Risk-sensitive optimization for robust quantum controls

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 (RS) 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 RS GRAPE and the adaptive RS GRAPE. Their effectiveness is demonstrated by numerical simulations, which is shown to be able to achieve high-control robustness while maintaining high fidelity.