QC IX: Quantum Finance - Academia & Industry
Recording: QC IX: Quantum Finance - Academia & Industry
okay so hello everyone uh welcome to quantum conversations number nine uh today we're going to talk about um the relationship of industry and academia particularly focusing on um quantum finance um both theory and practice so i am mart and amartha sanjana i just graduated from stanford my master's for my undergrad i was also at stanford i did a double major in physics and symbolic systems for for my master's i focused on management science and engineering i was a teaching assistant for the quantum computing course offered on campus and i've been working closely with the stanford quantum computing association uh i led the committee uh the partnerships committee there for a bit now josh leads the partnerships committee so you actually want to introduce yourself yeah uh hi everyone my name is josh i'm a third year at stanford currently majoring in physics and prospectively double majoring in math um yeah i'm the partners and committee partners and events uh committee lead uh right now um and yeah i'm glad that everybody's here and showing an interest in quantum computing yes so for our main speaker today we're going to have dmitri chairmoshan said if i'm not watching your name and um works on a very cool project uh on using quantum techniques to do or to optimize portfolios he's working on real world cases right now um so we're going to focus on that and this is going to be very informal we also have a professor wing sheet kim participating as well he will be in the audience and he will chip in from time to time uh professor kim is a chair of the theoretical part of information sciences at imperial university um his main research is not in quantum finance but he's interested in quantum finance and he also he holds an mba as well so he has worked on options pricing before um so today i would like to be very very informal and so it will be very much discussion based usually who people who have participated in our sessions know that we we are more uh we are more on the informal side where people talk all the time it's very much discussion based so this is not like a presentation more it's more like a a talk of of friends so it would be great if he went around quickly and asked everyone to introduce themselves and um so it would be great if people are available they would open up their cameras on and maybe give like a five minute introduction so dmitry can we do that first before the presentation if that's fine uh yes of course um my name is yeah yes sorry okay okay let's start yes we can start now yes yeah we can hear you um okay uh hello dear colleagues my name is phd student at skulltech it's a scholarly institute of science and technology in russia and also i'm a researcher in the quantum optics group or the russian quantum center my scientific advisor is alexander livorsky who is a professor at the university of oxford and today i'd like to provide a general talk about uh some part of our research projects in the area of optimization like quantum adiabatic and quantum inspired algorithms uh in different tasks especially in portfolio optimization okay just a moment okay thank you um and and yeah can we uh do the introductions of the participants as well just for like five minutes before we delve into the of course okay um so uh joshua already introduced himself logan i you have been coming to our sessions a lot and uh we'd love to hear about you as well okay let's go to jamie all right hey mert um i'm jamie uh taking classes at stanford this quarter and uh next fall i'll be in graduate school quantum computing at wisconsin madison and we have nathan hi nathan hi uh so i'm a retired guy who's always been interested in math and physics and uh finally realized it's time to do something about it so three years ago i when quantum was coming out it was a good uh it very very much appealed to me because it intersects both those fields and plus it's a lot of philosophy too which is what i majored in when 50 years ago when i was in college uh which is probably uh twice the time that the average age of the person on this conversation is um anyway um so i've been doing a lot of studying and self-studying and it's coming along well and i put together a site called quantum curious and i'm hoping to show you at the end for five minutes it's very good for beginners and get some feedback from you guys about it but i very much like these sessions the interactive part yeah thanks nathan yeah nathan's website is amazing i would uh appreciate i would um highly suggest checking that out if you want to learn more about quantum computing um hello hi so this is xi jin jian i am an associate professor in physics at uc merced so the new campus of the uc system and i i work on theoretical atomic and molecular physics but i'm also interested in say applications in quantum computation so button see everyone cool nice to meet you um logan i have i see your video is on now do you want to say a few words about yourself yeah sure i'm logan i'm a freshman and i'm prospective math cs major but i'm really interested in quantum algorithms especially like uh applications to cryptography great great um let's see i'm trying to find people who have already their videos on um alexey is driving he's gonna when he has a better connection he will uh join us oh brett nice to see you again if you want to say a few breadth coins if you want