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arXiv 2301.09192quant-phcs.ITcs.LGmath.IT
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基于单次测量的Pauli信道学习的下界

Lower Bounds on Learning Pauli Channels with Individual Measurements

Univ Lyon, Inria, ENS Lyon, UCBL, LIP(里昂大学,法国国家信息与自动化研究所,里昂高等师范学校,克洛德·贝尔纳里昂第一大学,里昂计算机科学实验室)

Omar Fawzi, Aadil Oufkir, Daniel Stilck França

AI总结:

本文研究Pauli信道学习的样本复杂度下界,针对无纠缠辅助且测量后复用信道的场景,分别给出非自适应与自适应算法的下界,证明现有学习过程的最优性,且部分下界适用于更强的信道使用模型。

摘要:

了解影响量子器件的噪声对于量子技术的规模化发展具有根本重要性。Pauli信道是一类尤为重要的噪声模型,因为随机编译技术可将任意量子信道有效转化为该形式,且其结构比通用量子信道显著更规整。本文给出了在钻石范数下学习Pauli信道的样本复杂度的基本下界。我们考虑的策略不使用与未知信道输入纠缠的辅助系统,且必须在复用信道前执行测量。对于非自适应算法,我们证明学习n量子比特Pauli信道的下界为Ω(2^{3n}ε^{-2})。这一结果尤其表明,Flammia和Wallman近期提出的学习过程在本质上是最优的。在自适应场景下,我们证明当ε=O(2^{-n})时下界为Ω(2^{2.5n}ε^{-2}),且对任意ε>0均有下界Ω(2^{2n}ε^{-2})。最后这一下界甚至适用于更强的模型:在每一步测量前,未知信道可与幺正运算交替任意多次顺序使用。

英文摘要:

Understanding the noise affecting a quantum device is of fundamental importance for scaling quantum technologies. A particularly important class of noise models is that of Pauli channels, as randomized compiling techniques can effectively bring any quantum channel to this form and are significantly more structured than general quantum channels. In this paper, we show fundamental lower bounds on the sample complexity for learning Pauli channels in diamond norm. We consider strategies that may not use auxiliary systems entangled with the input to the unknown channel and have to perform a measurement before reusing the channel. For non-adaptive algorithms, we show a lower bound of $Ω(2^{3n}\varepsilon^{-2})$ to learn an $n$-qubit Pauli channel. In particular, this shows that the recently introduced learning procedure by Flammia and Wallman is essentially optimal. In the adaptive setting, we show a lower bound of $Ω(2^{2.5n}\varepsilon^{-2})$ for $\varepsilon=\mathcal{O}(2^{-n})$, and a lower bound of $Ω(2^{2n}\varepsilon^{-2} )$ for any $\varepsilon> 0$. This last lower bound holds even in a stronger model where in each step, before performing the measurement, the unknown channel may be used arbitrarily many times sequentially interspersed with unital operations.

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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