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arXiv 2305.02439quant-ph
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用于高效量子可观测量估计的测量方案的机器学习

Machine learning of measurement schemes for efficient quantum observable estimation

University of Toronto（多伦多大学）；Vector Institute for Artificial Intelligence（向量人工智能研究所）；National Institute of Advanced Industrial Science and Technology (AIST)（产业技术综合研究所）；Keio University（庆应义塾大学）

Zi-Jian Zhang, Kouhei Nakaji, Matthew Choi, Alán Aspuru-Guzik

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AI总结：

针对量子可观测量估计这一关键且受限的任务，提出用机器学习技术直接从可观测量学习测量方案的框架，具体实现C-LBCS，经数值验证其在分子系统上优于现有方法，为大量子系统可观测量估计开辟新途径。

摘要：

可观测量期望值的估计是量子计算中的关键子程序，也是许多近期量子算法性能的瓶颈。虽已提出多种方法通过设计测量方案减少所需测量次数，但这些方案通常基于手工启发式构建，限制了测量效率。本文提出一个直接从可观测量学习测量方案的框架，使用随机梯度下降等机器学习技术及双时间尺度更新规则。具体实现为复合局部偏置经典影子（C-LBCS），它能端到端学习局部偏置经典影子及其混合权重。在高达\(CO_2\)（30个量子比特）的分子系统上进行数值验证，结果表明C-LBCS虽简单却优于先前的最先进方法。该方法为大量子系统上的高效可观测量估计开辟了可靠且可扩展的途径。

英文摘要：

Estimation of the expectation value of observables is a key subroutine in quantum computing and is also the bottleneck of the performance of many near-term quantum algorithms. Many methods have been proposed to reduce the number of measurements needed for this task by designing measurement schemes that decide the measurements to perform; however, these schemes are usually constructed from hand-crafted heuristics, which limits the measurement efficiency they can achieve. In this paper, we propose a framework for learning measurement schemes directly from the observable, using machine learning techniques including stochastic gradient descent and a two time-scale update rule. As a concrete realization of this framework, we introduce Composite-Locally Biased Classical Shadow (C-LBCS), which learns a mixture of locally-biased classical shadows and their mixing weights end-to-end. We numerically demonstrate C-LBCS on molecular systems up to $\mathrm{CO}_2$ (30 qubits) and show that C-LBCS outperforms the previous state-of-the-art methods despite its simplicity. We believe our approach opens up a reliable and scalable path toward efficient observable estimation on large quantum systems.

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。

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