-gate for instance the publix gate and ask for uh an and use the power ask for uh an and use the power uh and compute the power gate of that uh and compute the power gate of that gate and gate and we also have the phase gates in general we also have the phase gates in general any possible phase but in particular any possible phase but in particular s and t gates are already defined and of s and t gates are already defined and of course halamar and course halamar and swap gates we also have more swap gates we also have more sophisticated gates so for instance we sophisticated gates so for instance we can can generate any possible gate as i said if generate any possible gate as i said if we have we have their median their median operator so their median their median operator so this is the exponential power string this is the exponential power string so we can just call that gate and give a so we can just call that gate and give a public string public string and tequila will generate the and tequila will generate the corresponding gate and we can also corresponding gate and we can also throttle rise our gates so in case that throttle rise our gates so in case that we want to run we want to run some throttle decomposition we can also some throttle decomposition we can also use that in tequila and we have to use that in tequila and we have to provide the generators the angles and provide the generators the angles and the throttle steps of course the throttle steps of course and all these gates uh as i said accept and all these gates uh as i said accept control and target which means that we control and target which means that we can rotarize uh can rotarize uh for instance structuralize a gate and for instance structuralize a gate and then ask for the control um then ask for the control um to perform this gate with as many to perform this gate with as many control qubits as we would prefer so then we have the objectives which is so then we have the objectives which is as i said the tequila chord so this as i said the tequila chord so this class represents the mathematical class represents the mathematical manipulation of manipulation of all the expectation values and these are all the expectation values and these are some examples of how can we construct some examples of how can we construct the objectives so we can have an the objectives so we can have an objective that is just the sum of two objective that is just the sum of two expectation values like e0 expectation values like e0 and e1 or we can have an objective that and e1 or we can have an objective that is is some power of some expectation value and some power of some expectation value and we can even multiply and manipulate we can even multiply and manipulate these of different objectives all these of different objectives all together to generate another one together to generate another one so in the end these objectives are so in the end these objectives are constructed by the by computing the constructed by the by computing the expectation value expectation value and then and they are compiled into the and then and they are compiled into the quantum backend quantum backend so then i will present more some so then i will present more some examples of these objectives examples of these objectives uh in in this talk uh in particular for uh in in this talk uh in particular for instance for quantum machine learning instance for quantum machine learning applications then we have the optimizers applications then we have the optimizers the function is tequila minimize and the function is tequila minimize and there are there are many arguments that are accepted the many arguments that are accepted the mandatory ones are of course the mandatory ones are of course the objective objective so what what's the quantity that you so what what's the quantity that you want to minimize or to optimize want to minimize or to optimize and the other one will be the method to and the other one will be the method to use uh so for instance grading descent use uh so for instance grading descent or lv or lv lpg sorry i always forgot how to lpg sorry i always forgot how to pronounce that pronounce that anyway any adam optimizer etc anyway any adam optimizer etc and then for quantum simulations we have and then for quantum simulations we have uh other possible uh other possible arguments for instance the back end if arguments for instance the back end if we want a quantum simulator we want a quantum simulator or we want a real backend we can just or we want a real backend we can just call that like kiskeet call that like kiskeet circ or qlax etc we can decide if we circ or qlax etc we can decide if we want to sample or not if we don't sample want to sample or not if we don't sample tequila will automatically tequila will automatically simulate the subway function we can also simulate the subway function we can also call the device so if we want to run our call the device so if we want to run our circuit in a circuit in a specific real device like ibm quantum specific real device like ibm quantum tokyo tokyo or any other then we can also call that or any other then we can also call that and we also can call any noise model we and we also can call any noise model we can construct can construct our noise model independently and then our noise model independently and then call that function and so i will present call that function and so i will present after this slide and additional keywords after this slide and additional keywords of course are of course are method options variables initial values method options variables initial values gradient silent the outputs or not so gradient silent the outputs or not so this is something that this is something that is easy to understand if we you start to is easy to understand if we you start to play with it but as you can imagine uh play with it but as you can imagine uh it's everything that is needed for and it's everything that is needed for and for minimizing any for minimizing any any any function these are the current any any function these are the current optimizers supported in in tequila as i optimizers supported in in tequila as i said said sci-fi optimizer also many gradient sci-fi optimizer also many gradient descent and base descent and base optimizers and also some by asian optimizers and also some by asian optimism optimized optimism optimized liponics or gpio optimization liponics or gpio optimization then in gradient methods we can decide then in gradient methods we can decide which kind of gradient we want to which kind of gradient we want to compute compute so we have an analytical gradients which so we have an analytical gradients which are the default and that use are the default and that use jacks and and then we also have jacks and and then we also have numerical gradients of course numerical gradients of course with by calling method to point and then with by calling method to point and then the step size that we would prefer the step size that we would prefer and then but we can also custom and then but we can also custom gradients and we can just generate gradients and we can just generate whatever function whatever function is uh it's better for us to compute that is uh it's better for us to compute that gradients and gradients and call this and call this function using call this and call this function using this syntax this syntax or we can also implement a quantum or we can also implement a quantum natural gradient which is a result a natural gradient which is a result a quite recent result from quite recent result from stock zero and by calling qng stock zero and by calling qng so in the end the idea of tequila is so in the end the idea of tequila is that providing as much flexibility as that providing as much flexibility as possible so if possible so if something is not a program in the something is not a program in the in the library you can just run by in the library you can just run by yourself and use it yourself and use it very easily in a simple line uh we also have recently implemented the uh we also have recently implemented the directing version of iterative subspace directing version of iterative subspace that this that this uh algorithm would which uh algorithm would which help us to convert into machine help us to convert into machine precision and this is very important for precision and this is very important for chemistry simulations and this chemistry simulations and this this works um once we are close to the this works um once we are close to the solution solution and just kicks in when the maximal and just kicks in when the maximal gradient achieve the tolerance gradient achieve the tolerance so these are for instance some benchmark so these are for instance some benchmark simulations uh that compares the simulations uh that compares the some the different optimizers some the different optimizers and then the in particular the and then the in particular the stochastic grain descent with this stochastic grain descent with this this uh optimization so this this will this uh optimization so this this will be useful especially for chemistry as i be useful especially for chemistry as i said when you need said when you need to press huge accuracy in in your result to press huge accuracy in in your result this is also implemented we just have to this is also implemented we just have to specify the tolerance and and that's it and then as i said we have the numerical and then as i said we have the numerical and customized gradients that we can and customized gradients that we can just construct just construct and we use with the gradient-based and we use with the gradient-based optimizers and in the end the idea is optimizers and in the end the idea is that that we want to implement the gradients as we want to implement the gradients as significantly cheaper significantly cheaper with the expectation values etc so as i with the expectation values etc so as i said said the goal of tequila is always to the goal of tequila is always to simplify the effort simplify the effort of programming so everything is already of programming so everything is already programmed but in case it's not programmed but in case it's not you can still use the language so you you can still use the language so you can just program it by yourself can just program it by yourself and call the your method in a single and call the your method in a single line and then just to close with the and then just to close with the optimization part we also optimization part we also have implemented by using optimization have implemented by using optimization in particular phoenix and j in particular phoenix and j biopt and you can check the biopt and you can check the the documentation of these of these the documentation of these of these methods in their methods in their respective repositories but respective repositories but as i said we want to even apply even as i said we want to even apply even more so if you have any suggestions of more so if you have any suggestions of other optimization techniques or other other optimization techniques or other gradient based techniques please let us gradient based techniques please let us know and we are know and we are we will be more than happy to implement we will be more than happy to implement that in the future that in the future and then finally we have the noise and then finally we have the noise models and the problem with noise models models and the problem with noise models is that every is that every backend and deals with it in a different backend and deals with it in a different way way so we needed to find a way to to use so we needed to find a way to to use that in tequila in a unified form that in tequila in a unified form so uh the same code and the same noise so uh the same code and the same noise model can be applied to any other model can be applied to any other backends easily so these are the backends easily so these are the these are the general features that we these are the general features that we take the assumptions that we take into take the assumptions that we take into account so if noise is present account so if noise is present any gate may be affected by noise the any gate may be affected by noise the second one is that noise effects second one is that noise effects and qubit gaze independent of the noise and qubit gaze independent of the noise on mqb gates on mqb gates which means that two qubit gates noise which means that two qubit gates noise is independent of the noise on three is independent of the noise on three qubit gates for instance qubit gates for instance then noise probabilities are independent then noise probabilities are independent on the position of the circuit so it on the position of the circuit so it doesn't matter if the gate is at the doesn't matter if the gate is at the beginning or it's at the end the noise beginning or it's at the end the noise model will be applied exactly the same model will be applied exactly the same in these two gates in these two gates and then the number of qubits involved and then the number of qubits involved in the gate in the gate dictates what noise may occur so dictates what noise may occur so c not gates is not nicer than a control c not gates is not nicer than a control z gates the noise is exactly the same z gates the noise is exactly the same because the only thing that matters is because the only thing that matters is that it's a two cubic gate noise model and then these are the some of the and then these are the some of the supporting simulated backgrounds of of supporting simulated backgrounds of of these noise models with flips face flips these noise models with flips face flips amplitude dams amplitude dams face damps and symmetrical polarizing face damps and symmetrical polarizing phase amplitude damps so these are the phase amplitude damps so these are the very common ones very common ones and you you can just create your noise and you you can just create your noise model by model by combining all of them and generate your combining all of them and generate your own noise model and just by calling own noise model and just by calling noise equal your noise model uh tequila noise equal your noise model uh tequila will implement that will implement that that model so something that to take that model so something that to take into account noise is only supported into account noise is only supported when sampling when sampling obviously so you can you need to specify obviously so you can you need to specify in your minimization or in your in your minimization or in your simulation the samples of your simulation the samples of your of your simulation and also tequila of your simulation and also tequila supports and device noise simulation so supports and device noise simulation so in case that somebody can propose a in case that somebody can propose a particular noise model particular noise model that is suitable for this particular that is suitable for this particular chip you can call it by chip you can call it by by just spelling the name of that device so with this i conclude all these so with this i conclude all these general features of of tequila and i general features of of tequila and i will will start with more examples and particular start with more examples and particular usage of tequila starting with the usage of tequila starting with the most basic ones which is creating a most basic ones which is creating a quantum circuit quantum circuit so for instance if we want to create a so for instance if we want to create a quantum circuit with a hammer gate and a quantum circuit with a hammer gate and a control not gate which is called the control not gate which is called the kill against hadamard and kill against hadamard and specified the target we can grab the specified the target we can grab the target equal target equal whatever cubit or we can just put the whatever cubit or we can just put the then the qubit then the qubit number inside of it and by default it number inside of it and by default it will tequila will understand will tequila will understand that that's the target and then once we that that's the target and then once we have our circuit we can decide have our circuit we can decide to print that circuit or we can draw the to print that circuit or we can draw the circuit circuit and not only drawing in this particular and not only drawing in this particular manner but we can just manner but we can just use particular drawing and use particular drawing and of of some of the backends for instance of of some of the backends for instance kiskit kiskit prefers to draw circuits in a different prefers to draw circuits in a different way so we can also do that if we prefer way so we can also do that if we prefer this other form so in the end since all this other form so in the end since all kisket is implemented in kisket is implemented in in tequila we can use any feature that in tequila we can use any feature that kisket has kisket has in tequila another way to in tequila another way to to construct the circuit uh if we want to construct the circuit uh if we want to implement hadamard gates in more than to implement hadamard gates in more than one qubit we can just specify that in a one qubit we can just specify that in a vector in a list form vector in a list form so it will be easier to or it will be so it will be easier to or it will be more compact to more compact to to construct this circuit so this is to construct this circuit so this is also supported then we have the quantum circuit gates then we have the quantum circuit gates that i implement i explained before but that i implement i explained before but let's see some particular examples of let's see some particular examples of them so we have predefined gates versus them so we have predefined gates versus poly strings and control poly strings and control target definition so we can either call target definition so we can either call rotational white gate with angle 1 and rotational white gate with angle 1 and target 0 target 0 plus the x gate or we can just um plus the x gate or we can just um as for this poly string in this case the as for this poly string in this case the uh uh the poly y gate and it will generate the poly y gate and it will generate exactly the same gate so exactly the same gate so the the the good thing of having this uh the the the good thing of having this uh rp gate is that we can just decide any rp gate is that we can just decide any possible poly string possible poly string of course it's only y or x or z or z of course it's only y or x or z or z they are predefined they are predefined but if not you can still use them and but if not you can still use them and these two ways these two ways are exactly the same in delivers exactly are exactly the same in delivers exactly the same circuit the same circuit then we have power gates so for instance then we have power gates so for instance we can call y-gate and we can call y-gate and specify the power and specify the power and and we also have public strings versus and we also have public strings versus structuralization in this case structuralization in this case both gates are the same so as i said you both gates are the same so as i said you can just specify your published strings can just specify your published strings as in this case x and y or you can also as in this case x and y or you can also throttlerize your gate using the throttlerize your gate using the specifying the generator in this case specifying the generator in this case probably x and y then with wavefunction measurements we then with wavefunction measurements we can simulate the wave function by just can simulate the wave function by just spelling the q simulate and in spelling the q simulate and in particular we can specify the backend if particular we can specify the backend if we don't specify the backend we don't specify the backend tequila will find the backend that tequila will find the backend that supports a wave function simulation supports a wave function simulation and this is particularly useful in my and this is particularly useful in my opinion when you want to check if your opinion when you want to check if your code is working properly or if you don't code is working properly or if you don't mess up any part of your circuit mess up any part of your circuit especially with the small simulations especially with the small simulations and then we can simulate the circuit but and then we can simulate the circuit but this can this time by this can this time by sampling so automatically we will obtain sampling so automatically we will obtain the measurements in this case the measurements in this case uh we obtain 10 times the state 0 0. uh we obtain 10 times the state 0 0. we can print the measurements and and we can print the measurements and and specify which measurements we would like specify which measurements we would like to print so if we are only interested to print so if we are only interested