to say a few words about yourself as well we were here before hello my name is rick coons i'm from missouri in the midwest i did a talk a couple months back with you all and yeah i think it's just a really interesting up and coming field in general um chris cell so it's our tradition to cold call people uh i'm i'm sorry if you don't want to like introduce yourself that's fine but i'm just going to keep on cold calling who has their videos off yeah yeah uh hi everyone uh i'm a post-doc in mingxix group at imperial i work on variational quantum algorithms quantum simulation trying to run quantum algorithms on on real ibm hardware all right thanks chris um tobias thomas howe hi uh i'm tobias so i'm uh sorry okay i'm tobias i'm a postdoc at ninja group before i was a pc student in singapore as well as uh i used to work at entropical labs the quantum computing startup so pretty much i work on anything related to quantum computing recently if some queries was about a review on on this quantum computing which may be interesting for you yes it's interesting to all of us i think who are in the session um thank you but um i can't know of [Music] this quantum initiative because of the coding school which is that i'm doing a sort of like a course or an introduction to quantum computing and i'm particularly interested in quantum cryptography and quantum communication and i'm probably going to start with the internship or like a research program after august so that's why and i'm from bangalore india yeah amazing it's kind of late there i guess um or early i'm so confused 9pm okay it's okay uh math my namesake martha hello everyone i am from charity i am currently the undergrad student at eskeros manga university my department is mathematics and computer science also i'm associated with a community on turkey his name churchill [Music] who works on quantum computation so um also that's okay i think yeah thanks for sharing my cue turkey we've been working closely with them q world actually is the larger organization so they have a lot of uh branches around the world um zebo zebo yang uh hi uh i'm a graduate student in uc berkeley and my advisor is brigitte whaley i'm just have a general interest in all like branches of quantum computing and my advisors forwarded your like email and i yeah i'm curious to see what's in finance yeah yeah so stanford quantum has been partnering a lot uh with the berkeley quantum computing club i don't know if you're involved with them but they're great yeah i've been to several of their like talks yeah they're doing great good things yeah yeah when i was at stanford we organized the chess tournament and berkeley was the berkeley finalist was against the stanford finalists for the final game and uh berkeley won unfortunately and there's another one coming up i think josh we could talk about that after the meeting um so co uh cody cody go again hi i'm a student at uh university of chicago and i'm just interested in learning more about quantum computing and like its application in finance thank you um augusto que alberts oh hi how are you oh i'm oh nice to see you again uh sorry i'm just bringing you around yeah yeah yeah yeah i attended a few meetings last year i was i missed the once on the start of this year but i'm located on buenos aires argentina i did a master on physics but now i work as a softworking engineer in a finance risk related company so i'm trying to stay up with the quantum things a little bit by checking the meetings a few times but i'm mainly doing that for now thanks for joining um i think sarah has low bandwidth issues that's what i read on the chat yeah yeah she's a mit junior year and she her major is math and cs okay um same with a b you can so he is short in choice he's in biomedicine or she is in biomedicine or yeah i can't understand a lot from the text i have intro sorry it's very small i've taken interactive course in quantum computing here in helsinki i am very keen to learn more about different fields in quantum computing application side where it's likely to find an application the foreseeable future and very happy to be here what you say okay um great um s chen we already go through yeah sorry yeah um yeah um yeah i'm um i'm a vc venture capital i just want to learn a little bit about this thing thanks yeah thank you um we have ali ali lopez here as well okay um david nissi hey um i'm david niece and i'm a freshman at stanford i'm a prospective major in physics and philosophy philosophy is actually almost kind of my primary interest but i really like physics and i also like finance and this seemed like a kind of a cool little intersection so i'm mostly here to just listen amazing lots of people from stanford today um william wang okay and then lastly vasily oh hello uh i'm a soft engineer from los angeles uh just interested in translate of quantum walls and yeah just stay in tune okay so um now that everyone had their uh intros we can go ahead and start but please uh chip in at any time uh if you have a question shout out again we're very informal let's let's keep this as a discussion please and uh yeah so we can go ahead with uh dimitri thanks for waiting richard okay yeah uh and i want to start my presentation with a brief description of our organization that's the russian quantum center uh rqc rqc is a private research organization in russia that's specialized in fundamental research in quantum physics uh currently there are 17 scientific groups