Guillaume Verdon
speaker
342 appearances
1 recordings
1 series
first heard Dec 2023
last heard Dec 2023
Guillaume Verdon’s voice in public audio — every appearance, attributed to the second.
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So how do you capture information from the real world in superposition and not destroy the superposition but digitize for a quantum mechanical computer? information from the real world.
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 their latent representation, right? And I think that many of the problems facing our civilization are actually beyond this complexity barrier. I mean, the greenhouse effect is a quantum mechanical effect.
Chemistry is quantum mechanical. You know, nuclear physics is quantum mechanical. A lot of biology 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 needed to develop.
So I spent several years doing that. But over time, I kind of grew weary of the timelines that were starting to look like nuclear fusion.
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, but we're not used to thinking in superpositions because, well, our brains can't do that. So we have to translate the quantum mechanical world to, say, linear algebra to grok it. 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, superconducting, metal, photons at different frequencies, I think you can make a quantum computer out of many things.
But to me, the thing that was really interesting was both quantum machine learning was about understanding 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 the 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, even to this day after quantum computing. It's still in this sort of journey to merge really physics and AI fundamentally.
Yeah, it's learning quantum mechanical representations. That would be quantum deep learning. Alternatively, you can try to do classical machine learning on a quantum computer. I wouldn't advise it because 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 error 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, 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 a delivery truck, right?
I think that's a great question. I mean, fundamentally, it's any system that has sufficient quantum mechanical 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 but which systems are still relevant to industry, that's a big question. People are leaning towards chemistry, nuclear physics. I've worked on actually processing inputs from quantum sensors. 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. For example, Fermilab has a project exploring detecting dark matter with these quantum sensors. To me, 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 peer into the earliest parts of the universe. For example, the LIGO is a quantum sensor. It's just a very large one. So yeah, I would say quantum machine perception simulations, grokking quantum simulations, similar to AlphaFold. AlphaFold understood the probability distribution over configurations of proteins. You can understand
quantum distributions over configurations of electrons more efficiently with quantum machine learning.
Yeah, that was a funky paper. That was one of my first papers 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. Essentially, quantum computing is usually... you embed 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 the force kick. You just kick the system with a certain force that is proportional to your loss function that you wish to optimize. And then by performing uncomputation, You start with 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 superposition over many potential parameters, right? And our goal is to... Is using phase kicks somehow? Right. To adjust parameters? Because phase kicks emulate... having the parameter space be like a particle in n dimensions and you're trying to get the Schrodinger equation
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