  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.