that cover i think uh all areas of quantum physics uh since we are a non-state company we need to look for a private investment and launch our own commercial projects this now the russian quantum center has launched eight spin-off companies engaged in developing communitization devices and technologies designed in their rpc scientific groups more detailed information about our center you can find in our site and okay let's go further and um currently our scientific group investigations are mainly focused on the problems that uh their diabetic canadian approach can solve uh and uh let's talk about the quantum annealing and about the practical tasks that this idea can solve uh what is the meaning of the animation process adiabatic canadian is a general approach for solving optimization problems for from a white class uh in the process of unique we optimize the sum cost function and the most famous example where um i mean is used as a problem often constrained binary optimization quadratic and construct binary optimization with code which called cube here we want to optimize the polynomial function on the binary sequence of zeros and ones but in physical devices we operate not with zeros and ones we operate with spins and the values of this pins is plus or minus one therefore we should reformulate reformulate our initial problem in the isaac model formalist and finally optimize the eisen energy yeah uh please note that uh our quantum magnitude is different from the anime for example metalloroot metallurgy in metallurgy people used it for stress releasing some materials and from organization for better mechanical properties okay a little bit further the most famous commercial company that realizes the process of quantum annealing is the wave systems their device is separated with superconducting loops so that play the role of quantum qubits and then process in the wave is described by the following hamiltonian you can see that this hemitone and the sum of two terms are the initial hamiltonian and the final hemitonium let's consider the first term and the lowest energy state of this initial hamiltonian is when all cubits are in a superposition state of zeros and ones this term is also called the tunneling hamiltonian uh and if we consider the second term uh the final hamiltonian the lowest energy state or the final hamiltonian is the answer of the problem that we are trying to solve uh the final state is a classical state and includes the cubic biases and the couplings between qubits this term is also called the problem in quantum annealing the system begins in the lowest energy state uh hygiene state of the initial hamiltonian and acetones introduces the problem to melatonin which contains the biases and coppers and it reduces the influence of the initial hamiltonian you can see it from this graph we reduce the coefficient a before the initial hamiltonian and increase the efficiency as a result uh at the end of the avenue it is the system state it is the ibm state or the problem okay um okay let's talk about the practical application so then you can uh many practical tasks in real life can be transformed the eyes and model formalities it is for example combinatorial optimization tasks traveling salesman problems portfolio optimization protein folding uh investigation of the energy structure of molecules etc in general such problems are mp hard the result problems with large sizes cannot be solved by classical determined approaches in satisfactory time that means in large charges we face with the effect of combinatorial explosion uh but in practice we almost every time uh face to work with large problems and we want to have a possibility to obtain good enough solutions for these problems uh and the new generation of special purpose devices like d wave will help us to get such answers uh and the special proposed device that we investigate in our group it's based on optics and code coherence coherence machine today we will not precisely describe the device technical aspects but let's try to understand the main idea of this device briefly uh the main element of coherentizing machine is a non-linear crystal where the process of parametric down conversion could be realized uh we use the pump you can see here and if we are working the regime when we work below the oscillation threshold uh we can generate the squeeze state of the light in our millennium crystal uh but if we want to walk above the oscillation threshold we can generate the incoherent mixture of two components with the phase zero and five for example yeah um and what is the structure of the real humanizing machine the real caffeine tyson's machine proposed by nagaki at all this non-linear amplifier squeezer is placed inside the fiber loop this moment is find the cheaper laser yeah okay inside the fiber loop um and we measure yeah and inside this loop the sequence of light pulses circulates circulate we measure the quadrature x i of each pulse exciting the amplifier and then apply the displacement operator on amplitude of amplitude to each g pulse yeah uh which entering the amplifier then we start with all processing the welcome state and repeat this procedure for 100 or 1000 loops and finally the results in amplified pulse sequence will have phases corresponding to the minimum of the eisenhorn uh and uh the question why why we have uh the phases that that corresponds to the ground state the answer is the following uh we can see that the