Physicist explains quantum computers | Guillaume Verdon and Lex Fridman

when you were talking about intelligence you mentioned multipartite quantum entanglement so high level question first is what do you think is intelligence when you think about quantum mechanical systems and You observe some kind of computation happening in them what do you think is intelligent about the kind of computation the universe is able to do a small small inkling of which is the kind of computation the human brain is able to do I I would say like intelligence and computation aren't quite the same thing I think that the universe is very much you know doing a a Quantum computation if you had access to all the degrees of freedom you could and a very very very large quantum computer with many many many cubits uh let's say a few cubits per uh plank volume right um which is more or less the pixels we have uh then you you'd be able to simulate the whole universe right uh on a on a sufficiently large quantum computer assuming you're looking at a finite volume of course of the Universe um I think that at least to me intelligence is the you know I go back to cybernetics right the ability to perceive predict and control our world but really it's nowadays it seems like a lot of intelligence um we use is more about compression right it's about um it's about operationalizing information Theory right in information Theory you have the notion of entropy of a distribution or a system and entropy tells you that you need this many bits uh to encode this distribution or this subsystem if you had the most optimal code and AI at least the way we you we do it today for llms and for Quantum uh is is very much Trying to minimize uh relative entropy between our models of the world and the world distributions from the world and so we're learning we're searching over the space of computations to process the world to find that compressed representation that has distilled all the variance and noise and entropy right um and originally I I came to Quantum machine learning from the study of black holes because the entropy of black holes is very interesting in a sense they're physically the most dense objects in the universe you can't pack more information spatially any more densely than in black hole and so I was wondering how do black holes actually encode information what is their compression code and so that got me into the space of algorithms to search over space of quantum uh codes um and uh it got me actually into also how do you acquire Quantum information the from the world right so something I've worked on uh this is public now is quantum analog digital conversion so how do you capture information from The Real World in superposition and not destroy the superp position but digitize for a quantum mechanical computer uh information from The Real World um and so if you have an ability to capture Quantum information and search over learned representations of it now you can learn compressed representations that may have some useful information in there latent representation right um and I think that many of the problems facing our civilization are actually Beyond this this complexity barrier right I mean the greenhouse effect is a quantum mechanical effect right chemistry is quantum mechanical um you know Nuclear Physics is quantum mechanical a lot of biology and and and and protein folding and so on is affected by quantum mechanics and so unlocking an ability to augment human intellect with quantum mechanical computers and quantum mechanical AI seemed to me like a fundamental capability for civilization that we we needed to develop um so I spent several years doing that um but over time I kind of grew weary of the the timelines that we starting to look like nuclear fusion so one highle question I can ask is Maybe by way of definition by way of explanation what is a quantum computer and what is uh Quantum machine learning so a quantum computer really is a quantum mechanical system over which we have sufficient control and it can maintain its quantum mechanical State and quantum mechanics is how nature behaves at the very small scales when things are very small or very cold and it's actually more fundamental than probability Theory so we're used to things being this or that uh but we're not used to thinking in superpositions because uh while our brains can't uh can't do that so we we have to translate the quantum mechanical world to say linear algebra to grocket unfortunately that translation is exponentially inefficient on average you have to represent things with very large matrices but really you can make a quantum computer out of many things right and we've seen all sorts of players you know from neutral atoms trapped ions super conducting metal um photons at different frequencies I think you can make a quantum computer out of many things but to to me the thing that was really interesting was both Quantum machine learning was about understand the quantum mechanical world with quantum computers so embedding the physical world into AI representations and quantum computer engineering was embedding AI algorithms into the physical world so this bidirectionality of embedding physical world into ai ai into the physical world the symbiosis between physics and AI really that's the sort of core of my quest really uh even to this day after Quantum Computing it's still in this sort of um journey to merge really physics and AI fundamentally so Quantum machine learning is a way to do machine learning on a uh representation of nature that is you know stays true to the quantum mechanical aspect of nature yeah it's learning quantum mechanical representations that would be Quantum deep learning um alternatively you can try to do classical machine learning on a quantum computer I wouldn't advise it because um you may have some speedups but very often the speedups come with huge costs using a quantum computer is very expensive why is that because you assume the computer is operating at zero temperature which no physical system in the universe can achieve that temperature so what you have to do is what I've been mentioning this quantum correction process which is really an algorithmic fridge right it's trying to pump entropy out of the system trying to get it closer to zero temperature and when you do the calculations of how many resources it would take to say do deep learning on a quantum computer classical deep learning uh it's there's just such a huge overhead it's not worth it it's like thinking about shipping something across a city using a rocket and going to orbit and back it doesn't make sense just use uh and you know delivery truck right what kind of stuff can you figure out can you predict can you understand with Quantum deep learning that you can't with deep learning so incorporating quantum mechanical systems into the into the learning process I think that's a great question I mean fundamentally it's any system that has sufficient uh quantum mechanical uh correlations that are very hard to capture for classical representations then there should be an advantage for a quantum mechanical representation over a purely classical one the question is which systems have sufficient correlations that are very Quantum uh but is also uh which systems are still relevant to Industry that's a big question you know people are leaning towards chemistry uh Nuclear Physics uh um I've worked on actually processing inputs from Quantum sensors right if you have a network of quantum sensors they've captured a quantum mechanical image of the world and how to post-process that that becomes a sort of quantum form of machine perception and so for example uh fery