Optimizing quantum control pulses with complex constraints and few variables through Tensorflow

• URL: http://arxiv.org/abs/2110.05334v2
• Date: Tue, 12 Oct 2021 04:37:41 GMT
• Title: Optimizing quantum control pulses with complex constraints and few variables through Tensorflow
• Authors: Yao Song, Junning Li, Yong-Ju Hai, Qihao Guo, and Xiu-Hao Deng
• Abstract summary: We show how to apply optimal control algorithms on realistic quantum systems by incorporating multiple constraints into the gradient optimization. We test our algorithm by finding smooth control pulses to implement single-qubit and two-qubit gates for superconducting transmon qubits with always-on interaction. Our algorithm provides a promising optimal quantum control approach that is friendly to complex and optional physical constraints.
• Score: 0.0
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Applying optimal control algorithms on realistic quantum systems confronts two key challenges: to efficiently adopt physical constraints in the optimization and to minimize the variables for the convenience of experimental tune-ups. In order to resolve these issues, we propose a novel algorithm by incorporating multiple constraints into the gradient optimization over piece-wise pulse constant values, which are transformed to contained numbers of the finite Fourier basis for bandwidth control. Such complex constraints and variable transformation involved in the optimization introduce extreme difficulty in calculating gradients. We resolve this issue efficiently utilizing auto-differentiation on Tensorflow. We test our algorithm by finding smooth control pulses to implement single-qubit and two-qubit gates for superconducting transmon qubits with always-on interaction, which remains a challenge of quantum control in various qubit systems. Our algorithm provides a promising optimal quantum control approach that is friendly to complex and optional physical constraints.

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