从噪声样本计算出的无噪声期望值的可证明边界。

IF 12 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Samantha V Barron, Daniel J Egger, Elijah Pelofske, Andreas Bärtschi, Stephan Eidenbenz, Matthis Lehmkuehler, Stefan Woerner
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引用次数: 0

摘要

量子计算已成为一种强大的计算范式,能够解决经典计算机无法解决的问题。然而,当今的量子计算机噪声很大,给获得准确结果带来了挑战。在此,我们探讨了噪声对量子计算的影响,重点关注从噪声量子计算机中采样比特串的挑战以及对优化和机器学习的影响。我们正式量化了从噪声量子计算机中提取良好样本的采样开销,并将其与层保真度联系起来,层保真度是确定噪声量子处理器性能的指标。此外,我们还展示了如何利用高噪声样本的风险条件值来确定无噪声期望值的可证明边界。我们讨论了如何针对不同算法利用这些界限,并通过在涉及多达 127 个量子比特的真实量子计算机上进行实验来证明我们的发现。结果显示与理论预测非常吻合。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Provable bounds for noise-free expectation values computed from noisy samples.

Quantum computing has emerged as a powerful computational paradigm capable of solving problems beyond the reach of classical computers. However, today's quantum computers are noisy, posing challenges to obtaining accurate results. Here, we explore the impact of noise on quantum computing, focusing on the challenges in sampling bit strings from noisy quantum computers and the implications for optimization and machine learning. We formally quantify the sampling overhead to extract good samples from noisy quantum computers and relate it to the layer fidelity, a metric to determine the performance of noisy quantum processors. Further, we show how this allows us to use the conditional value at risk of noisy samples to determine provable bounds on noise-free expectation values. We discuss how to leverage these bounds for different algorithms and demonstrate our findings through experiments on real quantum computers involving up to 127 qubits. The results show strong alignment with theoretical predictions.

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CiteScore
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