displacement temperature that we applied to our uh our pulses um play place the role of the gradient for this hamiltonian uh and as a result our displacement pushes the spin towards energy minimum pushes the phases uh let me note that hear spins approximated by a continuous variable because we work with the laser pulses not with pins okay um and now we come to the next part of my presentation devoted to the quantum inspired algorithm sim sim because it's the simulation of a hearing machine uh this algorithm we developed in a russian quantum center and it simulates the process of optimization incoherent in real computing machine but on the classical computer moreover we showed that it is possible to achieve the same performance in terms of computational time as a real device by using a gpu yeah after the detailed investigations of fiber coherentizing machine that we [Music] saw in previous slides we understand that quantum effects do not play significant control in the optimization process as a result we can neglect such a quantum nature of the device and realize the classical optimization algorithm on the computer in our consideration we treat each amplitude in our in our continentizing machine as a real value and we place two photon losses by linear construct constraint you can see it here and finally our revolution simplifies and have the following form that you can see here just a simple just a simple step of our algorith algorithm uh then we benchmark our algorithm on different breaths on different different tasks uh and if you want uh if you if you want you can find the results in our paper okay then then we apply then then we apply our algorithm two different tasks like protein folding like quantum chemistry tasks uh and but we find that the most important and prospective task is portfolio optimization um okay to solve this problem we use the following scheme uh we take the historical date about asset prices and use the equally weighted markets model uh to transform this data into the cuba problem then we convert our data into the eisenhaten formation and start to optimize by our quantum spike algorithm system let me know that we fix the number of assets in the portfolio as m it's a number of assets in our portfolio and by using the penalty parameter lambda and also arrived parameter alpha to investigate the effective portfolio frontier which is uh one of the most important part of the markets optimization to find the optimal portfolio by uh investigation there the optimal effective frontier yeah and uh unfortunately due to non-disclosure agreement with our colleagues from central credit bank i can't provide a very precise and very detailed information about our strategy but i can show the performance and we developed with our colleagues strategy based on the application of sim sim algorithm enhanced by machine learning techniques uh and uh yes in such project we we collaborate with our colleagues from central credit bank supporting us and advising about strategy improvement from the financial point of view and we analyzed our strategy over the 25 years period of time and finally investigated that it shows excellent performance in terms of annual returns uh yeah our strategy is the first in the first strategy here and here and we investigate that our model is very good in terms of phenol returns in terms of sharp creation and in terms of maximum drawdown uh our analysis shows that this sharp range or the developed strategy is comparable with the sharp ratio paid by the resample efficient frontier technique with a good classical approach to that used in this area uh still the average channel returns in our model are much higher than uh in resample division frontier you can see from the first graph if compare the first and the third column and moreover our strategy has the best performance in terms of the maximum drawdowns it means that in all periods of 25 years we in one time lost only uh about 35 percent of our capital in one time but all other strategies a lot more yeah what's your time frame for that is that a yearly loss or a monthly loss for the drawdown for drawdown no we consider the maximum draw down through all 25 years period okay okay perfect in one month yeah but yeah but we but we buy and sell until the the assets every uh one month every month yeah okay but consider all the period of 25 years and i think that's the result of crisis in 2008. yes i remember that's that's okay yeah uh and yes in this slide you can see the monthly and final distributions of returns uh here and here there's a heat heat color map a heat map and here you can see the annual return distribution per year yeah and you can see that the the most crucial crisis was in 2008 uh and in this graph you can see uh distribution the histogram of monthly returns distribution uh and uh distribution relative to the s p uh 500 uh equal weight yeah and we can see that because we work in the equal weighted model we yes we use equal weights for all companies in our portfolio and as a result we should compare it with the equal weighted models it's it's more more precise i think and you can see that we also better in mean value and generally better than the xmp yeah and i think that's that's all for for our our research in portfolio optimization and if you have any question i can i will hate to answer them i have a quick one uh dimitri uh nathan here maybe you mentioned it or i missed it so on this graph you're showing