Quantum error mitigation in quantum annealing

IF 6.6 1区物理与天体物理 Q1 PHYSICS, APPLIED

npj Quantum Information Pub Date : 2025-03-06 DOI:10.1038/s41534-025-00977-3

Jack Raymond, Mohammad H. Amin, Andrew D. King, Richard Harris, William Bernoudy, Andrew J. Berkley, Kelly Boothby, Anatoly Smirnov, Fabio Altomare, Michael Babcock, Catia Baron, Jake Connor, Martin H. Dehn, Colin Enderud, Emile Hoskinson, Shuiyuan Huang, Mark W. Johnson, Eric Ladizinsky, Trevor Lanting, Allison J. R. MacDonald, Gaelen Marsden, Reza Molavi, Travis Oh, Gabriel Poulin-Lamarre, Hugh Ramp, Chris Rich, Berta Trullas Clavera, Nicholas Tsai, Mark Volkmann, Jed D. Whittaker, Jason Yao, Niclas Heinsdorf, Nitin Kaushal, Alberto Nocera, Marcel Franz, Jacek Dziarmaga

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Abstract

Quantum error mitigation (QEM) presents a promising near-term approach to reducing errors when estimating expectation values in quantum computing. Here, we introduce QEM techniques tailored for quantum annealing, using zero-noise extrapolation (ZNE). We implement ZNE through zero-temperature and zero-time extrapolations. The practical zero-time extrapolation developed exploits the Kibble-Zurek mechanism so that only problem-Hamiltonian rescaling is required. We conduct experimental investigations into the quantum critical and post-critical dynamics of a transverse-field Ising spin chain by examining statistics with weak and strong post-critical dynamics. We demonstrate successful mitigation of thermal noise and non-thermal errors through both of these extrapolation techniques.

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量子退火中的量子误差缓解

量子误差缓解（QEM）是在量子计算中估计期望值时减少误差的一种有前途的近期方法。在这里，我们介绍了使用零噪声外推（ZNE）为量子退火量身定制的QEM技术。我们通过零温度和零时间外推来实现ZNE。开发的实际零时间外推利用了Kibble-Zurek机制，因此只需要问题-哈密顿重标度。我们对横向场伊辛自旋链的量子临界和后临界动力学进行了实验研究，通过检查具有弱和强后临界动力学的统计。通过这两种外推技术，我们成功地证明了热噪声和非热误差的缓解。

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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来源期刊

npj Quantum Information Computer Science-Computer Science (miscellaneous)

CiteScore

13.70

自引率

3.90%

发文量

130

审稿时长

29 weeks

期刊介绍： The scope of npj Quantum Information spans across all relevant disciplines, fields, approaches and levels and so considers outstanding work ranging from fundamental research to applications and technologies.