in some basis elements we can also in some basis elements we can also specify that specify that or we can just we can simulate the or we can just we can simulate the measurements using this measurements using this other syntax and then we have the parameterized and then we have the parameterized quantum circuits uh we define the quantum circuits uh we define the variables first we variables first we need to define the tequila variable and need to define the tequila variable and then we introduce that variable into our then we introduce that variable into our quantum circuit quantum circuit and then we can simulate the wave and then we can simulate the wave function by specifying the value of that function by specifying the value of that simulation we can also print the circuit simulation we can also print the circuit and draw the circuit with this this and draw the circuit with this this value value and as i will show you uh that will be and as i will show you uh that will be useful when we want to optimize the useful when we want to optimize the circuit as a function of these values circuit as a function of these values so we can also extract the variables so so we can also extract the variables so if we are dealing with tons of variables if we are dealing with tons of variables for some reason and we don't remember for some reason and we don't remember exactly how many exactly how many variables we have we can extract this by variables we have we can extract this by these variables easily these variables easily or if we construct our circuit in a loop or if we construct our circuit in a loop so we so we don't remember exactly how many don't remember exactly how many variables it has we can also extract variables it has we can also extract those variables and we can also optimize those variables and we can also optimize some of these variables and let the some of these variables and let the others others and let the others without touching and let the others without touching during the minimization process for during the minimization process for instance if we want to optimize instance if we want to optimize using the layer wise approach so this is using the layer wise approach so this is very easily implemented in tequila very easily implemented in tequila and then we have different ways to and then we have different ways to define a hamiltonian so in general the define a hamiltonian so in general the hamiltonian will be defined with poly hamiltonian will be defined with poly strings strings so paulie strings are not the same class so paulie strings are not the same class as paulie mattresses as paulie mattresses but the idea is is very similar so we but the idea is is very similar so we just called paulie x just called paulie x in this case or probably y etc we can in this case or probably y etc we can use a list use a list to implement this poly x gate on to implement this poly x gate on different qubits at the same time different qubits at the same time and we can also check if the hamiltonian and we can also check if the hamiltonian is our median if it's not is our median if it's not we can extract the medium part on the we can extract the medium part on the anterior median part anterior median part and as i will show you later we can also and as i will show you later we can also obtain the hamiltonian for a molecule obtain the hamiltonian for a molecule directly directly and of course printing that hamiltonian and of course printing that hamiltonian if necessary if necessary and then how can we create an objective and then how can we create an objective as i said the objective is as i said the objective is is the expectation value or a function is the expectation value or a function of the expectation values of the expectation values so in this case the objective is just so in this case the objective is just expectation value of the hamiltonian of expectation value of the hamiltonian of publix gate publix gate so when creating this expectation value so when creating this expectation value tequila will ask for the hamiltonian and tequila will ask for the hamiltonian and the the and the silkwood that generates the wave and the silkwood that generates the wave function function and we can compile this expectation and we can compile this expectation value and an value and an obtainer and and and once it's uh it's obtainer and and and once it's uh it's uh it's compiler we can print it and we uh it's compiler we can print it and we can can evaluate it more easily by just you know evaluate it more easily by just you know specifying the value of the specifying the value of the of the in this case of the of the of the in this case of the of the variable variable a and something very important with a and something very important with objective is objective is they can be differentiated so if your they can be differentiated so if your objective at the end you objective at the end you need to compute something that includes need to compute something that includes some some differential parts of the expectation differential parts of the expectation value you can do that value you can do that using the the queue grad and this is using the the queue grad and this is also very important sometimes or for also very important sometimes or for some of the objectives some of the objectives so let me show you an example of only so let me show you an example of only all together all together and these are very complicated and these are very complicated objectives and hamiltonian and objectives and hamiltonian and and and a unitary circuit so just to and and a unitary circuit so just to show you how can we show you how can we wrote everything together so in this wrote everything together so in this case we define the variable a case we define the variable a of our unitary operation when then we of our unitary operation when then we construct the construct the the unitary gate by using this tequila the unitary gate by using this tequila variable and variable and in this case we apply the index uh the in this case we apply the index uh the exponential with exponential with uh with this variable and then we uh with this variable and then we generate the the cubic hamiltonian in generate the the cubic hamiltonian in this case we can generate it from a this case we can generate it from a string so it's not necessary to specify string so it's not necessary to specify dq pauli x dq poly y etc we can just dq pauli x dq poly y etc we can just wrote the wrote the the string and and tequila will the string and and tequila will translate that into poly matrices translate that into poly matrices and then we compute the expected value and then we compute the expected value and as i said it can be differentiated and as i said it can be differentiated so we can compute the so we can compute the the derivative of this expectation value the derivative of this expectation value and construct an objective that is and construct an objective that is much more sophisticated than just the much more sophisticated than just the expectation value of the hamiltonian and then in this case we use phoenix so and then in this case we use phoenix so we can we can minimize by specifying the method for minimize by specifying the method for enex and the objectives enex and the objectives and all the phonics configuration that and all the phonics configuration that we have decided we have decided and the number of maximal iterations etc and the number of maximal iterations etc and more things that we can add and more things that we can add and we can also plot the energies and and we can also plot the energies and angles of angles of of this optimization by just spelling of this optimization by just spelling energies and energies and angles and this is the result with the angles and this is the result with the points that points that phoenix visited during the optimization so let me move and show you a particular so let me move and show you a particular chemistry example chemistry example and first of all we will define the and first of all we will define the molecule and molecule and this molecule in this case is a hydrogen this molecule in this case is a hydrogen and and lithium sorry i don't remember how to lithium sorry i don't remember how to pronounce that pronounce that hli and the basis set is uh hli and the basis set is uh sto3g so first of all we can just print sto3g so first of all we can just print the molecule all this information will the molecule all this information will come directly from your chemistry come directly from your chemistry back-end so you need to install back-end so you need to install first either open fermion of psi4 or any first either open fermion of psi4 or any other other possible back-end rated with chemistry possible back-end rated with chemistry that we have installed and implemented that we have installed and implemented in tequila in tequila so all of this will all this information so all of this will all this information come from come from part of this information will come from part of this information will come from this vacant in particular this example this vacant in particular this example use the ci4 backend use the ci4 backend and we can print the monica molecular and we can print the monica molecular orbitals and extract all the information orbitals and extract all the information that this backing can provide that this backing can provide and then we will want to obtain the and then we will want to obtain the hamiltonian because we want to run a hamiltonian because we want to run a bigquery algorithm for instance bigquery algorithm for instance so the first if we don't specify the so the first if we don't specify the method tequila will use the jordan method tequila will use the jordan vignette transformation vignette transformation by just a spelling molecule make by just a spelling molecule make hamiltonian hamiltonian but we can also specify other kinds of but we can also specify other kinds of transformation transformation for instance uh the bravic dive one so for instance uh the bravic dive one so just just by saying transformation bravicate f and by saying transformation bravicate f and then then making the hamiltonian and then we can making the hamiltonian and then we can also select the active spaces to also select the active spaces to reduce the number of terms of the reduce the number of terms of the hamiltonians so if i'm not mistaken the hamiltonians so if i'm not mistaken the previous this hamiltonian has like 670 previous this hamiltonian has like 670 or so number of terms or so number of terms if we restrict the active orbitals we if we restrict the active orbitals we will decrease that number will decrease that number and that could be more useful for us if and that could be more useful for us if we want to run a simulation with we want to run a simulation with a few cubits and not many of them or if a few cubits and not many of them or if we want to we want to reduce our quantum circuit as much as reduce our quantum circuit as much as possible if we use the unitary couple possible if we use the unitary couple cluster cluster assets after that so then the this the example with the so then the this the example with the classical methods classical methods in general we would like to compare the in general we would like to compare the result of our big we for instance with result of our big we for instance with uh uh with the exact result or with the with the exact result or with the classical method classical method so we we can just compute the energy so we we can just compute the energy using mp2 or using fci using mp2 or using fci and in the end and print everything that and in the end and print everything that from from from these classical methods and also from these classical methods and also extract the extract the the variables of these and the the variables of these and the amplitudes of of amplitudes of of obtained from these molecules obtained from these molecules and then this is the example of bicui and then this is the example of bicui from from lithium iodide now i remember lithium lithium iodide now i remember lithium hydride and hydride and first as i said we define the molecule first as i said we define the molecule we we we we constrain the number of orbitals the constrain the number of orbitals the active orbitals to this one active orbitals to this one these two and we generate the molecule these two and we generate the molecule and using this particular basis set and and using this particular basis set and the transformation bravicated and then the transformation bravicated and then we made the equivalent hamiltonian which we made the equivalent hamiltonian which will be the one that we will optimize in will be the one that we will optimize in our objective our objective and then in this case we can just decide and then in this case we can just decide to create a to create a totally general bigquery answers or in totally general bigquery answers or in this case use the unitary couple cluster this case use the unitary couple cluster assets which is in general used in assets which is in general used in in quantum chemistry so by just calling in quantum chemistry so by just calling the function make ucc the function make ucc answers and then we compute the expected answers and then we compute the expected value as i said you before we can value as i said you before we can define the expectation value and the define the expectation value and the objective in a more complicated way in objective in a more complicated way in this example this example it will be just the the ground state of it will be just the the ground state of of this of this of this hamiltonian and then to compute of this hamiltonian and then to compute the reference energies we can call the the reference energies we can call the classical methods of classical methods of fci for instance and print the results fci for instance and print the results similarly to as i show you before similarly to as i show you before so let me move to the last example which so let me move to the last example which is a quantum machine learning example is a quantum machine learning example which is based on which is based on on the data reblogging for universal on the data reblogging for universal quantum classifier work quantum classifier work this all these examples are in the this all these examples are in the tutorial sections and there are many tutorial sections and there are many more so please more so please go ahead and check other ones and go ahead and check other ones and this exam i use this example because to this exam i use this example because to me it would be very easy to me it would be very easy to show how can we compute fidelity which show how can we compute fidelity which is something that is something that in general is it's used in in general is it's used in in in some quantum machine learning in in some quantum machine learning algorithms algorithms so first of all i didn't set that but we so first of all i didn't set that but we can can cons define the wave function not only cons define the wave function not only from the quantum circuit from the quantum circuit but also from a string of from an array but also from a string of from an array so we can so we can specify in the string for instance in specify in the string for instance in this case the zero zero plus one one this case the zero zero plus one one state state or we can just specify the array of of or we can just specify the array of of this this of this quantum state and of course when of this quantum state and of course when we we define these wave functions manually define these wave functions manually remember to normalize it just in case remember to normalize it just in case because in general will not be because in general will not be normalized uh that doesn't happen if you normalized uh that doesn't happen if you if you define the if you obtain the y if you define the if you obtain the y function for the circuit obviously function for the circuit obviously because you have applied because you have applied unitary gates so these are the three unitary gates so these are the three ways that you can use to define your ways that you can use to define your your wave function in tequila your wave function in tequila and then we have two methods to compute and then we have two methods to compute the fidelity the the first one is the the fidelity the the first one is the most intuitive one most intuitive one which is just compute the the overlap which is just compute the the overlap between your target state and the wave between your target state and the wave function of your circuit for instance function of your circuit for instance and the second one is the one that uses and the second one is the one that uses objectives which is in the end the chord objectives which is in the end the chord of tequila as i said of tequila as i said so we construct the the we compute the so we construct the the we compute the the density matrix of of our target the density matrix of of our target state state and we decompose that into poly and we decompose that into poly projectors and projectors and use these poly projectors as objective use these poly projectors as objective by just computing the expected value of by just computing the expected value of the the quantum circuit which will generate the quantum circuit which will generate the the way one of the wave functions the way one of the wave functions uh with respect to the to the uh with respect to the to the to the density matrix of of the of the to the density matrix of of the of the target state target state and in the end is exactly the same but and in the end is exactly the same but the second option will be more suitable the second option will be more suitable for the kill especially if we want to for the kill especially if we want to optimize the fidelity for as a function optimize the fidelity for as a function of some of some variables this is the model of the variables this is the model of the example so we have a model which example so we have a model which which is divided into layers and its which is divided into layers and its layer it's uh layer it's uh it's uh some single qubit gates in it's uh some single qubit gates in particular the general unitary gate but particular the general unitary gate but uh it's just like a composition of uh it's just like a composition of rotational z rotational y rotational z rotational y and rotational z gates and our the idea and rotational z gates and our the idea of the this classifier is that of the this classifier is that we have two classes in this case and we we have two classes in this case and we want to train our classifier to achieve want to train our classifier to achieve one state or another and this is a very one state or another and this is a very general uh way of thinking about quantum general uh way of thinking about quantum classification and there are many other classification and there are many other words that do the same words that do the same and the difference with others is that and the difference with others is that we introduce the data we introduce the data or in this case the the value of or in this case the the value of that we want to classify into the that we want to classify into the quantum circuit manually quantum circuit manually and we apply that in all layers so in and we apply that in all layers so in the end the cost function as in any the end the cost function as in any other words is other words is the the overlap between your target the the overlap between your target state and the stick with the statewide state and the stick with the statewide function function which depends on the additional which depends on the additional parameters that we have to optimize parameters that we have to optimize so first of all we need to define the so first of all we need to define the target the same way function target the same way function and in this case uh i decided to and in this case uh i decided to to define it from an array so we have to define it from an array so we have the the state 0 when the class is 0 and the state 0 when the class is 0 and the state 1 when the class is one state 1 when the class is one and of course we can generate any other and of course we can generate any other possible wave functions so possible wave functions so this is just an example and then we this is just an example and then we construct the quantum classifier and in construct the quantum classifier and in this case this case since it's organized by layers we it since it's organized by layers we it will be useful to will be useful to just construct a for loop that that adds just construct a for loop that that adds as many layers as as we require in this as many layers as as we require in this case as i said case as i said this model this answer contains this model this answer contains rotational white gates rotational white gates that includes the the value of the