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arXiv 2008.11178quant-phcs.ITmath-phmath.ITmath.MPmath.STstat.TH
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量子信道多参数估计的RLD Fisher信息界

RLD Fisher Information Bound for Multiparameter Estimation of Quantum Channels

Vishal Katariya, Mark M. Wilde

AI总结:

本文研究量子信道多参数估计的基本极限,证明RLD Fisher信息值的链式法则不等式,指出摊销无法提升其值,进而得出有限时多参数设置中海森堡标度不可达的结论。

摘要:

量子计量学的基本任务之一是估计嵌入在噪声过程(即量子信道)中的多个参数。本文通过摊销概念和右对数导数(RLD)Fisher信息值研究量子信道估计的基本极限。我们的关键技术成果是证明了RLD Fisher信息值的链式法则不等式,这表明摊销(即访问催化剂态族)不会增加量子信道的RLD Fisher信息值。该技术成果从RLD Fisher信息值的角度,为序贯设置中的多参数信道估计提供了一个基本且可高效计算的限制。因此,我们得出结论:若RLD Fisher信息值有限,则在多参数设置中无法实现海森堡标度。

英文摘要:

One of the fundamental tasks in quantum metrology is to estimate multiple parameters embedded in a noisy process, i.e., a quantum channel. In this paper, we study fundamental limits to quantum channel estimation via the concept of amortization and the right logarithmic derivative (RLD) Fisher information value. Our key technical result is the proof of a chain-rule inequality for the RLD Fisher information value, which implies that amortization, i.e., access to a catalyst state family, does not increase the RLD Fisher information value of quantum channels. This technical result leads to a fundamental and efficiently computable limitation for multiparameter channel estimation in the sequential setting, in terms of the RLD Fisher information value. As a consequence, we conclude that if the RLD Fisher information value is finite, then Heisenberg scaling is unattainable in the multiparameter setting.

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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