Guillaume Verdon

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.

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.

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.

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.

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?

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?

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.

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.

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?

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?

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

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

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.