Guillaume Verdon

Yes, it is full stack. And so we're folks that have built differentiable programming into the quantum computing ecosystem with TensorFlow Quantum. One of my co-founders of TensorFlow Quantum is the CTO, Trevor McCourt. We have some of the best quantum computer architects, those that have designed IBM's and AWS's systems.

Yes, it is full stack. And so we're folks that have built differentiable programming into the quantum computing ecosystem with TensorFlow Quantum. One of my co-founders of TensorFlow Quantum is the CTO, Trevor McCourt. We have some of the best quantum computer architects, those that have designed IBM's and AWS's systems.

Yes, it is full stack. And so we're folks that have built differentiable programming into the quantum computing ecosystem with TensorFlow Quantum. One of my co-founders of TensorFlow Quantum is the CTO, Trevor McCourt. We have some of the best quantum computer architects, those that have designed IBM's and AWS's systems.

They've left quantum computing to help us build what we call actually a thermodynamic computer.

They've left quantum computing to help us build what we call actually a thermodynamic computer.

They've left quantum computing to help us build what we call actually a thermodynamic computer.

Right. I mean, that was a challenge to build, to invent, to build, and then to get to run on the real devices. Can you actually speak to what it is? Yeah. So TensorFlow Quantum was an attempt at, well, I mean, I guess we succeeded at combining deep learning or differentiable classical programming with quantum computing and turn quantum computing into...

Right. I mean, that was a challenge to build, to invent, to build, and then to get to run on the real devices. Can you actually speak to what it is? Yeah. So TensorFlow Quantum was an attempt at, well, I mean, I guess we succeeded at combining deep learning or differentiable classical programming with quantum computing and turn quantum computing into...

Right. I mean, that was a challenge to build, to invent, to build, and then to get to run on the real devices. Can you actually speak to what it is? Yeah. So TensorFlow Quantum was an attempt at, well, I mean, I guess we succeeded at combining deep learning or differentiable classical programming with quantum computing and turn quantum computing into...

or have types of programs that are differentiable in quantum computing. And Andrej Karpathy calls differentiable programming software 2.0. It's like gradient descent is a better programmer than you. The idea was that in the early days of quantum computing, you can only run short quantum programs. Which quantum programs should you run? Well, just let gradient descent find those programs instead.

or have types of programs that are differentiable in quantum computing. And Andrej Karpathy calls differentiable programming software 2.0. It's like gradient descent is a better programmer than you. The idea was that in the early days of quantum computing, you can only run short quantum programs. Which quantum programs should you run? Well, just let gradient descent find those programs instead.

or have types of programs that are differentiable in quantum computing. And Andrej Karpathy calls differentiable programming software 2.0. It's like gradient descent is a better programmer than you. The idea was that in the early days of quantum computing, you can only run short quantum programs. Which quantum programs should you run? Well, just let gradient descent find those programs instead.

We built the first infrastructure uh, to not only run differentiable quantum programs, but combine them as part of broader deep learning, uh, graphs, uh, incorporating deep neural networks, you know, the ones you know and love with what are called quantum neural networks. Um, and, uh, Ultimately, it was a very cross-disciplinary effort.

We built the first infrastructure uh, to not only run differentiable quantum programs, but combine them as part of broader deep learning, uh, graphs, uh, incorporating deep neural networks, you know, the ones you know and love with what are called quantum neural networks. Um, and, uh, Ultimately, it was a very cross-disciplinary effort.

We built the first infrastructure uh, to not only run differentiable quantum programs, but combine them as part of broader deep learning, uh, graphs, uh, incorporating deep neural networks, you know, the ones you know and love with what are called quantum neural networks. Um, and, uh, Ultimately, it was a very cross-disciplinary effort.

We had to invent all sorts of ways to differentiate, to back-propagate through the hybrid graph. But ultimately, it taught me that the way to program matter and to program physics is by differentiating through control parameters. If you have parameters that affect the physics of the system, and you can evaluate some loss function, you can optimize...

We had to invent all sorts of ways to differentiate, to back-propagate through the hybrid graph. But ultimately, it taught me that the way to program matter and to program physics is by differentiating through control parameters. If you have parameters that affect the physics of the system, and you can evaluate some loss function, you can optimize...

We had to invent all sorts of ways to differentiate, to back-propagate through the hybrid graph. But ultimately, it taught me that the way to program matter and to program physics is by differentiating through control parameters. If you have parameters that affect the physics of the system, and you can evaluate some loss function, you can optimize...

the system to accomplish a task, whatever that task may be. And that's a very sort of universal meta framework for how to program physics-based computers.

the system to accomplish a task, whatever that task may be. And that's a very sort of universal meta framework for how to program physics-based computers.