point that includes the the value of the point to be classified plus to be classified plus a parameter that will be optimized later a parameter that will be optimized later in the minimization part of it in the minimization part of it and then we have the loss function first and then we have the loss function first of all using the fidelity function that of all using the fidelity function that i just described before i just described before so it will take as an input the wave so it will take as an input the wave function the target wave function function the target wave function and it will compute the and the compose and it will compute the and the compose into projector the into projector the the density matrix and then we have the the density matrix and then we have the objective objective expectation value with the quantum expectation value with the quantum circuit that will be our quantum circuit that will be our quantum classifier classifier and the target and the target density and the target and the target density matrix matrix and with that we can construct any and with that we can construct any possible cost function in this case it's possible cost function in this case it's a very simple one because it's just the a very simple one because it's just the sum of all infidelities sum of all infidelities but we can construct a more but we can construct a more sophisticated one and as i said we can sophisticated one and as i said we can differ even differentiate this loss differ even differentiate this loss function if necessary so function if necessary so we can do whatever we prefer with these we can do whatever we prefer with these objectives and this is the result this is the code and this is the result this is the code for the training part for the training part so here we have this we will use the so here we have this we will use the points inside and outside of a circle points inside and outside of a circle for classification for classification and we generate the training set using a and we generate the training set using a function that function that i didn't wrote here but just delivers i didn't wrote here but just delivers the points the points of inside and outside of a circle and of inside and outside of a circle and levels them levels them and then we generate the variables in and then we generate the variables in this the kilo variables in this case i this the kilo variables in this case i wanted to call them wanted to call them theta th we can decide any other name theta th we can decide any other name and and then we initialize these and and then we initialize these variables at random in this case this is variables at random in this case this is not very useful in general for not very useful in general for variational quantum algorithms as variational quantum algorithms as many of you may know because there many of you may know because there aren't plato problem but we can just aren't plato problem but we can just decide if we decide if we can start at random of a particular can start at random of a particular point point and then we we decide the optimization and then we we decide the optimization parameters in this case that would use a parameters in this case that would use a numerical gradient and i would use numerical gradient and i would use rms preparation method and rms preparation method and and as i said the cost function is the and as i said the cost function is the objective and is the function that they objective and is the function that they just presented before just presented before that uses the data the labels and the that uses the data the labels and the parameters parameters and then we can just test but we can and then we can just test but we can generate the test by minimizing that generate the test by minimizing that objective objective and this is the result so we can bring and this is the result so we can bring the the loss which is the the the loss which is the there is the energy of this minimization there is the energy of this minimization and we can plot the energies and angles and we can plot the energies and angles and this is the result and this is the result so it's very easy to just plot it uh so it's very easy to just plot it uh with tequila because you you have a with tequila because you you have a first idea of what's going on in your first idea of what's going on in your minimization minimization of course after that you can just use of course after that you can just use your data for generating other kinds of your data for generating other kinds of plots plots but this is more or less the result that but this is more or less the result that we will obtain with we will obtain with with tequila and then we run the test with tequila and then we run the test in this case this uh this um not so in this case this uh this um not so related with the killer so in this case related with the killer so in this case the the test is 1000 points and the the test is 1000 points and we generate the quantum circuit uh with we generate the quantum circuit uh with the parameters of the parameters of random parameters to generated for the random parameters to generated for the test we compute the wave function test we compute the wave function and in this case this is the important and in this case this is the important line here the wave function qc equal to line here the wave function qc equal to q simulate q simulate uh here we specify that the variables of uh here we specify that the variables of the quantum circuit of the quantum the quantum circuit of the quantum classifier are the results of the test classifier are the results of the test so by just spelling test.angles we so by just spelling test.angles we automatically substitute all the values automatically substitute all the values of of our quantum circuit bar by the value and our quantum circuit bar by the value and the value and the values of the the value and the values of the variables variables and with that we generate all all all and with that we generate all all all the the test points and we check if they are test points and we check if they are correct or not and this is the result correct or not and this is the result so this is just an example of a very so this is just an example of a very simple algorithm simple algorithm that can be used for quantum that can be used for quantum classification of course there are many classification of course there are many more more and we are working in new tutorials that and we are working in new tutorials that implement other kinds of algorithms implement other kinds of algorithms precisely precisely because we believe that is the way to because we believe that is the way to understand how not only how tequila understand how not only how tequila works but also to prove works but also to prove and the plasticity of tequila how can it and the plasticity of tequila how can it will will and the versatility of it and how can it and the versatility of it and how can it be be applied to anybody or to any too many applied to anybody or to any too many different um problems different um problems and these are the pro and the current and these are the pro and the current projects that use tequila these are more projects that use tequila these are more sophisticated sophisticated ones so the basis set free approach for ones so the basis set free approach for bigquery employing per natural orbitals bigquery employing per natural orbitals all these all these all these words have uh an example all these words have uh an example called at least the metabolization called at least the metabolization quantum mechan solver and also the quantum mechan solver and also the computerized design of quantum optical computerized design of quantum optical hardware hardware so these are more sophisticated codes so these are more sophisticated codes that use tequila and that use tequila and it could be used as a as a proof as i it could be used as a as a proof as i said that the versatility of this said that the versatility of this language language that can be used for state-of-the-art that can be used for state-of-the-art and to developing new algorithms and to developing new algorithms and we are currently working in in in and we are currently working in in in adding more and more adding more and more back-ends in particular orchestra kibo back-ends in particular orchestra kibo by quest and by quest and intel qs i believe kivo is if it's not intel qs i believe kivo is if it's not ready it's almost ready as well as ready it's almost ready as well as orchestra but jacob can orchestra but jacob can can say more about that but i believe can say more about that but i believe they are almost ready they are almost ready and then we will also like to work with and then we will also like to work with new libraries to implement new libraries to implement mythic and tensorflow in particular but mythic and tensorflow in particular but we are open to any suggestions comments we are open to any suggestions comments and recommendations and also if you want and recommendations and also if you want to to be part of tequila and let us know be part of tequila and let us know because we are because we are we want to generate an open quantum an we want to generate an open quantum an open source open source quantum language developed by academia quantum language developed by academia for academia and for academia and and beyond so in the end and the and beyond so in the end and the important thing of this language is that important thing of this language is that everybody participates on them and everybody participates on them and everybody is everybody is willing to add more and more features so willing to add more and more features so it can be it can be really useful and the goal of tequila as really useful and the goal of tequila as i said at the beginning is i said at the beginning is to provide a general framework that can to provide a general framework that can be used to benchmark and to test any be used to benchmark and to test any possible quantum algorithm possible quantum algorithm so you don't have to then rely on so you don't have to then rely on particular backends or that can become particular backends or that can become obsolete or maybe they obsolete or maybe they or maybe they are not useful for you so or maybe they are not useful for you so for instance i for instance i some years ago i did a a simulation and some years ago i did a a simulation and i needed to i needed to to compare the the results in the ibm to compare the the results in the ibm computer and the regretted computer and computer and the regretted computer and i have to wrote i have to wrote two different programs to exactly wrote two different programs to exactly wrote to exactly run exactly the same to exactly run exactly the same algorithm so if i had the kill at that algorithm so if i had the kill at that time it was time it was it had been it will be much easier in it had been it will be much easier in the sense that i just the sense that i just wrote one algorithm and then i called wrote one algorithm and then i called back and equal kiskit or back and equal kiskit or and i will obtain the corresponding uh and i will obtain the corresponding uh simulation of this particular vacance simulation of this particular vacance so this is the idea of in behind tequila so this is the idea of in behind tequila so we want just to so we want just to simplify the efforts for theoreticians simplify the efforts for theoreticians as us as us and and try to take into account as many and and try to take into account as many possible possible new libraries and and codes and new libraries and and codes and packages that helps in quantum packages that helps in quantum simulation so for instance simulation so for instance uh i don't remember who you said that uh i don't remember who you said that but that has been working in tensor but that has been working in tensor networks uh simulations networks uh simulations that would be very useful too to mix that would be very useful too to mix that with tequila at some point so you that with tequila at some point so you can use tequila also can use tequila also to uh perform tensor network simulations to uh perform tensor network simulations or to simulate your circuit with tensor or to simulate your circuit with tensor networks so you can networks so you can compute some some values interesting compute some some values interesting values here so please let us know any values here so please let us know any suggestion suggestion and thank you for your attention and for and thank you for your attention and for all the developers of tequila so in the all the developers of tequila so in the end end the very big two developers were jacob the very big two developers were jacob and sumner and sumner and the rest of us we contribute in some and the rest of us we contribute in some of the of the of the features of tequila and i hope of the features of tequila and i hope that this list just that this list just grows in the future so tequila 2.0 grows in the future so tequila 2.0 can include more and more people all the can include more and more people all the community if possible community if possible so thank you and please let me know any so thank you and please let me know any questions and i will try to answer them thank you very much alba we have uh time thank you very much alba we have uh time for questions i also have seen for questions i also have seen that uh jacob answered the symbolic that uh jacob answered the symbolic back-end back-end question on chat so question on chat so now we have open discussion i have a question alba um i have a question alba um so uh you you showed um when you were so uh you you showed um when you were discussing the discussing the or showing the api that they're or showing the api that they're classical optimizers classical optimizers and you kind of alluded to quantum and you kind of alluded to quantum circuit optimization at one point circuit optimization at one point um but i'm curious whether the api um but i'm curious whether the api like might support additional like an additional stage for quantum circuit optimization either quantum circuit optimization either through i don't know if through i don't know if like you know for instance kiskit has like you know for instance kiskit has their optimization levels i don't know their optimization levels i don't know if it's if it's if it's possible to pass those through if it's possible to pass those through when you're compiling the quantum when you're compiling the quantum circuit but circuit but there's also you know like projects like there's also you know like projects like pi pi zx pi pi zx which will you know kind of work outside which will you know kind of work outside of the of the embedded compilation loop of the embedded compilation loop of the packages themselves so packages themselves so i'm curious like i'm a big proponent of i'm curious like i'm a big proponent of pi zx i think like it's a pi zx i think like it's a you know it works quite well and it you know it works quite well and it would be neat you know especially for would be neat you know especially for looking to run on looking to run on different harder back back ends so i'm different harder back back ends so i'm just wondering where that fits in to just wondering where that fits in to maybe tequila 2.0 maybe tequila 2.0 thanks for your question and i'm really thanks for your question and i'm really excited of of excited of of exit calculus to be honest so we are exit calculus to be honest so we are very interested on that very interested on that and currently as far as i know i at and currently as far as i know i at least in the open version least in the open version and they are not supported so you are and they are not supported so you are talking about compilation right so how talking about compilation right so how to simplify the circuit first to simplify the circuit first before the the the real simulation or before the the the real simulation or or running the the algorithm or to map or running the the algorithm or to map your circuit into a particular topology your circuit into a particular topology uh we don't have that yet implemented uh we don't have that yet implemented but it's part of the list of things that but it's part of the list of things that we want to include we want to include definitely so yeah yeah that will be definitely so yeah yeah that will be very useful very useful and that's one of the big points in my and that's one of the big points in my opinion that that tequila has to opinion that that tequila has to implement at some point because implement at some point because that's in the end the a very very that's in the end the a very very practical thing that we will need so how practical thing that we will need so how to map our circuit in a particular to map our circuit in a particular topology topology and also if we can simplify that circuit and also if we can simplify that circuit especially for nisk simulation so we especially for nisk simulation so we want want as as lower than gate depth as possible as as lower than gate depth as possible so yeah there are many techniques that so yeah there are many techniques that can be very useful and can be very useful and yeah we want to do that so it's part of yeah we want to do that so it's part of the list of things but it's not the list of things but it's not supported yet supported yet great thank you hey alba yeah thank you for the great hey alba yeah thank you for the great talk talk could you elaborate on how we could could you elaborate on how we could incorporate tensor networks with tequila incorporate tensor networks with tequila well that's that's the thing so we would well that's that's the thing so we would like to but like to but we will need to discuss that with we will need to discuss that with someone that works with those libraries someone that works with those libraries first first so i can think about for instance uh i so i can think about for instance uh i believe that it could be a model after believe that it could be a model after you simulate your circuit you just map you simulate your circuit you just map your so sorry you just call this um your so sorry you just call this um tensor network method for instance to tensor network method for instance to simulate that circuit with tensor simulate that circuit with tensor networks networks specifying i don't know bond dimension specifying i don't know bond dimension for instance for instance and i can think about that because i in and i can think about that because i in the end tequila is organized in a way the end tequila is organized in a way that you can just add more modules and that you can just add more modules and you don't touch the main code in the end you don't touch the main code in the end the core of tequila is the objective the core of tequila is the objective so you can add whatever you want around so you can add whatever you want around it it and just call it at any time so for and just call it at any time so for instance i can just instance i can just create the circuit the quantum circuit i create the circuit the quantum circuit i can call the wavefunction module to can call the wavefunction module to obtain the mod in the obtain the mod in the the way function or i can just call um the way function or i can just call um some tensor network module that will some tensor network module that will deliver the approximate the deliver the approximate the the matrix product states for instance the matrix product states for instance of that of that wave function of that of that wave function so that could be another way to think so that could be another way to think about it but i believe that it will be about it but i believe that it will be better to discuss that with better to discuss that with someone that is familiar with uh with someone that is familiar with uh with these libraries of tensor networks these libraries of tensor networks but yeah i believe that it will not be but yeah i believe that it will not be so difficult because in the end so difficult because in the end you can just call you know this extra you can just call you know this extra library library and and that's it and you just have to and and that's it and you just have to to introduce that in the to introduce that in the in the source code of tequila and yeah in the source code of tequila and yeah in the end yeah i don't think it would in the end yeah i don't think it would be very difficult and maybe be very difficult and maybe we can think about more sophisticated we can think about more sophisticated things i things i i you know like a full different module i you know like a full different module with with tensor network simulation maybe well it tensor network simulation maybe well it depends on depends on which applications are you thinking which applications are you thinking about but yeah about but yeah i think that we can try many