now how is this different or more beneficial to your uh clients than the classical approach they're using now to address some of these issues yeah yeah we we find that our approach have a very huge returns in comparison with the classical for example technique like marquis or the sample efficient frontier that's why it's good to to use our our optimization strategy if you want to have a high returns uh with comparable volatility uh you can use our model but yeah so on this graph now how can i confirm that by looking at this graph these are your results they're not comparing to classical results if you understand my question uh what you mean uh in classical results i mean if they didn't have this approach they'd have certain results and you're making this interesting claim that your approach is an improvement how much of an improvement or uh yeah the main the main idea why we can use this this algorithm and why it's benefit for us because uh we work uh the computational time of our algorithm is very small it's about two milliseconds per one optimization form and as a result we can test a lot of strategies in a very short time period and then we can understand what what is the best parameters for forever model and what is the worst parameters and take the better the better one uh we in comparison with classical approaches we uh you mean the classical approaches from finance so you mean classical approaches for for uh binary optimization algorithm in general just finance so you're saying this took i i didn't quite hear you uh two milliseconds or something that i'd affect how long would it have taken if one used conventional techniques maybe maybe actually i think it strongly depends on the algorithm that you use uh we compare our results with agarobi performance and groupings comparable with our approach and i think sometimes it's better okay thanks yeah that's awesome thank you dimitri um thank you for your talk for me to understand what you're doing can you go back to the mapping part so you had a slide to show how you use your computer so uh is it the next one um so there was a price and then there was some model um is it this or the next one yeah that's the correlated model and the first term is the covariance part of our quadratic form the second part is corresponds to the returns of our model yeah and the last one is so what is q on the what what is q so is it q is given q here is uh is the binary binary variable which have only two can have only two values zero or one and we want to understand uh yeah uh what the companies we should to buy it means that we have the one as a result value of the variable for this for this company or uh or zero if we don't buy the the company yes and this just weights just the weights of the company in our in our portfolio yeah if i'm not mistaken dimitri this is in terms of physics this is uh very similar to an ising model where the um these are these these are the spins right and then yeah zero is actually like a negative one in terms of analyzing model and a down spin and that one is an up spin and why this spectacle is that you convert anizing model into a portfolio optimization problem and the spins tell you either to buy or not buy the stocks yeah absolutely yeah yeah that's right but then it's still not very clear to me how how this helps your portfolio uh because can you show the next slide or there was another slide um not this one yes no before then i think it was the one before with the the annealing model symmetry the one before i think it was even before simpson i think it's fine um yeah probably it's my mistake that i don't remember which slide it was so you uh you're mapping your portfolio into the icing model but i still share the view i mean share the uh same question as nathan um i don't see how quantum computer helps you because it i mean do you use like coherence properties at all or okay and the answer the following we work with the discrete variables and as a result is a problem also in pi heart and if we will work with a huge with a very large universe when we try to optimize not uh for example 500 companies by 500 000 companies uh we should use the the devices like the wave or coherent isaac machine because the classical approach is not very effective and the effective for not a very large large quadratic terms and as a result this quantum annealing uh increase the time and increase the quality of the final solution that we can obtain here we here we consider only the 500 spin models because we're working with the s p 500 universe but practically we can extend it into large sizes and for large sizes the quantum quantum devices quantum adiabatic uh animal devices like the wave will be very useful yeah now we just in the first step uh on the investigations of the performance of this quantum adiabatic annealing devices in in different different problems and one of these problems is a portfolio optimization and we find that the humanitized machine will very effective in this in this problem so yes okay i have a question also have you tested this sort of model in uh maybe less bullish financial environments like i mean there's sure there was like the 2008 crash and then the one in last year um but there was there's been a pretty steady uptrend since like the 70s in u.s markets um and i'm wondering if this the same sort of thing could be applied to something like the the nikkei uh index where a japanese mark is just kind of like going up