鲁棒量子控制的风险敏感优化
Risk-sensitive Optimization for Robust Quantum Controls
摘要：
量子操作的高精度和鲁棒控制是实现可纠错量子计算的关键。在本文中，我们表明，高精度控制的鲁棒性可以显着提高通过基于采样的随机优化的风险敏感损失函数。遵循该损失函数的随机梯度下降方向，引导优化以可调方式惩罚性能差的不确定性样本。我们提出了两个算法，这是被称为风险敏感的GRAPE和自适应风险敏感的GRAPE。它们的有效性证明了数值模拟，这是能够实现高的控制鲁棒性，同时保持高保真度。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