things oh i think that we can try many things oh yeah that's that's really interesting yeah that's that's really interesting because because for my master thesis i'm simulating for my master thesis i'm simulating quantum circuits quantum circuits using tensor networks and matrix product using tensor networks and matrix product states so maybe i'll states so maybe i'll write to you later and then we can talk write to you later and then we can talk about this about this indeed yeah let's stay in thoughts and indeed yeah let's stay in thoughts and yeah we can try to figure out how to do yeah we can try to figure out how to do that that but yeah i believe that in the end the but yeah i believe that in the end the tequila chord tequila chord will not be affected by anything that we will not be affected by anything that we add on top of that add on top of that so that's the good thing of it so the so that's the good thing of it so the skeleton is the same the only thing that skeleton is the same the only thing that in this in this option part you may also add option part you may also add mbs simulation of your circuit right mbs simulation of your circuit right thank you and maybe you can even use the thank you and maybe you can even use the optimization methods the classical ones optimization methods the classical ones to i don't know to find to i don't know to find uh some of these uh some of the uh some of these uh some of the coefficients of the coefficients of the your matrix pro the state or optimize on your matrix pro the state or optimize on top of that or i don't know top of that or i don't know so in the end you have all the so in the end you have all the optimization methods here so you can optimization methods here so you can also take advantage of them that they also take advantage of them that they are around you know they are already are around you know they are already programmed there you can call them programmed there you can call them etc okay thank you so much maybe a comment on that like um if maybe a comment on that like um if you're for example developing like you're for example developing like tensor network codes um tensor network codes um what's like as soon as like your back what's like as soon as like your back end which you are developing which uses end which you are developing which uses tensor networks if this tensor networks if this understands like quantum circuits and it understands like quantum circuits and it sounded a little bit to me sounded a little bit to me like it does if you say you are like it does if you say you are simulating quantum circuits simulating quantum circuits then this can be more or less plugged then this can be more or less plugged into tequila the same way as we plug in into tequila the same way as we plug in for example the culex simulator for example the culex simulator and this means then as soon as you like and this means then as soon as you like made this connection made this connection you can use all the algorithms that have you can use all the algorithms that have been developed been developed using tequila before it's like the whole using tequila before it's like the whole deal of like if you have multiple deal of like if you have multiple expectation values and you make an expectation values and you make an objective function out of them and then objective function out of them and then you sample them and then you need you sample them and then you need gradients from them gradients from them this is all independent of the actual this is all independent of the actual tensor network simulation of the tensor network simulation of the individual circuits individual circuits and as soon as you like connected it you and as soon as you like connected it you have like access to all those algorithms have like access to all those algorithms to to do like benchmarks or like tests like do like benchmarks or like tests like how far can your backend go how far can your backend go right um yeah yeah i agree thank you thank you jacob uh and jacob also thank you jacob uh and jacob also answered some questions in the chat so answered some questions in the chat so thanks for that as well he's the master thanks for that as well he's the master of tequila basically so sorry any other questions for alba or jacob i guess i have one small question about i guess i have one small question about the user interface um the user interface um is it uh is it pretty straightforward to is it uh is it pretty straightforward to for the user to for the user to to easily define their own onslaughts to easily define their own onslaughts including in onsets where you have i including in onsets where you have i mean it seems like most um mean it seems like most um packages that i've seen and i haven't packages that i've seen and i haven't looked that much but um looked that much but um you know it kind of assumes that all you know it kind of assumes that all rotation gates are supposed to be rotation gates are supposed to be parameterized by the optimizer parameterized by the optimizer but there's cases where you know i only but there's cases where you know i only want some want some some percentage of the rotations to be some percentage of the rotations to be parameterized is that easy to specify parameterized is that easy to specify or is there okay yeah let me show you a or is there okay yeah let me show you a particular example particular example for instance for instance [Music] [Music] for chemistry we have already for chemistry we have already implemented the unitary couple clusters implemented the unitary couple clusters so you don't have to care about that so so you don't have to care about that so just call just call ucc and that's it and the answers is ucc and that's it and the answers is automatically uh automatically uh generated and then for you know more generated and then for you know more manual answers manual answers uh this is the example of this example uh this is the example of this example for instance so here for instance so here i decided to uh to construct rotational i decided to uh to construct rotational y y with the whatever values in this case with the whatever values in this case one parameterized value one parameterized value but of course this x val is something but of course this x val is something fixed fixed that it comes in my definition of the that it comes in my definition of the quantum circuit quantum circuit i can just erase this parameter here and i can just erase this parameter here and just fix x bar parameter just fix x bar parameter and not only that but tequila also and not only that but tequila also allows and this is one of the allows and this is one of the one of the features that includes the one of the features that includes the minimization minimization part to optimize only a few variables part to optimize only a few variables which means that i fix some of them which means that i fix some of them and then i run my optimization as a and then i run my optimization as a function of function of some others this is for instance can be some others this is for instance can be used for the layer-wise uh used for the layer-wise uh minimization that is using some minimization that is using some variation or quantum algorithms that you variation or quantum algorithms that you fix the parameters of one of all the fix the parameters of one of all the layers except one layers except one and you optimize on top of that and then and you optimize on top of that and then you move to the next layer and you you move to the next layer and you keep the the result of the previous one keep the the result of the previous one and optimize the next one and so on and and optimize the next one and so on and so forth so forth you can do that with tequila easily you you can do that with tequila easily you just have to specify which variables just have to specify which variables would you like to optimize would you like to optimize and the others if you prefer to fix a and the others if you prefer to fix a value of them value of them or if you don't fix any value they will or if you don't fix any value they will be initialized at random be initialized at random by default so this is also one feature by default so this is also one feature in in i believe it's in here sorry too many slides sorry too many slides here in variables in additional keywords here in variables in additional keywords variables the list of variables that you variables the list of variables that you want to optimize want to optimize by default will be all of them and then by default will be all of them and then initial values you have to specify initial values you have to specify which values in general you can use which values in general you can use extract variables and extract variables and and that's it and you will have all of and that's it and you will have all of them and then initialize at random or a them and then initialize at random or a particular value particular value or you can just decide initial values or you can just decide initial values and the values that you want to optimize and the values that you want to optimize if you have a specified before the if you have a specified before the variables but then again variables but then again then in the in the quantum gates then in the in the quantum gates definition definition which i believe is here yeah these all which i believe is here yeah these all the quantum gates that you have some of the quantum gates that you have some of them are parametrized other are not for them are parametrized other are not for instance the x y and z are not instance the x y and z are not parametrized parametrized they can be parametrized if you compute they can be parametrized if you compute the power of them the power of them so the power could be a parameters if so the power could be a parameters if you wish you wish but it's not necessarily so you can just but it's not necessarily so you can just mix whatever you prefer mix whatever you prefer there are some examples in the tutorials there are some examples in the tutorials i believe so you can check that there i believe so you can check that there perfect thank you [Music] any other questions uh if not let's thank god again for the uh if not let's thank god again for the excellent talk thank you very much excellent talk thank you very much thank you alexei and thank you everyone thank you alexei and thank you everyone for your attention for your attention uh so uh i just wanted to also um uh so uh i just wanted to also um introduce introduce um mercury who i see joined us he's also um mercury who i see joined us he's also from stanford from stanford quantum he's a colleague of jeremiah so quantum he's a colleague of jeremiah so welcome mert welcome mert uh and we have some type of general uh and we have some type of general discussion um and uh i've seen discussion um and uh i've seen in the recipes uh we ask folks to in the recipes uh we ask folks to um either propose lightning talks um either propose lightning talks or ask for lighting talks and uh i think or ask for lighting talks and uh i think folks who propose some of the lightning folks who propose some of the lightning talks uh talks uh actually are not here but i think rishi actually are not here but i think rishi asked uh if somebody can asked uh if somebody can teach us um zx calculus teach us um zx calculus if i'm correct and uh i guess we need to if i'm correct and uh i guess we need to bring alex uh back from bring alex uh back from oxford with 101 right i think this was oxford with 101 right i think this was the the topic we uh discussed right but we need topic we uh discussed right but we need to have some to have some um 101 level tutorials um 101 level tutorials so if you guys are interested i think so if you guys are interested i think it's uh it's probably a good idea to it's uh it's probably a good idea to kind of kind of invite invite invite invite some of the speakers back and teach us some of the speakers back and teach us the basics which brings me to another the basics which brings me to another question i think there are several folks question i think there are several folks here who here who are looking to enter the field and i are looking to enter the field and i received received requests from some of the pretty senior requests from some of the pretty senior software engineers in the bay area whom software engineers in the bay area whom i knew i knew as colleagues in various startups and as colleagues in various startups and some you know folks kind of really some you know folks kind of really uh advanced for instance databases and uh advanced for instance databases and they actually they actually educate themselves in quantum computing educate themselves in quantum computing and so i just wanted to ask amir maybe and so i just wanted to ask amir maybe uh uh sasha uh so you guys are looking to move sasha uh so you guys are looking to move into this field into this field what are the recommendations what are what are the recommendations what are some starting points do you have some starting points do you have some uh ideas uh how some uh ideas uh how can somebody you know with general can somebody you know with general science background maybe working in um science background maybe working in um software engineering right now what's software engineering right now what's what are the good ways to what are the good ways to kind of familiarize themselves with this kind of familiarize themselves with this field field uh and maybe kind of like eventually uh and maybe kind of like eventually i think folks really want to get a job i think folks really want to get a job in this field um in this field um if anybody has an idea because you know if anybody has an idea because you know i i receive actually specific requests i i receive actually specific requests for advice what would you advise yeah i for advice what would you advise yeah i have a quick have a quick addition to that uh because i prepared addition to that uh because i prepared some questions for the discussion and uh some questions for the discussion and uh also can you focus on like everyone here also can you focus on like everyone here with like phds with like phds and in the field and also people who and in the field and also people who don't have phds in the field focus on don't have phds in the field focus on the benefits the benefits of and the necessity of a phd well i mean in the in this i mean phd well i mean in the in this i mean phd versus not phd versus not phd and i believe that things and i believe that things has changed a lot in the last years in a has changed a lot in the last years in a sense that when sense that when when i started my phd for instance um when i started my phd for instance um who was working in quantum computing i who was working in quantum computing i mean were mean were people that were working in quantum people that were working in quantum information for the last years basically information for the last years basically it was quite a small subfield in physics it was quite a small subfield in physics and quantum computing in particular and and quantum computing in particular and but with all this creation of these but with all this creation of these companies companies and and the realization that yeah and and the realization that yeah quantum computer may quantum computer may exist and may be useful and many many exist and may be useful and many many jobs have appeared jobs have appeared and now we need not only theoreticians and now we need not only theoreticians that you know that that you know that think about the complexity of the think about the complexity of the algorithms or algorithms or or designing the the new ones etc but we or designing the the new ones etc but we need many engineers need many engineers many to construct the device because if many to construct the device because if not it not it doesn't make sense to everything about doesn't make sense to everything about the algorithms the algorithms but also many software engineers so and but also many software engineers so and tequila is one of the tequila is one of the of the examples and many other languages of the examples and many other languages in the end so in the end so there is a lot of jobs that doesn't there is a lot of jobs that doesn't require a phd require a phd but rather a huge experience with python but rather a huge experience with python for instance or with other tools for instance or with other tools to really implement these quantum to really implement these quantum languages because that would be the only languages because that would be the only way that it would be useful for us way that it would be useful for us uh especially in this niskera because we uh especially in this niskera because we have the have the one so experimentally we have a quantum one so experimentally we have a quantum computer which is computer which is you know a device an experimental device you know a device an experimental device we want to control that we want to control that and we need a good interface between our and we need a good interface between our computer and our computer and our hardware so we need to engineer that and hardware so we need to engineer that and especially if the coherence times in the especially if the coherence times in the quantum computers are so quantum computers are so short and we need to you know perform short and we need to you know perform all of the operations as fast as all of the operations as fast as possible and for that i believe that possible and for that i believe that there's plenty of jobs and you can check there's plenty of jobs and you can check easily in many of the startups and easily in many of the startups and companies companies that doesn't require a phd but rather that doesn't require a phd but rather maybe experience in quantum computing or maybe experience in quantum computing or not even that not even that like experience in other fields and and like experience in other fields and and then this from one side but also from then this from one side but also from other sides uh other sides uh even if you have a phd or not and we even if you have a phd or not and we only only we also need people from different we also need people from different backgrounds not only physics backgrounds not only physics we need people for chemistry for we need people for chemistry for instance to tell us instance to tell us what are the problems in chemistry what are the problems in chemistry people from finance that tell us what people from finance that tell us what are the problems in finance etc are the problems in finance etc because in the end we are developing a because in the end we are developing a tool that can be used for tool that can be used for solving many problems but we need to solving many problems but we need to know which problems we want to solve know which problems we want to solve so that's why maybe people that doesn't so that's why maybe people that doesn't have a psd is also useful of course have a psd is also useful of course because because if they have experience in i don't know if they have experience in i don't know finance for instance finance for instance they can provide this this information they can provide this this information to us and we can work together to find to us and we can work together to find the proper algorithm for instance the proper algorithm for instance yeah so uh a quick follow-up on that so yeah so uh a quick follow-up on that so we we see that trend in the industry we we see that trend in the industry right that right that in like most emerging technologies uh in like most emerging technologies uh peop peop usually like uh people with phds drive usually like uh people with phds drive the industry until there's like a point the industry until there's like a point where where you need more engineers especially you need more engineers especially software engineers software engineers that don't necessarily do research in that don't necessarily do research in that field or have that like that field or have that like breadth of knowledge but can do like the breadth of knowledge but can do like the hard tasks of like coding that in or hard tasks of like coding that in or like like making the like circuit connections that making the like circuit connections that in in the hardware right in in the hardware right but um the question is here i think like but um the question is here i think like right now right now what i'm trying to ask is do you think what i'm trying to ask is do you think that people can train themselves that people can train themselves and do research in the field like um and do research in the field like um doing like designing actual quantum doing like designing actual quantum algorithms not like the hard hard task algorithms not like the hard hard task jobs but as like as researchers do you jobs but as like as researchers do you think people can think people can join the field as research