and down and up and down over the last uh however many decades yeah we consider the the period from 1995 here to 2021. yeah that's the period but we test only on the usa market and only for the s p 500 market s p 500 universe yeah i that's an interesting question logan um actually i've been thinking about this as well uh because as we saw there's a drawdown in 2008 as expected and it would be interesting to look at a model where shorting is allowed so that you can also have negative weights and it's very hard to do it in this setting because we only have two um two spins that we can use it's either up or down which means you either bias buy or buy a stock or not buy a stock and there are techniques around that maybe you limit yourself to either only buying and shorting that would allow you to maybe be more hedged against uh against a more volatile market um there are other techniques to increase your weights but the the thing here is that we're working with np-hard problems which are discrete uh discrete weights and the isaac model inherently allows for two selections uh but that's a very good question i think dimitri that's a very interesting um test to do maybe test your model at a more volatile market or even a bearish market yes yes okay thank you so have you looked at any shorting um models at all committee what do you mean in time period shorting i mean like maybe negative negative weights only make for example only only short uh portfolio no no no no as i told we just start this project and unfortunately we haven't got enough people and time to test all all possible configurations and tasks yes i mean it's a fascinating uh area to look into i think because there's so much data available in the financial markets and it's very well tracked because people care about money so um so there's a lot of data to work with and here there's there's a distinction that we have to make as well so right now we work we dmitry is describing a a a solution inspired from quantum uh a quantum device but working on a classical computer uh so maybe we could talk about a bit about the korean icing machines themselves the hardware and when you think a korean icing machine would be not scalable but even like allowing for the test of of an algorithm like this yeah if you write about humanitizing machines these devices very scalable because these the our japanese colleagues use fast fpj it means that create the displacement by the electronic device and as i remember now they they scale their machine up to the 20 to 20 000 of variables and it's it's a very huge device also also as you remember they compare the performance of coherent types of machine with the uh g-wave the wave 2000 q and show that sim uh significantly uh outperformed the wave in the dance graph but the performance is comparable uh on the sparse sparse matrices um another another good point for for sim that they can realize fully connected graphs and although they can connect all variables inside the this machine but in the wave we know that we can use for 2000 q or we can solve only problems with the size of sixty four seven member cube it's not not larger yeah and uh this is another performance i think another advantage the officer that they they can scale and solve fully dense fully connected np heart fuel problems perfect uh yeah i i work with dva machines a bit and um it is very important to like it's very impressive that these machines have all told connectivity because the ua machines have constrained connectivity for example a 500 variable problem becomes a 1600 1700 qubit problem in the actual machine because you have to embed it so all to all connectivity is very important for uh for financial applications yes yes so do we have any other technical questions otherwise i want to take the discussion to a different different level but this is a fascinating topic so please shoot if you have any uh i've just got a small question i so forgive me i i don't really have uh too great of a knowledge in finance or physics so if this question was already answered uh sorry for that but to my understanding um the markovitz model is um part of modern portfolio theory which is which is one way of sort of doing um asset or sorry portfolio optimization problems but um i'm wondering how generalizable this solution is so for example like it seems like the way this works now is that you've discovered some similarities between the sizing model and the markovic's model but people have you know some complaints about the markovic's model and how it evaluates risk you know for example so if somebody were to create a new model would you still be able to use the same techniques here in order to take that classical model and put it into a quantum system where you have more computational power or is this like does this only work in response to the marketers model ah yeah of course we started this project because we plan we also develop the coherentizing machine in russian quantum center and we plan further use uh use this devices that we develop we will develop practical tasks and initially this this consideration was devoted to mapping of data to to uh quantum and quantum inspired or quantum real quantum device uh but uh here uh we use the classical computer optimization and i think that this research mostly related the performance of coherentizing machine to solve the the financial problems here we consider the marquis model but of course we collaborate with our