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arXiv · 2002.08376

A differentiable programming method for quantum control

Frank Schäfer·Michal Kloc·Christoph Bruder·Niels Lörch
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Abstract

Optimal control is highly desirable in many current quantum systems, especially to realize tasks in quantum information processing. We introduce a method based on differentiable programming to leverage explicit knowledge of the differential equations governing the dynamics of the system. In particular, a control agent is represented as a neural network that maps the state of the system at a given time to a control pulse. The parameters of this agent are optimized via gradient information obtained by direct differentiation through both the neural network \emph{and} the differential equation of the system. This fully differentiable reinforcement learning approach ultimately yields time-dependent control parameters optimizing a desired figure of merit. We demonstrate the method's viability and robustness to noise in eigenstate preparation tasks for three systems: a~single qubit, a~chain of qubits, and a quantum parametric oscillator.

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quant-phcond-mat.dis-nnphysics.comp-ph

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Frank Schäfer, Michal Kloc, Christoph Bruder, Niels Lörch. 2020-02-19. A differentiable programming method for quantum control. https://doi.org/10.1088/2632-2153%2Fab9802

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