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arXiv 2201.11046quant-ph
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面向近期量子器件稀疏测量结果的高效量子读出误差缓解

Efficient quantum readout-error mitigation for sparse measurement outcomes of near-term quantum devices

Bo Yang, Rudy Raymond, Shumpei Uno

AI总结:

针对近期量子器件的稀疏测量结果,本文提出两种O(ns²)复杂度的高效QREM方法,并在IBM器件与MLAE模拟中验证其可扩展且误差缓解更优。

摘要:

近期量子器件上的读出误差是主要噪声因素之一,可通过称为量子读出误差缓解(QREM)的经典后处理加以缓解。标准QREM将噪声校准矩阵的逆应用于结果概率分布,所需计算资源相对于被测量子比特数呈指数增长。对于当前拥有数十个或更多量子比特的量子器件而言,这变得不可行。本文提出两种高效QREM方法,对于n个量子比特和s次采样(shots)的概率分布,可在O(ns²)时间内完成,主要旨在缓解仅有少数状态占主导的稀疏概率分布。我们在以下三种情形中将所提方法与若干近期QREM方法进行比较:GHZ态的期望值、其保真度,以及采用改进Grover迭代器的最大似然振幅估计(MLAE)算法的估计误差。GHZ态的前两种情形在真实IBM量子器件上进行,第三种则通过数值模拟完成。使用所提方法,65量子比特GHZ态的缓解仅需数秒,并且我们观察到29量子比特GHZ态的保真度超过0.5。所提方法还成功降低了MLAE算法中的估计误差,总体上优于其他QREM方法所得结果。

英文摘要:

The readout error on near-term quantum devices is one of the dominant noise factors, which can be mitigated by classical postprocessing called quantum readout error mitigation (QREM). The standard QREM applies the inverse of noise calibration matrix to the outcome probability distribution using exponential computational resources to the number of measured qubits. This becomes infeasible for the current quantum devices with tens of qubits or more. Here we propose two efficient QREM methods finishing in $O(ns^2)$ time for probability distributions of $n$ qubits and $s$ shots, which mainly aim at mitigating sparse probability distributions such that only a few states are dominant. We compare the proposed methods with several recent QREM methods in the following three cases: expectation values of the GHZ state, its fidelities, and the estimation error of maximum likelihood amplitude estimation (MLAE) algorithm with a modified Grover iterator. The two cases of the GHZ state are on real IBM quantum devices, while the third is with numerical simulation. Using the proposed method, the mitigation of the 65-qubit GHZ state takes only a few seconds, and we witness the fidelity of the 29-qubit GHZ state exceeding 0.5. The proposed methods also succeed in reducing the estimation error in the MLAE algorithm, outperforming the results by other QREM methods in general.

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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