colleagues from banks and they give an advice is how we can uh transform our model to more to more appropriate uh way uh and to to improve how we can improve our model from the financial point of view and how we can uh how can change change the model yeah and i i think that's partially about the marquis model but partially about the usage of uh quantum and quantum inspired algorithms on this class of problems all right thanks yeah i i think that's also very valid question because yeah yeah yeah i'm sorry just maybe let me know please that this results it's not only the classical markovic's approach we also use some some some techniques [Music] that we that we get from our our financial colleagues but unfortunately i cannot tell about this this yeah this uh features now maybe maybe we publish a paper maybe we won't do this unfortunately i don't know but now i have a non-disclosure agreement yeah yeah so yeah i mean david this is a uh it did so the microsoft came up in like the 50s i think so it's a very old model it's based on a highly optimistic assumption that um hey according to capital asset pricing model um the prices of the assets in a in an efficient market are the actual prices so um but we we've seen over the years that that doesn't really work in practice so but it is a very good base for approximating um prices and actually not appropriating prices but maximizing returns and minimizing risk in some level if if you have other parameters like dimitri is using and not able to disclose um and it is very lucky that it it fits the ising model very well very well um so that's why people in quantum research are actually focusing it on focusing on it and um using it as a base to optimize portfolios but there's always another term usually that's used in practice it's either a penalty term a concentration turn something that actually makes the um makes the model fit into the real world data and uh following that dimitri so the way you use the the markups part of your model the way you calculate returns and risks that's how that's based on historical data and that's purely deterministic right you don't use any uh risk models that actually um broadcast into the forecast into the future you you create your covariance matrix and everything through the historical data right yes yes okay so again yeah this is like a pure marker it's in that side then but um there are very many more techniques and to have this returns is a these returns so early in the in the modeling part is very uh i think impressive okay any more questions for the technical side i think we can generally discuss about the uh the usage of quantum computing and financial area maybe maybe you can find some another interesting tasks not only portfolio optimization yeah yeah there are many many tasks um i think that people are trying to do um there is the recall rerouting problem is very famous and uh inventory management also famous so optimization problems are all over the industry which brings us back to our topic where research and industry meet so i think this is a very exciting field because in quantum computing there has to be a collaboration between industry and in academia it is a very capital heavy uh research area because it's very expensive to build quantum computers and it's also very beneficial for the industry to have these very powerful machines theoretically powerful machines that are proposed to have real-world use cases as we see with dimitri here so maybe do it you can talk about your experience as a researcher how how you communicate with people from the industry how your role shifts throughout the day from a person who's doing pure research with academics and then a person who's talking to finance financial advisors and so how is that uh how's that for you in a day-to-day basis uh yeah okay now uh now the quantum technologist is very very important and very popular from the industry and i think the in the last two years that's not so complicated to talk with their people from financial sector of or for example oil engineering companies something else the main problem i think is to find the same language with these guys because we as a scientist we talk about the high materials and high matters and [Music] sometimes it's very sometimes it's very complicated so to tell your ideas to the guys from the financial sector for example from the banks and you should to understand how you can change your language when you communicate with these people and and then it will be success i think this will be successful um what are you what do people think so most of us here i think are part of uh university or academia but if anyone is just in more in the industry side can can someone talk about that experience i think i can shut down the presentation yes well i guess i can talk about my experience but i quit like i i've been in industry for the last few months and um my role is very research based so um it's been like this since the last two companies i've been in uh but um from what i see in in in my organizations is that um organizations like this quantum conversations or stanford computing association where we don't have this division between industry or university like anymore um everyone is interested in learning more or in quantum computing because we are all students it's a field that changes every day every day there's a new idea and another idea gets demolished so it's very fast um so everyone is listening to each other