Lightning Talk: Simulating Quantum Systems with PyTorch
summary
PyTorch-powered dynamiqs (github.com/dynamiqs/dynamiqs) provides GPU-accelerated, differentiable solvers that implement the Schrödinger Equation, Lindblad Master Equation and Stochastic Master Equation for closed, open and continuously measured quantum systems. It computes gradients via PyTorch autograd or a constant‑memory adjoint method (like the Neural ODE adjoint), handles batched simulations for speed, and is developed by physics PhD students at Alice & Bob. Requests: native complex number support, sparse matrices, and a direct matrix‑exponential‑times‑vector routine.
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chapters (6)
- 0:00The need for quantum computing
- 1:47Challenges in quantum simulation
- 4:14Quantum hardware technologies
- 5:41Pytorch for quantum physics
- 8:10Introducing Dynamics library
- 9:35Q&A and technical discussion
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