Guillaume Verdon

Schrodinger dynamics in the loss landscape of the neural network, right? And so, you do an algorithm to induce this phase kick, which involves a feedforward, a kick, and then when you uncompute the feedforward, then all the errors in these phase kicks and these forces backpropagate and hit each one of the parameters throughout the layers.

Schrodinger dynamics in the loss landscape of the neural network, right? And so, you do an algorithm to induce this phase kick, which involves a feedforward, a kick, and then when you uncompute the feedforward, then all the errors in these phase kicks and these forces backpropagate and hit each one of the parameters throughout the layers.

Schrodinger dynamics in the loss landscape of the neural network, right? And so, you do an algorithm to induce this phase kick, which involves a feedforward, a kick, and then when you uncompute the feedforward, then all the errors in these phase kicks and these forces backpropagate 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. And the advantage in principle would be that it can tunnel through the landscape and find new optima that would have been difficult for stochastic optimizers.

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. And the advantage in principle would be that it can tunnel through the landscape and find new optima that would have been difficult for stochastic optimizers.

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. And the advantage in principle would be that it can tunnel through the landscape and find new optima that would have been difficult for stochastic optimizers.

But again, this is kind of a theoretical thing, and in practice, with at least the current architectures for quantum computers that we have planned, such algorithms would be extremely expensive to run.

But again, this is kind of a theoretical thing, and in practice, with at least the current architectures for quantum computers that we have planned, such algorithms would be extremely expensive to run.

But again, this is kind of a theoretical thing, and in practice, with at least the current architectures for quantum computers that we have planned, such algorithms would be extremely expensive to run.

Yeah, I mean, some of the original team for the TensorFlow Quantum Project, which we started in school at the University of Waterloo, there was myself. Initially, I was a physicist, a mathematician. We had a computer scientist. We had a mechanical engineer, and then we had a physicist that was experimental primarily. And so...

Yeah, I mean, some of the original team for the TensorFlow Quantum Project, which we started in school at the University of Waterloo, there was myself. Initially, I was a physicist, a mathematician. We had a computer scientist. We had a mechanical engineer, and then we had a physicist that was experimental primarily. And so...

Yeah, I mean, some of the original team for the TensorFlow Quantum Project, which we started in school at the University of Waterloo, there was myself. Initially, I was a physicist, a mathematician. We had a computer scientist. We had a 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 share knowledge is really the key to doing this sort of interdisciplinary engineering work. I mean, there is a big difference. In mathematics, you can explore mathematics for mathematics' sake. In physics, you're applying mathematics to understand the world around us.

putting together teams that are very cross-disciplinary and figuring out how to communicate and share knowledge is really the key to doing this sort of interdisciplinary engineering work. I mean, there is a big difference. In mathematics, you can explore mathematics for mathematics' sake. In physics, you're applying mathematics to understand the world around us.

putting together teams that are very cross-disciplinary and figuring out how to communicate and share knowledge is really the key to doing this sort of interdisciplinary engineering work. I mean, there is a big difference. In mathematics, you can explore mathematics for mathematics' sake. In physics, you're applying mathematics to understand the world around us.

And in engineering, 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 do things.

And in engineering, 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 do things.

And in engineering, 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 do things.

Right. I think that an overall theme of my company is that we have folks that are, you know, there's a sort of exodus from quantum computing and we're going to broader physics-based AI that is not quantum. So that gives you a hint. So we should say the name of your company is Extropic. Extropic, that's right.

Right. I think that an overall theme of my company is that we have folks that are, you know, there's a sort of exodus from quantum computing and we're going to broader physics-based AI that is not quantum. So that gives you a hint. So we should say the name of your company is Extropic. Extropic, that's right.