and it's a great field that brings all of these different parts we have national labs we have universities and we have people who have other motivations for their own organization like increasing profits but we have we are all um combined with the same goal which is advancing the field because it serves everyone that's why we see everyone coming together from even different parts of the world so right now we have people from all around the world even in this talk right now um and i think it's very exciting that we are all part of it and uh and and even we have like different internal motivations as industrialists or uh academics we as students we we i think are joined towards the same goal you know uh even more to reinforce what you're saying even over the last year uh there's been amazing changes uh globally in quantum for instance uh a company went public ion q listed on nisa you know a very uh highly regarded exchange and uh you have to jump through a lot of hoops to get listed like that so they must be doing a lot of things right uh and and companies are getting funded for hundreds hundreds of millions of dollars like psy quantum i think got over 300 million dollars now you know vcs obviously are far from uh stupid so they're not going to give out that kind of money and unless there was some realistic chance there was going to be a payoff so not just from the science point of view but from the uh marketplace point of view there's a lot of attention coming in now so it's a good time to be involved yeah and i even near me so i'm based in right around near cleveland ohio right now um since stanford's virtual and i heard that the cleveland clinic was actually going to be one of the first or the first like commercial um customer for ibm's quantum computers so i mean it's it's actually expanding into use now basically i mean if i was the average the age of the average person on here it's a great move to be involved because it really bodes well for your future i think in three four years it's going to be an amazingly ongoing sector also it's super interesting i mean it's fun to be in this ah that too it kept kept my sanity during kovic i would have gone berserk if i didn't have something to focus on uh hi nathan alexey here uh great to hear you i i just want to say i think you're so you know passionate and vigorous that any young person can can be jealous of that so you know i think you set a great example uh for all of us here and it's it's you know it's great basically i think um logan mentioned ibm right so i'm at ibm quantum now uh we see a lot of interest right a lot of different clients i mean i can talk cannot talk about specific lines but we do have clients in finance uh exploring similar approaches to what um uh we heard in the first talk right so so definitely uh what i can add from um industry perspective right a lot of work is now in exploration so industry has i think like primary kind of player which we see is a large company uh such as a large you know multinational bank or global oil and gas exploration company right which basically has an r d arm which uh we just asked with um research in quantum computing as an expansion right of high performance computing ai so i would say there is a lot of very cool uh work where you know it's basically like installation of the lab right what merc says it's kind of it's kind of you know you're an industry but you're in a research group right so you read papers you work with smart people and you explore this so i think it's the best of the both worlds because you're kind of paid industry wages right and and uh kind of global companies provide huge opportunities and and interconnections uh and uh at the same time you're working this cutting-edge problem another very interesting phenomena i realized is happening uh i i've seen the simon's institute uh talk by euban tong and uh she presented very interesting work uh so she she kind of dequantized a bunch of algorithms right so quantum algorithms pose the question of quantum advantage however what might happen is that people start looking at classical and then they start being inspired by quantum math to actually implement a classical algorithm which will be faster than the state of the art and so she actually kind of i think there is like this movement now to demonetize a bunch of things and make classical faster right i don't think it takes away from from quantum computing but it it shows that all this math uh deep dive has impact on a lot of different areas i would say kind of the best of the both verses that you get to kind of learn or relearn all this math which is very interesting and then you can kind of expand your mind and apply to different things so that's kind of my current feedback on the industry connection yeah thanks alexey as i said alexis started this whole organization and um he's right now at ibm so great perspective on from his side he's been dealing with academics and people from industry for a long time um but yeah definitely agree it's it's fruitful to be in this field in any in every perspective so i'm glad that we i see that in our all of our sessions i see a a very diverse crowd so um thanks dimitri uh for leading today um for our next event i think we will keep everyone posted um thanks all for joining i'm gonna stop the recording now if you have anything that you want to discuss we can keep this going but otherwise thanks all all for coming thank you so much thanks guys