researchers join the field as research researchers without a phd without a phd yeah and i believe it's possible yeah in yeah and i believe it's possible yeah in general the only problem that i general the only problem that i i can see of course i have a phd so i'm i can see of course i have a phd so i'm biased okay biased okay so uh in the indexes matter but so uh in the indexes matter but the only problem that i see because i the only problem that i see because i also suffer from that also suffer from that is there is a lot of noise in the field is there is a lot of noise in the field and that happens all the time with all and that happens all the time with all fields fields so sometimes it's difficult to tackle so sometimes it's difficult to tackle what are the real problems or if what are the real problems or if what you are doing is actually useful or what you are doing is actually useful or not not and for that it's really useful to talk and for that it's really useful to talk with other people with other people that provides feedback about your idea that provides feedback about your idea and what about what you are doing and what about what you are doing and this is very difficult to do if you and this is very difficult to do if you are you know alone in your home are you know alone in your home and you are not part of an organization and you are not part of an organization or or or a university or a company but to be or a university or a company but to be honest there are many many honest there are many many ways to get in touch with other ways to get in touch with other researchers nowadays researchers nowadays for instance this kind of talks and for instance this kind of talks and events and many others events and many others so i believe that you can really start so i believe that you can really start by your own by your own and then at some point of course you and then at some point of course you will have to make some contact with will have to make some contact with other people but other people but you can you have you have i believe all you can you have you have i believe all the tools to start the tools to start and then of course just contact other and then of course just contact other guys and ask guys and ask questions like yeah i have this idea do questions like yeah i have this idea do you think this has said etc or just you think this has said etc or just discuss that in some panel discussions discuss that in some panel discussions like like this one so it's easy to start by your this one so it's easy to start by your own own and but yet at some point you should and but yet at some point you should take some contact with other people take some contact with other people and other researchers if you want to do and other researchers if you want to do something because it's really difficult something because it's really difficult that you just have that you just have an idea that works because it may be an idea that works because it may be worse in your head but worse in your head but it's or maybe someone has developed that it's or maybe someone has developed that before and that happens before and that happens all the time so especially with this all the time so especially with this quantum information on quantum computing quantum information on quantum computing many people have worked in this field many people have worked in this field for years for years and there are many papers that are and there are many papers that are hidden and you just discover these hidden and you just discover these papers like papers like you have a super clear clever idea and you have a super clear clever idea and then some guy in the 90s just develop then some guy in the 90s just develop exactly the same exactly the same and it's difficult to keep track of that and it's difficult to keep track of that only the all guys in the field know this only the all guys in the field know this kind of paper so that's why it's kind of paper so that's why it's also useful to to make some contacts also useful to to make some contacts okay so from what i understand you're okay so from what i understand you're saying that the the saying that the the the the collaboration uh the the collaboration uh between researchers is necessary it's between researchers is necessary it's not it doesn't matter not it doesn't matter whether you have a piece or not but you whether you have a piece or not but you it's good to be a part of an it's good to be a part of an organization or organization or an academic institution can we do a an academic institution can we do a quick survey here on quick survey here on how many people watching this right now how many people watching this right now are are have phds if you do have one can you put have phds if you do have one can you put a plus one to the chat a plus one to the chat obviously alba has one but um i it would obviously alba has one but um i it would be nice to have like uh be nice to have like uh um see how many people have had phds um see how many people have had phds and watching this interested in quantum and watching this interested in quantum computing um computing um if i could add a few comments um i if i could add a few comments um i totally agree with everything totally agree with everything alba said but um so i'm i have a alba said but um so i'm i have a position at intel position at intel and i should say there are some and i should say there are some companies including intel that still companies including intel that still have a very strong bias towards phds have a very strong bias towards phds you know that's not obviously not you know that's not obviously not universally true i know people at universally true i know people at righetti righetti who don't have phds same with zapata but who don't have phds same with zapata but i don't want you to get the i don't want you to get the the wrong impression that there are some the wrong impression that there are some companies that are a bit slow to adopt companies that are a bit slow to adopt this but i think that should change this but i think that should change pretty soon pretty soon because as alba said we need so many because as alba said we need so many workers um workers um to do all the work yeah i mean as as for my background i'm yeah i mean as as for my background i'm currently working part-time with qceware currently working part-time with qceware and i i am a masters student at stanford and i i am a masters student at stanford right so right so we see we see this trend changing uh but we see we see this trend changing uh but obviously there are like obviously there are like different perks of having a phd and um different perks of having a phd and um i'm not like biased against a phd i i'm not like biased against a phd i would like to get one as well i'm just would like to get one as well i'm just like trying to like trying to um see the understand the field and make um see the understand the field and make other people understand other people understand um another question that i have and this um another question that i have and this is also open to everyone is also open to everyone um is about the barriers to quantum um is about the barriers to quantum simulation so simulation so um obviously tequila is backhand or um obviously tequila is backhand or hardware agnostic it's not just a hardware agnostic it's not just a simulation tool it can run on real simulation tool it can run on real hardware hardware but um what are the but um what are the what are the barriers to quantum what are the barriers to quantum simulation when we're trying to simulation when we're trying to understand real understand real quantum systems and do you think there quantum systems and do you think there will be a point where the will be a point where the the simulations will no longer be the simulations will no longer be relevant we'll reach a point in hardware relevant we'll reach a point in hardware where the hardware will be we won't be able to uh simulate that we won't be able to uh simulate that hardware well that's hardware well that's theoretically that's that's that's theoretically that's that's that's that's possible but that's possible but when do you think that will happen and when do you think that will happen and again this is open to everyone so if again this is open to everyone so if anyone wants to join in well sorry to answer again well sorry to answer again um yeah i mean if you think so um yeah i mean if you think so you mean classical simulation of quantum you mean classical simulation of quantum algorithms yeah i mean algorithms yeah i mean you know it's exponentially hard so at you know it's exponentially hard so at some point it will not be possible at some point it will not be possible at all all although if you have low entanglement although if you have low entanglement you can do efficient simulations with you can do efficient simulations with tensor network techniques for instance tensor network techniques for instance so in the end that's to me one of the so in the end that's to me one of the important points of quantum computing important points of quantum computing any algorithm any application you need any algorithm any application you need to check to check if the amount of entanglement is high or if the amount of entanglement is high or not because if not because if it's not you don't need a quantum it's not you don't need a quantum computer you can do everything computer you can do everything classically so that's on one side uh on classically so that's on one side uh on the other side the other side um we are we don't only have quant um we are we don't only have quant digital quantum computers we cannot we digital quantum computers we cannot we also have quantum simulators like called also have quantum simulators like called atoms etc atoms etc and and you can do practical stuff with and and you can do practical stuff with that the problem with these ones is they that the problem with these ones is they are not are not universal so they are only useful for universal so they are only useful for particular problems or you can also have particular problems or you can also have quantum annealing quantum annealing so there are other techniques besides so there are other techniques besides digital quantum computation digital quantum computation that can be useful for simulation even that can be useful for simulation even simulating many things simulating many things and but yeah in the end um what we do as and but yeah in the end um what we do as a a theoretically let's say when you develop theoretically let's say when you develop an algorithm you just develop a proof of an algorithm you just develop a proof of concept uh concept uh algorithm you run that with 14 cubits or algorithm you run that with 14 cubits or so something that your laptop can so something that your laptop can understand understand and what you should check is and what you should check is theoretically if there is some advantage theoretically if there is some advantage or at least even if the result is or at least even if the result is heuristic as the variational algorithms heuristic as the variational algorithms if the scaling is exponential because if if the scaling is exponential because if it's exponential and your it's exponential and your and your algorithm is working with a few and your algorithm is working with a few qubits of course you will know that it qubits of course you will know that it will continue working with more probably will continue working with more probably but still you can't simulate that but still you can't simulate that anymore and that's one of the quantum anymore and that's one of the quantum super super and the supremacy paper from google was and the supremacy paper from google was precisely that they precisely that they do they rest to simulate the result with do they rest to simulate the result with a classical computer but at some point a classical computer but at some point it was not possible anymore because the it was not possible anymore because the amount of entanglement was amount of entanglement was too high yeah so yeah so if we did put a time frame to that if we did put a time frame to that uh when when do when when does everyone uh when when do when when does everyone think think that simulation we won't be able to that simulation we won't be able to like yes for like low entanglement like yes for like low entanglement systems systems we can efficiently simulate but there we can efficiently simulate but there will be a point where like the will be a point where like the algorithms algorithms that are useful to real world uh that are useful to real world uh will not be we won't be able to simulate will not be we won't be able to simulate them when do you think that's gonna them when do you think that's gonna happen if you happen if you if you were to take a guess if you had if you were to take a guess if you had to like to like say like a month when do you think say like a month when do you think that's gonna happen that's gonna happen i'm not asking about like quantum i'm not asking about like quantum supremacy supremacy in some sense but like just like in some sense but like just like we you simulations won't be like useful we you simulations won't be like useful or relevant anymore or relevant anymore that will have to stick to using that will have to stick to using hardware even if you're not doing useful hardware even if you're not doing useful things things but we will have to stick the hardware but we will have to stick the hardware to the research to the research but that's the definition of quantum but that's the definition of quantum supremacy precisely when you cannot supremacy precisely when you cannot simulate that with a classical computer simulate that with a classical computer even if it's something that is not even if it's something that is not useful at all but useful at all but you know yeah i don't know because we you know yeah i don't know because we are currently are currently in this area because now the chips are in this area because now the chips are 50 cubits or so 50 cubits or so still you need to control these qubits still you need to control these qubits if you have the much the coherence if you have the much the coherence and the entanglement is not high so you and the entanglement is not high so you can again can again simulate that officially classically but simulate that officially classically but i would say that in the following years i would say that in the following years i mean this year i mean this year the next one and so i believe that yeah the next one and so i believe that yeah we will start we will start seeing some results that cannot be seeing some results that cannot be simulated with a classical computer simulated with a classical computer i don't believe that there will be i don't believe that there will be probably not super useful results probably not super useful results because it's still because it's still you know small things and see things you know small things and see things that maybe can be simulated with a that maybe can be simulated with a quantum simulator quantum simulator but we will still i believe that in the but we will still i believe that in the next next two years we will see something in this two years we will see something in this direction direction but still will depend on the on the but still will depend on the on the hardware side hardware side so we can develop many clever algorithms so we can develop many clever algorithms that take advantage that take advantage of noise and everything and solves many of noise and everything and solves many problems but problems but if the device is not well designed if the device is not well designed nothing will work nothing will work so yeah so that's why we need so many so yeah so that's why we need so many people in the end people in the end so yeah so yeah it's an exciting field to be in um also it's an exciting field to be in um also i wanna i wanna talk about like i wanna i wanna talk about like um the landscape in europe for a bit um um the landscape in europe for a bit um so this article just came out uh so this article just came out uh i wanna share it with everyone the link and there are there is a so united and there are there is a so united states states i think started uh they had more i think started uh they had more startups startups at the beginning but now like europe is at the beginning but now like europe is emerging with emerging with more projects and startups and more projects and startups and as a as a so now you're in toronto but as a as a so now you're in toronto but right if i'm not mistaken but you you right if i'm not mistaken but you you are from the are from the um you're from barcelona yeah um you're from barcelona yeah i'm checking this graph and there are i'm checking this graph and there are some so what is this landscape is some so what is this landscape is universities or also companies products universities or also companies products startups so it's at least in a spin it's startups so it's at least in a spin it's not correct because the not correct because the spanish national research council is not spanish national research council is not a startup it's a national institute a startup it's a national institute and this photonic science is the same and this photonic science is the same and barcelona cubic is not even an and barcelona cubic is not even an institute it's just institute it's just it's a twitter and linking account that it's a twitter and linking account that tweets about tweets about news about quantum information in news about quantum information in general general so what i can say is the in europe the so what i can say is the in europe the situation is the following situation is the following we have the quantum flagship which is a we have the quantum flagship which is a lot of money that the european union has lot of money that the european union has put in quantum technologies put in quantum technologies in general and that has several uh in general and that has several uh several parts several parts quantum simulation quantum computation quantum simulation quantum computation quantum comp quantum comp and communication and quantum sensing and communication and quantum sensing and metrology and metrology and also by basic science so we have to and also by basic science so we have to you know split the different parts of you know split the different parts of this money and in quantum computing and this money and in quantum computing and i believe that rishi you were the ones i believe that rishi you were the ones that that is in charmers right uh is in charmers right uh probably you can tell more about that probably you can tell more about that but there is there are different but there is there are different projects in the quantum computing part projects in the quantum computing part one of those is open super queue which i one of those is open super queue which i believe is the one that probably you're believe is the one that probably you're working on working on or i don't know which is the or i don't know which is the they are constructing a quantum they are constructing a quantum superconducting quantum circuit superconducting quantum circuit in charmers but there is a collaboration in charmers but there is a collaboration of many people in europe of many people in europe but this is from the you know from the but this is from the you know from the governmental point of view but there are governmental point of view but there are many new startups there is one many new startups there is one iqm for instance that is in thailand and iqm for instance that is in thailand and munich munich and in barcelona you have kilimanjaro and in barcelona you have kilimanjaro which is another startup which is another startup uh and also multiverse computing which uh and also multiverse computing which is a startup that focuses on software is a startup that focuses on software quantum software for finance and and quantum software for finance and and then then not only that so you you should also not only that so you you should also think about not only about the think about not only about the you know software computers or who is you know software computers or who is building the computer building the computer also who is developing the methods and also who is developing the methods and the devices the devices to build the computer and in europe you to build the computer and in europe you have blue force have blue force which which is a huge company for uh which which is a huge company for uh creating the creating the dilution refrigerators use it for um dilution refrigerators use it for um super conducting uh circuits so these super conducting uh circuits so these are basically the ones that are are basically the ones that are selling the all these refrigerators to selling the all these refrigerators to absolutely everybody including absolutely everybody including big