laab has a project exploring detecting dark matter with these Quantum sensors and to me uh that's in alignment with my quest to understand the universe ever since I was a child and so someday I hope that we can have very large networks of quantum sensors that help us um peer into the earliest parts of the the universe right for example the ligo is a Quantum sensor right it's just a very large one um so uh yeah I would say Quantum machine perception uh simulations right grocking Quantum simulations so similar to Alpha fold right Alpha fold understood the probability distribution over configurations of proteins you can understand Quantum distributions over configurations of electrons uh more efficiently with Quantum machine learning you co-authored a paper titled a universal training algorithm for Quantum deep learning uh that involves back prop with a Q very well done sir very well done how does it work is it is there some interesting aspects you can just mention uh on how kind of you know back propop and some of these things we know for classical machine learning transfer over to the the quantum machine learning yeah that was that was a that was a funky paper that was one of my first papers in in Quantum deep learning everybody was saying oh I think deep learning is going to be sped up by quantum computers and I was like well the best way to predict the future is to invent it so here's a 100 page paper have fun um essentially you you know Quantum Computing is usually you embed uh reversible operations into a Quantum computation and so the trick there was to do a feed forward operation and do what we call a phase kick but really it's just a force kick you just kick the system uh with a certain force that is you know proportional to your loss function that you you wish to optimize and then by performing uncomp computation you start with a superpositions over a superposition over parameters right which is pretty funky now you're not just you don't have just a point for parameters you have a superp position over many potential parameters right and our goal is is using face kicks somehow right to adjust parameters because phase kicks emulate uh having uh the parameter space be like a a particle in N dimensions and you're trying to get the shringer equation shringer Dynamics in the Lost landscape of the neural network right and so you do an algorithm to induce the space kick which you know involves a feed forward a kick and then when you uncomp compute the feed forward then all the errors and these phase kicks and these forces back propagate and hit each one of the parameters throughout the layers and if you alternate this with an emulation of kinetic energy then it's kind of like a particle moving in N dimensions a Quantum particle um and the advantage in principle would be that it can tunnel through the landscape um and find new Optima that would have been difficult for stochastic optimizers um but again this kind of a theoretical thing and in practice uh with at least the current architectures for quantum computers that we have planned uh you know such algorithms would be extremely expensive to run so maybe this is a good place to ask the difference between the different fields that you've had a tow in so mathematics physics PHS engineering and also you know entrepreneurship like the different layers of the stack I think a lot of the stuff you're talking about here is a little bit on the math side maybe physics almost working in theory what's the differen between math physics engineering and uh you know make making a product for Quantum Computing for Quantum machine learning yeah I mean you know some of the original team uh for the tensorflow quantum project which we started you know in school at University of water uh there was myself uh you know initially I was a a physicist apply matician mathematician we had a computer scientist uh we had mechanical engineer and then we had a physicist that was experimental primarily and so putting together teams that are very cross-disciplinary and figuring out how to communicate and and share knowledge is really the key to doing this sort of indis interdisciplinary uh engineering work um I mean there is there is a big uh difference you know in mathematics you can explore mathematics for mathematics sake in physics you're applying mathematics to understand uh the world around us uh and in engineer you're trying to you're trying to hack the world right you're trying to find how to apply the physics that I know my knowledge of the world to to to do things well in Quantum Computing in particular I think there's a just a lot of limits to engineering it just seems to be extremely hard yeah so there's a lot of value to be uh exploring Quantum Computing Quantum machine learning in the theory in with with with with math so I guess one question is why is it so hard to build a quantum computer what are uh what's your view of timelines in bringing these ideas to life right I I think that um you know an overall theme of my company is uh that we have folks that are uh you know there's a sort of Exodus from Quantum Computing and we're going to broader physics Bas dii that is not Quantum so that gives you a hint and um so we should say the name of your company is xtropicalovex temperature Subspace of information and the way to do that is by encoding information you encode a code within a code within a code within a code and so there's a lot of redundancy needed to do this error correction But ultimately it's a sort of um algorithmic refrigerator really it's just pumping out entropy out of the S the subsystem that is virtual and and delocalized that represents your quote unquote logical cubits aka the the payload Quantum bits in which you actually want to uh do run your quantum mechanical program it's very difficult because in order to scale up your quantum computer you need each component to be of sufficient quality for it to be worth it because if you try to do this error correction this Quantum error correction process in each Quantum bit and your control over them is if it's insufficient um uh it's not worth scaling up you're actually adding more errors than you remove and so there's this notion of a threshold where if your Quantum bits are of sufficient quality in terms of your control over them it's actually worth scaling up and actually in recent years people have been crossing the threshold and it's starting to be worth it and so it's just a very long slog of engineering But ultimately it's really crazy to me how much Exquisite level of control we have over these systems it's actually quite crazy uh and we're people are crossing you know they're achieving Milestones it's just you know in general the media always gets ahead right of where the technology is there's a bit too much hype it's good for fundraising but sometimes you know it causes Winters right it's the hype cycle I'm bullish on Quantum Computing on a 105 year time scale uh personally but I think there's other quests that can be done uh in the meantime I think it's in good hands right now

Channel: Lex Clips

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Keywords: ai, ai clips, ai podcast, ai podcast clips, artificial intelligence, artificial intelligence podcast, computer science, consciousness, deep learning, einstein, elon musk, engineering, friedman, guillaume verdon, joe rogan, lex ai, lex clips, lex fridman, lex fridman podcast, lex friedman, lex mit, lex podcast, machine learning, math, math podcast, mathematics, mit ai, philosophy, physics, physics podcast, science, tech, tech podcast, technology, turing

Id: vp5AG-V2dmE

Length: 18min 22sec (1102 seconds)

Published: Mon Jan 01 2024