Risk-sensitive Optimization for Robust Quantum Controls

TL;DR

This paper introduces risk-sensitive stochastic optimization algorithms to enhance the robustness of quantum control operations, achieving high fidelity and robustness through sampling-based methods.

Contribution

The paper proposes two novel algorithms, risk-sensitive GRAPE and adaptive risk-sensitive GRAPE, for improving quantum control robustness using a risk-sensitive loss function.

Findings

Algorithms significantly improve control robustness.

Numerical simulations demonstrate high fidelity and robustness.

Methods outperform traditional approaches in robustness.

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.