companies like google ibm's et big companies like google ibm's et cetera so and they cetera so and they they are from fina finland sorry they are from fina finland sorry so many uh so some of these enabling so many uh so some of these enabling technology and technology and parties are in europe some of them and parties are in europe some of them and and yeah newest startups are emerging and yeah newest startups are emerging but but i still think that the problem in europe i still think that the problem in europe is that uh is that uh we don't have the tradition like in we don't have the tradition like in america of developing all these america of developing all these startups and so people prefer to stay in startups and so people prefer to stay in academia in general academia in general but probably this is starts to change but probably this is starts to change because some people leave academia because some people leave academia because they are tired of other things because they are tired of other things or because or because it's not for them anymore but now maybe it's not for them anymore but now maybe they have the opportunity to continue they have the opportunity to continue their work and their research in as a their work and their research in as a startup so startup so there i believe that there are there are there i believe that there are there are some opportunities in europe and more some opportunities in europe and more than emerging so we will see it's than emerging so we will see it's kind of a race so between america now kind of a race so between america now europe and of course europe and of course china and and also japan and also in china and and also japan and also in in australia so there are many many in australia so there are many many parties here and in the end is parties here and in the end is we will see who builds the quantum we will see who builds the quantum computer first and who computer first and who and who developed the killer app you and who developed the killer app you know but yeah i believe that now there know but yeah i believe that now there are opportunities around the globe in are opportunities around the globe in general general uh which is very nice so you practically uh which is very nice so you practically you can select whatever continent you you can select whatever continent you want to leave want to leave and try to find the quantum startup and try to find the quantum startup there yeah that's great to hear alba so when you were saying about a alba so when you were saying about a like a clarification on something that like a clarification on something that you were saying when you say you were saying when you say the amount of entanglement grows the amount of entanglement grows exponentially exponentially the is it like a measure that you're the is it like a measure that you're referring to or referring to or so i keep coming across this but uh so i keep coming across this but uh how do you know the entanglement doesn't how do you know the entanglement doesn't grow exponentially grow exponentially sorry if uh sorry if i said that i was sorry if uh sorry if i said that i was mentioning that mentioning that the um the simulation will grow the um the simulation will grow exponentially of course your will exponentially of course your will function will grow exponentially function will grow exponentially the entanglement grows with the well it the entanglement grows with the well it depends what what you are simulating but depends what what you are simulating but with random circuits i believe it grows with random circuits i believe it grows linearly but i'm not super sure about linearly but i'm not super sure about that that with if you simulate condensed matter with if you simulate condensed matter experiments etc experiments etc it grows with the area law so it depends it grows with the area law so it depends but yeah the point is that at some point but yeah the point is that at some point if the entanglement is not if the entanglement is not low you can simulate everything low you can simulate everything classically with this classically with this yeah so but when you say entanglement is yeah so but when you say entanglement is low is it like low is it like uh one human entropy of the entanglement uh one human entropy of the entanglement or like are you or like are you i'm always thinking about entropy i'm always thinking about entropy between you know half of the system between you know half of the system versus the other which is the typical versus the other which is the typical measure measure but there is as far as i know there is but there is as far as i know there is no particular bond no particular bond of course like yeah if you have more of course like yeah if you have more than this you cannot do that anyway than this you cannot do that anyway anymore it's more like if you don't have anymore it's more like if you don't have so when you so when you approximate everything with decent approximate everything with decent networks you need to select your bond networks you need to select your bond dimension which is related with your dimension which is related with your smith rank smith rank yeah so if that will depend so you know yeah so if that will depend so you know if your computer is super powerful you if your computer is super powerful you can can still simulate highly entangled states still simulate highly entangled states you just keep the bond dimension high you just keep the bond dimension high and that's it and that's it so at some point if you want to cut that so at some point if you want to cut that you you you need to take into account that you you need to take into account that you are cutting a lot of entanglement of are cutting a lot of entanglement of your system probably if it doesn't have your system probably if it doesn't have much much it's okay but if if you suspect that it's okay but if if you suspect that there is much there is much it's not so okay but yeah as far as i it's not so okay but yeah as far as i know there is no know there is no you know particular boundary more than you know particular boundary more than this you cannot do that of course not it this you cannot do that of course not it depends on your computer depends on your computer but efficient in the sense that you can but efficient in the sense that you can approximate something that is close to approximate something that is close to the reality the reality yeah and another comment is back to the yeah and another comment is back to the phd or no phd thing uh phd or no phd thing uh like this this is open to all do people like this this is open to all do people here think that there's a benefit of here think that there's a benefit of having an industry experience before having an industry experience before doing their phd doing their phd or uh like directly jumping into phd and or uh like directly jumping into phd and then then exploring industry or academy or exploring industry or academy or whatever whatever so could someone who is experienced or so could someone who is experienced or like who knows more about this like who knows more about this comment on this it's like i don't have comment on this it's like i don't have like industrial experience but like at like industrial experience but like at least like my least like my scientific path was not super straight scientific path was not super straight so so i would say there's no general answer to i would say there's no general answer to this if you have industrial experience this if you have industrial experience before your phd before your phd this for sure has advantages but if you this for sure has advantages but if you jump into your phd directly this also jump into your phd directly this also has advantages because then you're like has advantages because then you're like finished earlier and those things it finished earlier and those things it really depends on really depends on like you like if you have an opportunity like you like if you have an opportunity to have to have like industry experience and you you like industry experience and you you like like that work that you're doing there then that work that you're doing there then you should do it but you should not you should do it but you should not force yourself to do something like this force yourself to do something like this in order to have some in order to have some possible advantage because then usually possible advantage because then usually this goes wrong this goes wrong like if you're if you're not enjoying it like if you're if you're not enjoying it yeah of course yeah of course of course but the question is if you of course but the question is if you have like both options that have like both options that you have an option to go to the industry you have an option to go to the industry you have an option to go to the academia you have an option to go to the academia then uh how would how would one go about then uh how would how would one go about weighing them weighing them both are interesting work let's say if both are interesting work let's say if you got to industry before like some of you got to industry before like some of my colleagues my colleagues worked like an industry a year or worked like an industry a year or something and then they decided they something and then they decided they want to do a phd want to do a phd and this wasn't a disadvantage for them and this wasn't a disadvantage for them like also like when they applied here uh like also like when they applied here uh in toronto like this was more like you in toronto like this was more like you have some additional experience have some additional experience right if you have been in industry like right if you have been in industry like for 10 years people might get skeptical for 10 years people might get skeptical um but in principle like um but in principle like i think that's fine i mean in general to i think that's fine i mean in general to the phd question from the four the phd question from the four like i think it's actually like if you like i think it's actually like if you want to do science you should get a phd want to do science you should get a phd there's no way around there's no way around it you can you can do things without a it you can you can do things without a phd phd but then you always have to be the one but then you always have to be the one who really sticks out yeah who really sticks out yeah because otherwise you will be the one because otherwise you will be the one who like who like if you like apply somewhere like they if you like apply somewhere like they get so many like applications get so many like applications and this is like the first thing like and this is like the first thing like how they filter it out how they filter it out and if you don't like have like i mean and if you don't like have like i mean you can you can be like if you have like a super good be like if you have like a super good reputation or something like reputation or something like then it's fine but like if people don't then it's fine but like if people don't know you like and you don't have a phd know you like and you don't have a phd they will just they will just this will be the first filter criteria this will be the first filter criteria and these other qualities that you might and these other qualities that you might bring they won't even see bring they won't even see yeah that's also like if you want to do yeah that's also like if you want to do research there's a lot of like things research there's a lot of like things you can apply for independent grants you can apply for independent grants like like uh you can apply like being like uh uh you can apply like being like uh like the union after your phds like for like the union after your phds like for postdoc grants like those things postdoc grants like those things they all require a phd it's like all they all require a phd it's like all these opportunities you will not have these opportunities you will not have if you don't have it like at least in if you don't have it like at least in the european system and i think it's the the european system and i think it's the same same in the american one so like you have a in the american one so like you have a huge disadvantage if you don't if you huge disadvantage if you don't if you don't have it don't have it but this is really just like if you want but this is really just like if you want to do research to do research if you want to work in science and if you want to work in science and technology that technology that might be different actually but if you might be different actually but if you really like if your goal is to do really like if your goal is to do independent research at some point independent research at some point you need to get a phd sooner or later you need to get a phd sooner or later yeah yeah definitely yeah it's also it's a good definitely yeah it's also it's a good like thing because like if you do your like thing because like if you do your phd soon like and then you maybe figure phd soon like and then you maybe figure out that you out that you don't like academic research at all and don't like academic research at all and that it's not like how you thought it that it's not like how you thought it was was then it's better like to make that then it's better like to make that experience like earlier experience like earlier and then you can still go to industry and then you can still go to industry like it's it's not that they say oh now like it's it's not that they say oh now you have a phd like you have a phd like no way we're gonna hire you it's more no way we're gonna hire you it's more like it's usually an advantage like it's usually an advantage right okay i can give you guys maybe a right okay i can give you guys maybe a little little unusual perspective uh it took me 15 unusual perspective uh it took me 15 years to finish my phd years to finish my phd right on my business in computer science right on my business in computer science uh and uh and um and actually i think i was the first um and actually i think i was the first uh graduate student who downloaded a uh graduate student who downloaded a significant chunk of twitter so i was significant chunk of twitter so i was the first receiver of the twitter the first receiver of the twitter streaming api streaming api and that's kind of my small clinton fan and that's kind of my small clinton fan that i discovered justin bieber when that i discovered justin bieber when nobody knew who he was nobody knew who he was uh so that's a little bit different uh so that's a little bit different right and so my my undergrad was in right and so my my undergrad was in physics so kind of you know physics so kind of you know uh what i would say in and then uh what i would say in and then basically i joined startups right so the basically i joined startups right so the first time i kind of lapsed in science first time i kind of lapsed in science when i joined in 2000 when i joined in 2000 a computer uh kind of you know internet a computer uh kind of you know internet boom and then boom and then you know the moment i finished my phd i you know the moment i finished my phd i actually went to actually went to silicon valley and joined startup so so silicon valley and joined startup so so i think really i think really i think you know it's kind of so i kind i think you know it's kind of so i kind of straddle academia and of straddle academia and industry and i kind of oscillated and industry and i kind of oscillated and finally desolation was kind of ended finally desolation was kind of ended when i ended up here right uh and so i when i ended up here right uh and so i think it's and i'm pretty unusual in the think it's and i'm pretty unusual in the sense that i finished my it took me 15 sense that i finished my it took me 15 years to finish my phd years to finish my phd and i finished it most people who who go and i finished it most people who who go to startups never do to startups never do so you know the force function was you so you know the force function was you know my first child was going to be born know my first child was going to be born and i kind of suddenly realized it's and i kind of suddenly realized it's much better to be a dad in a small much better to be a dad in a small university town than in the big city so university town than in the big city so i actually kind of went to i actually kind of went to to dartmouth to be kind of uh to work to dartmouth to be kind of uh to work with my regional advisors who my with my regional advisors who my my degrees from japan but my committee my degrees from japan but my committee member was at dartmouth and member was at dartmouth and he um uh george benka he uh you know he um uh george benka he uh you know basically had a lot of um basically had a lot of um uh grants for for doing this kind of uh grants for for doing this kind of research right so so i think it really research right so so i think it really changed it really depends uh what you're changed it really depends uh what you're gonna do gonna do right uh uh so i thought phd is very right uh uh so i thought phd is very important in order to collaborate with important in order to collaborate with academia i never academia i never wanted to make an academic career so wanted to make an academic career so people told me dude like you're too old people told me dude like you're too old already like you know you have to be already like you know you have to be like you know if you don't make it like you know if you don't make it before 30 you know you will not have a before 30 you know you will not have a kind of traditional kind of you know kind of traditional kind of you know stellar career stellar career track in in in universities right but track in in in universities right but what i think what really changes what i think what really changes especially especially uh with um kind of startup culture uh with um kind of startup culture right that a lot of work is done in right that a lot of work is done in industry so i think industry so i think what what what's really interesting for what what what's really interesting for me in this quantum conversations context me in this quantum conversations context is we see these two forces collide is we see these two forces collide right and so and i think quantum field right and so and i think quantum field is a bit is a bit delayed compared to traditional computer delayed compared to traditional computer science because if you look at computer science because if you look at computer science science you know it started in the 50s and 60s you know it started in the 50s and 60s there was no computers right so who there was no computers right so who joined it there are people from joined it there are people from linguistics linguistics math and physics people with the math and physics people with the traditional academic background traditional academic background right and they all fuse together and right and they all fuse together and suddenly in suddenly in general computer science now there's a general computer science now there's a feeling that like you're wasting your feeling that like you're wasting your time if you do a phd time if you do a phd because instead of you know spending because instead of you know spending five years on a phd you should have five years on a phd you should have joined google joined google in the year 2000 you know we would be in the year 2000 you know we would be like a zillionaire and you can like a zillionaire and you can study anything at your leisure right uh study anything at your leisure right uh or or you can do a startup so the kind of the you can do a startup so the kind of the wisdom in the silicon valley wisdom in the silicon valley shifted to kind of advice for action you shifted to kind of advice for action you know go in and do something first know go in and do something first figure it out right is so so in that figure it out right is so so in that sense i'm a big proponent that sense i'm a big proponent that it's really useful to take a break it's really useful to take a break between for instance master's and phd between for instance master's and phd right like i would not i would not right like i would not i would not unless you really want to be unless you really want to be a professor in the top school right then a professor in the top school right then if you really and i see a lot of folks if you really and i see a lot of folks like this right so i think like this right so i think and still regardless of enough all the and still regardless of enough all the money which is kind of flourishing money which is kind of flourishing around around the field we have folks like this even the field we have folks like this even in deploying machine learning in deploying machine learning who refuse to join a startup and advance who refuse to join a startup and advance science so i think it's really science so i think it's really what is kind