动力学期望值估计的新方向
New directions in dynamical expectation estimation
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中文总结 AI 辅助
本文提出一种联合优化状态与可观测值的扫描算法，用于动力学期望值估计，在30量子比特随机电路上误差比变分态压缩低两到三个数量级。
中文摘要 AI 辅助
计算动力学期望值通常依赖于针对状态或可观测值分别优化的近似方法，而未考虑它们的误差在最终期望值中如何结合。本工作探索了一种替代方法，其中每个近似都同时受状态和可观测值的引导，考虑它们如何共同决定目标期望值。这一思想通过一种带有耦合损失函数的扫描算法实现，用于前向状态和后向可观测值的更新。精确的误差关系为所提出的损失函数如何提高精度提供了分析依据。在30量子比特随机电路上的数值测试表明，在相同键维数下，误差比变分态压缩小两到三个数量级。这些结果激励了对动力学期望值估计中联合状态与可观测值近似的进一步探索。
英文摘要
Computing dynamical expectation values typically relies on approximations optimized for the state or observable separately, without accounting for how their errors combine in the final expression. This work explores an alternative approach in which each approximation is guided by both the state and the observable, accounting for how they jointly determine the target expectation value. This idea is implemented through a sweep algorithm with coupled loss functions for forward state and backward observable updates. Exact error relations provide an analytical rationale for how the proposed losses can improve the accuracy. Numerical tests on 30-qubit random circuits show errors two to three orders of magnitude smaller than those of variational state compression at equal bond dimensions. These results motivate further exploration of joint state and observable approximation for dynamical expectation value estimation.
发表机构
- The Affiliated Institute of ETRI（韩国电信研究院附属研究所）
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