从噪声量子态中检索非线性特征

Benchi Zhao, M. Jing, Lei Zhang, Xuanqiang Zhao, Yuanyi Chen, Kun Wang, Xin Wang
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摘要

准确估计量子态的高阶矩是量子计算中许多关键任务的基本先决条件,例如纠缠光谱学、熵估计、频谱估计以及从量子态预测非线性特征。但在现实中,不可避免的量子噪声使我们无法获得所需的值。本文针对这一问题,系统分析了从噪声态中提取高阶矩的可行性和效率。我们首先证明,当且仅当底层噪声信道是可逆的时,存在一种能够完成这一任务的量子协议。然后,我们建立了一种方法,可以推导出只使用量子操作和经典后处理就能达到最佳采样复杂度的协议。与传统协议相比,我们的协议开销更低,而且由于采用了一种名为"'可观测转移法 "的新技术,避免了对不同量子操作的采样,使这些协议成为在当前量子设备上实际使用的有力候选方案。所提出的方法还显示了纠缠协议在检索高阶信息方面的威力,而在现有方法中,纠缠并无帮助。我们进一步构建了大型量子系统的协议,以检索去极化信道,从而使所提出的方法具有可扩展性。我们的工作有助于加深对量子噪声如何影响高阶信息提取的理解,并为如何解决这一问题提供了指导。 美国物理学会出版 2024
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Retrieving Nonlinear Features from Noisy Quantum States
Accurately estimating high-order moments of quantum states is an elementary precondition for many crucial tasks in quantum computing, such as entanglement spectroscopy, entropy estimation, spectrum estimation, and predicting nonlinear features from quantum states. But in reality, inevitable quantum noise prevents us from accessing the desired value. In this paper, we address this issue by systematically analyzing the feasibility and efficiency of extracting high-order moments from noisy states. We first show that there exists a quantum protocol capable of accomplishing this task if and only if the underlying noise channel is invertible. We then establish a method for deriving protocols that attain optimal sample complexity using quantum operations and classical postprocessing only. Our protocols, in contrast to conventional ones, incur lower overheads and avoid sampling different quantum operations due to a novel technique called the “‘observable shift method,” making the protocols strong candidates for practical use on current quantum devices. The proposed method also indicates the power of entangled protocols in retrieving high-order information, whereas in the existing methods, entanglement does not help. We further construct the protocol for large quantum systems to retrieve the depolarizing channels, making the proposed method scalable. Our work contributes to a deeper understanding of how quantum noise could affect high-order information extraction and provides guidance on how to tackle it. Published by the American Physical Society 2024
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