A differentiable programming method for quantum control

Schäfer, Frank and Kloc, Michal and Bruder, Christoph and Lörch, Niels. (2020) A differentiable programming method for quantum control. Machine Learning: Science and Technology, 1 (3). 035009.

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Official URL: https://edoc.unibas.ch/80354/

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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 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.
Faculties and Departments:05 Faculty of Science > Departement Physik > Physik > Theoretische Physik (Bruder)
UniBasel Contributors:Bruder, Christoph and Schäfer, Frank and Kloc, Michal
Item Type:Article, refereed
Article Subtype:Research Article
Publisher:IOP Publishing
Note:Publication type according to Uni Basel Research Database: Journal article
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Last Modified:14 Apr 2021 12:08
Deposited On:14 Apr 2021 12:08

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