of what is your goal but if what is kind of what is your goal but if you if you if if you really want to change the world a if you really want to change the world a big scale for industry i think you big scale for industry i think you should really take a break and should really take a break and at least a year or two in turn we see a at least a year or two in turn we see a lot of people in turning lot of people in turning then there are in the us this scope then there are in the us this scope system in some places and system in some places and in canada there are like you know so in canada there are like you know so waterloo i think is very famous for this waterloo i think is very famous for this and stuff like that and stuff like that so a lot of these folks went to amazon so a lot of these folks went to amazon for instance right amazon hired a bunch for instance right amazon hired a bunch of of uh waterloo graduates because they had uh waterloo graduates because they had this experience and in the uss drexel this experience and in the uss drexel director university you know encourages director university you know encourages people to take breaks so people to take breaks so i i would really say it's very i i would really say it's very interesting right it's it's really interesting right it's it's really interesting how this field evolves and interesting how this field evolves and and i think we have kind of academic and i think we have kind of academic track people here who have industry track people here who have industry track people and i'd like to see more track people and i'd like to see more of both and kind of we'll see what of both and kind of we'll see what happens going forward but but i think happens going forward but but i think more and more people need industry more and more people need industry experience because you need to integrate experience because you need to integrate as this software gets into the world as this software gets into the world more and more people use it you get more more and more people use it you get more and more people and more people without phds who who need help right and without phds who who need help right and so so so i think it's really common on so i think it's really common on everybody here to kind of figure out how everybody here to kind of figure out how do we educate people do we educate people uh how do we do one-on-one kind of level uh how do we do one-on-one kind of level courses how do we do tutorials you know courses how do we do tutorials you know it's not really all about it's not really all about uh pushing the envelope how do we uh pushing the envelope how do we educate most people it kind of basic educate most people it kind of basic get them up to speed how and you know get them up to speed how and you know obviously coding is fun because obviously coding is fun because like something like tequila can check it like something like tequila can check it out on github and play with it right out on github and play with it right like this is the beauty of this like this is the beauty of this so let's get on some thoughts so let's get on some thoughts i had yeah my thoughts align i had yeah my thoughts align more with yours with your direction lexi more with yours with your direction lexi yeah it's just one thing um taking keep yeah it's just one thing um taking keep in mind the different mentality in that in mind the different mentality in that sense between america and europe sense between america and europe because that in america can work but for because that in america can work but for instance this oxidation taking 50 years instance this oxidation taking 50 years for having a pizza etc for having a pizza etc in europe in many universities you have in europe in many universities you have to achieve your phd in to achieve your phd in four or five years or you're done you four or five years or you're done you know so know so you need to do that because you are you need to do that because you are forced to so it depends on the model so forced to so it depends on the model so just if you just if you if you take that in mind and it's like if you take that in mind and it's like okay it's fine but i will do that in okay it's fine but i will do that in america which is not a problem so america which is not a problem so go ahead and taking a year to think go ahead and taking a year to think about your future and about your future and explore other possibilities i believe explore other possibilities i believe it's always it's always nice and good and if you want to come nice and good and if you want to come back to academia the only thing that you back to academia the only thing that you should also think is should also think is you need to continue publishing somehow you need to continue publishing somehow because it's the way because it's the way that you show that you have done that you show that you have done something etc something etc but still one year it's more than okay but still one year it's more than okay for for for achieving other things and as jacob for achieving other things and as jacob said i mean said i mean sometimes you start something and you sometimes you start something and you just realize that it's not for you which just realize that it's not for you which is is perfect because you don't lose your time perfect because you don't lose your time and you just move to another thing and you just move to another thing and that's that's uh very okay and now and that's that's uh very okay and now with quantum computing which is super with quantum computing which is super interesting is that interesting is that in all these new startups and also big in all these new startups and also big companies companies you you're actually doing research some you you're actually doing research some sometimes so sometimes so it's like being in academia but you're it's like being in academia but you're not in academia so you just not in academia so you just have to keep in mind that at some point have to keep in mind that at some point the things can change completely like the things can change completely like you know your boss tells you okay now you know your boss tells you okay now you have to work in this algorithm and you have to work in this algorithm and that's it that's it but in general it's not what is but in general it's not what is happening because in the end your boss happening because in the end your boss will be probably will be probably a guy who left academia so his a guy who left academia so his background or her background was background or her background was academia too academia too so so that's what's going on in quantum so so that's what's going on in quantum computing at the moment so it's not so computing at the moment so it's not so different from academia because all different from academia because all these people that is funding new these people that is funding new startups are people that are from startups are people that are from academia basically so that's why their academia basically so that's why their mentality is academia and that's why mentality is academia and that's why what uh they were saying before about what uh they were saying before about the intel etc they have this bias by psd the intel etc they have this bias by psd because in the end who hires you is a because in the end who hires you is a guy from academia so he guy from academia so he he or she has this bias too so but it's he or she has this bias too so but it's something that it will change with time something that it will change with time i'm sure so i'm sure so so yeah taking a year for it and that's so yeah taking a year for it and that's why the internships exist in the end so why the internships exist in the end so you have this opportunity to explore you have this opportunity to explore other paths other paths which is always good yeah but also these internships like yeah but also these internships like they usually ask the big research groups they usually ask the big research groups if their phds want to do internships if their phds want to do internships and then i think there was more like the and then i think there was more like the phenomenon that they phenomenon that they really didn't had like enough people really didn't had like enough people like the pool was not large enough like the pool was not large enough to fish from so they opened it up but to fish from so they opened it up but this will not stay like this this will not stay like this it's also not that i that i like that it it's also not that i that i like that it will not stay like this or something will not stay like this or something it's just i think that's a reality it's just i think that's a reality like there will be like more people like there will be like more people doing phds in that direction and then doing phds in that direction and then you will you will those are your competitors right so um those are your competitors right so um if you don't do one you haven't if you don't do one you haven't a disadvantage a clear one it's a disadvantage a clear one it's it's not that i'm in favor of the system it's not that i'm in favor of the system but um but um i think this is more or less how it i think this is more or less how it works it's like i don't want to like works it's like i don't want to like create the impression like you don't create the impression like you don't need it you can just do whatever you need it you can just do whatever you want um want um would be nice but i yeah you kind of would be nice but i yeah you kind of need it all right uh i think that that was a all right uh i think that that was a really good really good coverage of the phd topic i still you coverage of the phd topic i still you know we didn't get to the question of know we didn't get to the question of kind of kind of initial resources i wonder you know if initial resources i wonder you know if any of you guys have any of you guys have uh advice right like for uh advice right like for uh let's say software engineer in uh let's say software engineer in silicon valley who wants to get into the silicon valley who wants to get into the field field right and so they are practitioners so right and so they are practitioners so they're not graduate students they're not graduate students you know they they just want to like the you know they they just want to like the self-learners right essential do you self-learners right essential do you think it's feasible think it's feasible to do some self-learning in this field to do some self-learning in this field or do they need to kind of or do they need to kind of take classes and go to school what do take classes and go to school what do you guys think you guys think well i can give you um i can give you well i can give you um i can give you someone coming from an industry someone coming from an industry perspective and i'll take a non-standard perspective and i'll take a non-standard approach to answering this and that i approach to answering this and that i i'm not a fan of like i'm not a fan of like trying to get into the industry and i i trying to get into the industry and i i think i think it is a think i think it is a uh it's maybe a place of like uh it's maybe a place of like it depends on your your current it depends on your your current employment if you're employment if you're if you have a nice stable employment if you have a nice stable employment you're able to you're able to learn this stuff in your spare time and learn this stuff in your spare time and um yeah and maybe at least position yeah and maybe at least position yourself in your own company yourself in your own company as close to quantum computing as as close to quantum computing as possible like whether that's being possible like whether that's being involved in machine learning at your involved in machine learning at your company or whatever company or whatever you know there's there's some point you know there's there's some point within your company that you could within your company that you could probably get to probably get to if you're not there and and my thought if you're not there and and my thought is is instead of trying to get into the instead of trying to get into the industry i would rather i take this industry i would rather i take this viewpoint of let me build my skills viewpoint of let me build my skills enough enough and start contributing where the only and start contributing where the only thing i can do thing i can do to continue is to join is to become to continue is to join is to become involved in the industry involved in the industry more so than trying to find an avenue in more so than trying to find an avenue in for a job and for a job and you know each person may have their own you know each person may have their own needs needs maybe they you know they're coming out maybe they you know they're coming out of school they need a job or whatever of school they need a job or whatever but but that's my approach i think there are that's my approach i think there are plenty of good resources quantum country plenty of good resources quantum country quantum duck country uh the quantum duck country uh the um i went through the mit courses those um i went through the mit courses those are kind of expensive so i think are kind of expensive so i think the one on edx from berkeley is the one on edx from berkeley is phenomenal with um phenomenal with um uh i'm trying uh vazarani uh teaching uh i'm trying uh vazarani uh teaching um and yeah and and um and yeah and and now and i to the phd point i think it is now and i to the phd point i think it is very important to have a phd for the very important to have a phd for the level of formalism level of formalism and the type of research we're going to and the type of research we're going to be doing in the field for some time be doing in the field for some time there's definitely a lot of software there's definitely a lot of software engineering jobs but i'm not so engineering jobs but i'm not so interested in that to be honest even interested in that to be honest even though and hence my point about though and hence my point about getting into the industry yeah it'd be getting into the industry yeah it'd be cool to say i'm working on quantum cool to say i'm working on quantum computing stuff but i would much rather computing stuff but i would much rather put in the time to become an effective put in the time to become an effective researcher researcher and that's just my take thank you this is great perspective yeah thank you this is great perspective yeah absolutely if you know if you're able to absolutely if you know if you're able to balance it right it's essentially balance it right it's essentially if you can hold the job uh in the valley if you can hold the job uh in the valley and and and and learn on your own time right uh learn on your own time right uh that's certainly one option for folks that's certainly one option for folks who are curious and thanks for the merit who are curious and thanks for the merit posted a very cool uh link posted a very cool uh link in the chat so thanks for that in the chat so thanks for that so that's uh by one of the authors of so that's uh by one of the authors of um the nielsen and trunk um the nielsen and trunk book is like the bible of quantum book is like the bible of quantum computing in my opinion and computing in my opinion and nielsen is the person who created this nielsen is the person who created this website so website so um i didn't use it myself but um i think um i didn't use it myself but um i think it's really cool and uh it's really cool and uh i ha i think there is a clear um i ha i think there is a clear um [Music] there's no way to like a good a good there's no way to like a good a good platform to learn quantum computing on platform to learn quantum computing on your own your own uh where whereas there is like many like uh where whereas there is like many like you can teach yourself you can teach yourself machine learning now or like any other machine learning now or like any other emerging field emerging field but it's it's really hard for quantum but it's it's really hard for quantum computing because of the noise as alba computing because of the noise as alba mentioned in the mentioned in the in the in the field so um there's in the in the field so um there's definitely a need definitely a need for like something like an academy or or for like something like an academy or or an online an online platform to to self self teach quantum platform to to self self teach quantum computing computing yeah i can comment on that i use i have yeah i can comment on that i use i have started started learning the part of quantum media and learning the part of quantum media and there's a there's a a book uh says i think it's from this a book uh says i think it's from this year that has like the year that has like the from case kit let me take it from it and from case kit let me take it from it and it has like the interactive things and it has like the interactive things and on all the algorithms so you can on all the algorithms so you can like as you say do the machine learning like as you say do the machine learning things things you can try it out and step by step you can try it out and step by step trying to learning trying to learning and also i think that the cascade videos and also i think that the cascade videos that that they send in youtube all the time they they send in youtube all the time they are pretty are pretty good ones to start learning like the good ones to start learning like the beats if you don't have any idea beats if you don't have any idea of what uh what are you thinking and of what uh what are you thinking and one thing that i like is like in the one thing that i like is like in the point of point of quantum computing like taking like quantum computing like taking like out all the physics that you need and out all the physics that you need and say okay this is the operational way to say okay this is the operational way to do do quantum of beauty it's really quantum of beauty it's really interesting let me let me interesting let me let me see what is the name of the wall but maybe i what is the name of the wall but maybe i will bring you will bring you into there into the chat but yeah i think from my into the chat but yeah i think from my perspective perspective i'm commenting on what else i i did my i'm commenting on what else i i did my masters masters in physics and then i went into industry in physics and then i went into industry so and and then it's like it it was so and and then it's like it it was like a coincidence that they started like a coincidence that they started doing quantum doing quantum things in in during jb morgan so it was things in in during jb morgan so it was like oh you have you can have this like oh you have you can have this opportunity opportunity but i left the bank but i left the bank then but i am trying to do it again and then but i am trying to do it again and it's like it's like as you say a lot of noise and a lot of as you say a lot of noise and a lot of things to things to to learn and maybe if you want to learn and maybe if you want i don't know how is here but in i'm i'm i don't know how is here but in i'm i'm in argentina and it's really complicated in argentina and it's really complicated to to find like jobs for for doing like find like jobs for for doing like research or or doing something in research or or doing something in industry watching eating here in the industry watching eating here in the south south and it is it's another problem you you and it is it's another problem you you just talk like just talk like in united states and in europe but in in united states and in europe but in south america is like a complete south america is like a complete different thing so it's really different thing so it's really complicated complicated to do it by your own to do it by your own but i don't know there's one thing that but i don't know there's one thing that i see is like if you're i see is like if you're coming from this software engineering coming from this software engineering team it's team it's like alwa said you need uh people that like alwa said you need uh people that need need know how to do that the good quan the know how to do that the good quan the good engineering the software good engineering the software engineering engineering and that's the problem i know and in and that's the problem i know and in science like science like software in science is like very messy software in science is like very messy scripts scripts and trying to build up is like a and trying to build up is like a knowledge that you can have from there knowledge that you can have from there so it's like yeah i don't know it's so it's like yeah i don't know it's now the whole globalization and this is now the whole globalization and this is the remote works the remote works maybe you can have a like a new maybe you can have a like a new