Risk-sensitive Optimization for Robust Quantum Controls

• URL: http://arxiv.org/abs/2104.01323v1
• Date: Sat, 3 Apr 2021 06:44:10 GMT
• Title: Risk-sensitive Optimization for Robust Quantum Controls
• Authors: Xiaozhen Ge and Re-Bing Wu
• Abstract summary: We show that the robustness of high-precision controls can be remarkably enhanced through sampling-based optimization of a risk-sensitive loss function. We propose two algorithms, which are tunable as the risk-sensitive GRAPE and the adaptive risk-sensitive GRAPE.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Highly accurate and robust control of quantum operations is vital for the realization of error-correctible quantum computation. In this paper, we show that the robustness of high-precision controls can be remarkably enhanced through sampling-based stochastic optimization of a risk-sensitive loss function. Following the stochastic gradient-descent direction of this loss function, the optimization is guided to penalize poor-performance uncertainty samples in a tunable manner. We propose two algorithms, which are termed as the risk-sensitive GRAPE and the adaptive risk-sensitive GRAPE. Their effectiveness is demonstrated by numerical simulations, which is shown to be able to achieve high control robustness while maintaining high fidelity.

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