A differentiable programming method for quantum control
- URL: http://arxiv.org/abs/2002.08376v1
- Date: Wed, 19 Feb 2020 19:00:05 GMT
- Title: A differentiable programming method for quantum control
- Authors: Frank Sch\"afer, Michal Kloc, Christoph Bruder, Niels L\"orch
- Abstract summary: We introduce a method based on differentiable programming to leverage explicit knowledge of the differential equations governing the dynamics of the system.
We demonstrate the method's viability and robustness to noise in eigenstate preparation tasks for three systems.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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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