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.

中文翻译：

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稳健量子控制的风险敏感优化

对量子操作的高精度和鲁棒控制对于实现可纠错的量子计算至关重要。在本文中，我们表明，通过对风险敏感 (RS) 损失函数进行基于采样的随机优化，可以显着增强高精度控制的鲁棒性。遵循此损失函数的随机梯度下降方向，引导优化以可调节的方式惩罚性能不佳的不确定性样本。我们提出了两种算法，称为 RS GRAPE 和自适应 RS GRAPE。数值模拟证明了它们的有效性，这表明能够在保持高保真度的同时实现高控制鲁棒性。