perspective of what you want to do perspective of what you want to do maybe there's new positions in startups maybe there's new positions in startups or or local i don't know if you do like local i don't know if you do like meetups meetups and then people is like trying to engage and then people is like trying to engage in that that kind of things in that that kind of things maybe can start doing to to learn let me check the book thanks sasha actually you know guys uh i thanks sasha actually you know guys uh i just wanted kind of it occurred to me just wanted kind of it occurred to me you know amir and sasha you guys spent you know amir and sasha you guys spent obviously uh obviously uh uh some time educating yourselves maybe uh some time educating yourselves maybe uh we can do um a specific uh uh we can do um a specific uh uh quantum conversations meeting uh quantum conversations meeting uh where folks will just you know give uh where folks will just you know give talks how they approach the field right talks how they approach the field right and i think it even applies to the and i think it even applies to the academia for instance right like academia for instance right like um because even in universities a lot of um because even in universities a lot of people come into quantum people come into quantum field from other areas right because field from other areas right because they started out maybe in some other they started out maybe in some other areas of physics or chemistry areas of physics or chemistry or computer science so uh i would really or computer science so uh i would really appreciate it if you guys appreciate it if you guys want to give this talk maybe it does want to give this talk maybe it does have to be long right because we can do have to be long right because we can do for instance for instance you know uh three twenty minute talks or you know uh three twenty minute talks or two two thirty minute talks right like we can thirty minute talks right like we can and we're a bit flexible so and we're a bit flexible so um think about it right uh email me um think about it right uh email me uh you know my mail i'll uh you know my mail i'll put it here again so you can always put it here again so you can always contact me alexi contact me alexi chief scientist.org i had i held the job chief scientist.org i had i held the job of a chief scientist at some point so i of a chief scientist at some point so i kind of kind of got this you know uh got this you know uh handle so uh you know if you want to handle so uh you know if you want to propose a talk you know propose a talk you know just send me uh a note and we i would just send me uh a note and we i would really appreciate it because i think it really appreciate it because i think it would be very useful for others would be very useful for others to learn how you learn like how do you to learn how you learn like how do you know because a lot of this is know because a lot of this is self-learning self-learning discovering resources right and even in discovering resources right and even in the in the research group kind of the in the research group kind of how do you navigate the field uh and and how do you navigate the field uh and and and maybe and maybe like like you guys already share some like like you guys already share some links uh links uh in the chat maybe you can actually kind in the chat maybe you can actually kind of post some slides of post some slides with some you know useful things which with some you know useful things which were useful for you were useful for you right because different things are right because different things are helpful for different people helpful for different people so just you know requests for basically so just you know requests for basically requests for requests for for proposals so maybe we can do the next one so maybe we can do the next one yeah i will try to see if i can build yeah i will try to see if i can build something something and i will email you yeah like can be and i will email you yeah like can be pretty pretty informal right like you can you can put informal right like you can you can put together some some kind of together some some kind of a few slides and you can talk to kind of a few slides and you can talk to kind of what you know your general experience what you know your general experience so yes thank you have a quick question yes thank you have a quick question uh about tequila for alvaro jacob um uh about tequila for alvaro jacob um i guess a real quick um what types so i guess a real quick um what types so say like we want to create like a group say like we want to create like a group of students who want to work of students who want to work on tequila and help out on the github um on tequila and help out on the github um and help with issues what type of i and help with issues what type of i guess prereqs or skills guess prereqs or skills would they need um besides maybe basic would they need um besides maybe basic like python coding like python coding um to like effectively contribute to the um to like effectively contribute to the new github uh depends a little bit on like what uh uh depends a little bit on like what uh what feature like you want to implement what feature like you want to implement um for example um for example the easiest thing is like if you have the easiest thing is like if you have like developed some algorithm which just like developed some algorithm which just uses tequila uses tequila and then you want to like integrate this and then you want to like integrate this like as a like like as a like as a boxed module which you just can as a boxed module which you just can call call then it's like it's pretty easy like to then it's like it's pretty easy like to contribute because um contribute because um that usually you don't create a lot of that usually you don't create a lot of conflicts with that because this is like conflicts with that because this is like just just your code um and then you just uh you your code um and then you just uh you just make a pull request just make a pull request and that's it um and then usually like and that's it um and then usually like we will go over the code and like check we will go over the code and like check if like uh if like uh some things could be potential conflicts some things could be potential conflicts but that's more or less it but that's more or less it if you want to go deeper into the if you want to go deeper into the library it marks more or less the same library it marks more or less the same like like if you you can add like features or like if you you can add like features or like optimize it like deeper in the library optimize it like deeper in the library you make like a pull request we go over you make like a pull request we go over the code and the code and we might notice some things where we see we might notice some things where we see um that this will cause trouble um that this will cause trouble for other projects which like usually it for other projects which like usually it should like already be flagged in the should like already be flagged in the automatic like tests automatic like tests which run on github but like if not then which run on github but like if not then we might be like um we might be like um would be good to change this like this would be good to change this like this and then we can start like discussions and then we can start like discussions um um but that's more or less it's um if but that's more or less it's um if someone like someone like would like plan like to make like really would like plan like to make like really deep deep changes or something it might also be changes or something it might also be like useful like to let us know like useful like to let us know beforehand beforehand then we can try like to coordinate um then we can try like to coordinate um but it's more or less like this like you but it's more or less like this like you need need you need to have the skills like to you need to have the skills like to implement what you want to do like of implement what you want to do like of course and then it's just like you need course and then it's just like you need roughly to know how git works um roughly to know how git works um if that's a problem like people can also if that's a problem like people can also like just approach us like just approach us and say like how does it work with the and say like how does it work with the forking and pull requests like it's forking and pull requests like it's then we can like hint to like the one of then we can like hint to like the one of the thousand like tutorial videos the thousand like tutorial videos then uh like it already worked like some then uh like it already worked like some some people already like did some some people already like did some contributions um contributions um and it worked fine yeah in the end if and it worked fine yeah in the end if you want just to start and get you want just to start and get familiarized with it that it can familiarized with it that it can recommend you to just recommend you to just you know one algorithm that you have you know one algorithm that you have developed or maybe developed or maybe not have developed but you want just you not have developed but you want just you know to practice with that oops sorry know to practice with that oops sorry and create a tutorial with tequila and create a tutorial with tequila you know and then we add that to uh to you know and then we add that to uh to the tutorial section the tutorial section so after that you got familiarized with so after that you got familiarized with it and maybe if you it and maybe if you it's it could be a way to just show your it's it could be a way to just show your work and then after that that tutorial work and then after that that tutorial could become a module of tequila you could become a module of tequila you know know so after you know the clear idea how to so after you know the clear idea how to do that etc do that etc we also know that so you can just okay we also know that so you can just okay let's implement that in tequila directly let's implement that in tequila directly and then we also for instance if you and then we also for instance if you have some simulator in mind or some have some simulator in mind or some other language that you want to other language that you want to to also add as a backend it's you know to also add as a backend it's you know as jacob said as jacob said it's okay let's move let's add another it's okay let's move let's add another backhand and this is something that is backhand and this is something that is made separately of tequila made separately of tequila and and you just add that and of course and and you just add that and of course it has to pass the test etc so it has to pass the test etc so there are many ways to contribute so there are many ways to contribute so just you know explored a little with the just you know explored a little with the github repo and let us know because of github repo and let us know because of course we can help course we can help okay well yeah the main reason i asked okay well yeah the main reason i asked is because um at stanford i guess we're is because um at stanford i guess we're creating a new initiative uh like a new creating a new initiative uh like a new course for next quarter like we're course for next quarter like we're starting january starting january where we put a teams of students where we put a teams of students together undergraduates or graduates to together undergraduates or graduates to work on open source projects or in work on open source projects or in general like open with open source general like open with open source resources resources um i think tequila be a great i guess um i think tequila be a great i guess like uh like uh package to work with as well and so package to work with as well and so either one like students could try to either one like students could try to just you know just you know improve tequila and fix issues or in improve tequila and fix issues or in general help out you guys in certain general help out you guys in certain parts of the github but also we also parts of the github but also we also hope in that class to use tequila hope in that class to use tequila ideally if students don't feel ideally if students don't feel comfortable with it as like an actual comfortable with it as like an actual resource to do quantum simulations resource to do quantum simulations um so it's good to know like what sort um so it's good to know like what sort of background you need and how you both of background you need and how you both said like you said like you not really too much i suppose it's gotta not really too much i suppose it's gotta make your own modules more kind of make your own modules more kind of yeah that would be very cool actually yeah that would be very cool actually because in the end we also need feedback because in the end we also need feedback you know you know so like for instance i don't i don't so like for instance i don't i don't like that tequila implements this like that tequila implements this syntax in this way for some reason so syntax in this way for some reason so maybe we can maybe we can check that so it's also good that more check that so it's also good that more people start using it so in the end people start using it so in the end tequila skeleton is there so the idea is tequila skeleton is there so the idea is that that everybody that wants to contribute and everybody that wants to contribute and add more things it would be easier to do add more things it would be easier to do that because you just have to that because you just have to wrote the module and plug it and of wrote the module and plug it and of course if you want to course if you want to check the code more in more detail so check the code more in more detail so you can you can also do that so yeah that would be great also do that so yeah that would be great so let us know if you decide to do that so let us know if you decide to do that because because we will be more than happy to provide we will be more than happy to provide any help that is necessary any help that is necessary yes definitely awesome thank you yeah yes definitely awesome thank you yeah there's also the opportunity like there's also the opportunity like especially if it's like especially if it's like a group of students um we could also a group of students um we could also like add them to a shared slack channel like add them to a shared slack channel with with us or something and if they have like us or something and if they have like questions we can answer them on the questions we can answer them on the fast way because sometimes people are fast way because sometimes people are also like afraid like if they have also like afraid like if they have questions like to raise a github issue questions like to raise a github issue because it's like on because it's like on worldwide display so to say and i think worldwide display so to say and i think it's like it's a limiting factor often it's like it's a limiting factor often and then it's like and then it's like then sometimes something doesn't work or then sometimes something doesn't work or they get like some error messages they they get like some error messages they can just can just send them to us and then we have a look send them to us and then we have a look it's basically what we do like with our it's basically what we do like with our like colleagues also who use it um like colleagues also who use it um now we are we have some we will have now we are we have some we will have some mentees of the quantum open source some mentees of the quantum open source foundation i foundation i i see that some of you mentioned the i see that some of you mentioned the open source thing and they will be open source thing and they will be working with the kill i mean they have working with the kill i mean they have different backgrounds so they we will different backgrounds so they we will give them different projects to work give them different projects to work with with and the ideas you know help us to and the ideas you know help us to collaborate and grow the community and collaborate and grow the community and this is something this is something not only open source but also develop not only open source but also develop from from academia from university of from from academia from university of toronto so it's toronto so it's it you know it's the idea is that it you know it's the idea is that everybody around the world everybody around the world it doesn't matter if you're part of a it doesn't matter if you're part of a company or academia you don't have to company or academia you don't have to ask permission to anyone just contribute ask permission to anyone just contribute if you want and yeah that would be very if you want and yeah that would be very great great and we have a very list of things that and we have a very list of things that we want to implement we want to implement so we can also give you some ideas if so we can also give you some ideas if you don't know you don't know any uh of what kind of things can you any uh of what kind of things can you help us to contribute and help us to contribute and increase the features in tequila increase the features in tequila you know screen maybe over the next you know screen maybe over the next month or so i'll reach out to you guys month or so i'll reach out to you guys and we can have a deeper conversation and we can have a deeper conversation about this yeah feel free to do so yeah about this yeah feel free to do so yeah so i was like we have a little bit of so i was like we have a little bit of overview like over like for example overview like over like for example some of those for this open source some of those for this open source foundation foundation who are doing some projects and then if who are doing some projects and then if some other students want to do more or some other students want to do more or less the same less the same like we can already like prevent that um like we can already like prevent that um they are like like working on that and they are like like working on that and then the next month someone else like then the next month someone else like adds that and then like they feel like adds that and then like they feel like uh uh then that's a big bump on motivation i then that's a big bump on motivation i would say would say i'm like to avoid like having these kind i'm like to avoid like having these kind of like clashes of like clashes like they don't have to they don't have like they don't have to they don't have to share their research secrets with us to share their research secrets with us but like but like if it's like uh basic if it's like uh basic like projects which are fine like to like projects which are fine like to share so um share so um just yeah yeah feel free to reach out just yeah yeah feel free to reach out like we're more than happy to help with like we're more than happy to help with that that awesome thank you again all right i think we covered a bunch of all right i think we covered a bunch of topics and so i think we're kind of to topics and so i think we're kind of to our our mark so i want to thank everyone uh mark so i want to thank everyone uh for uh great quantum conversations and i for uh great quantum conversations and i think think going forward uh i think this format going forward uh i think this format really works we will have one main talk really works we will have one main talk in my main theme right and we also have in my main theme right and we also have uh uh kind of a longer general discussion i kind of a longer general discussion i think really great topics came up we'll think really great topics came up we'll have some links i'll post them have some links i'll post them uh on the site and i want to thank again uh on the site and i want to thank again uh jeremiah and merc uh jeremiah and merc as community organizers again you guys as community organizers again you guys are very welcome to are very welcome to uh to join in and help this is really a uh to join in and help this is really a community endeavor community endeavor so thanks a lot everybody really so thanks a lot everybody really appreciate it and uh appreciate it and uh send me some proposals for the talks i send me some proposals for the talks i think you know uh this kind of think you know uh this kind of one-on-one self-earning one-on-one self-earning theme uh would be great for the next theme uh would be great for the next time so i'll ask some folks to time so i'll ask some folks to to to give talks and send me some to to give talks and send me some proposals and i proposals and i see you guys again last wednesday of see you guys again last wednesday of november november thank you very much thanks a lot guys thank you very much thanks a lot guys have a nice day thank you guys have a nice day thank you guys everyone thank you