Lightning Talk: Simulating Quantum Systems with PyTorch - Pierre Guilmin, Alice & Bob

Lightning Talk: Simulating Quantum Systems with PyTorch - Pierre Guilmin, Alice & Bob In this talk, I propose to explain why simulating quantum systems is a formidable challenge, and how leveraging modern hardware and software can result in notable performance improvement. PyTorch is ideally suited for this task, first because running solvers on GPUs results in a significant speed-up, and second because numerous tasks related to the calibration and control of quantum systems require the computation of gradients based on the time-evolved quantum state. The emerging research effort to develop quantum computers heavily relies on such tools. The dynamiqs library (https://github.com/dynamiqs/dynamiqs) is a Python library powered by PyTorch, designed to address this challenge. It provides differentiable solvers for the *Schrödinger Equation* which governs closed quantum systems, the *Lindblad Master Equation* for open quantum systems and the *Stochastic Master Equation* for continuously measured quantum systems. Gradients can be computed with PyTorch’s automatic differentiation, or using a constant memory cost method. The library is being developed by several PhD students in physics, most of whom have substantial experience in software development.