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arXiv 2406.09299quant-ph
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中段测量的泡利噪声学习

Pauli Noise Learning for Mid-Circuit Measurements

Jordan Hines, Timothy Proctor

AI总结:

针对中段测量(MCMs)基准测试的可扩展性和误差量化局限,提出泡利噪声学习理论并创建MCM循环基准测试方法,可提取误差率详细信息、量化相关误差,且能集成现有技术扩展表征含MCMs的电路。

摘要:

当前中段测量(MCMs)的基准测试在可扩展性或可量化的误差类型方面存在局限,因此需要新的技术来量化其性能。在此,我们提出一种学习MCMs中泡利噪声的理论,并利用该理论创建MCM循环基准测试——一种可扩展的MCM基准测试方法。MCM循环基准测试可提取随机编译的MCM层和克利福德门层中误差率的详细信息,我们展示了其结果如何用于量化当前量子硬件上MCM期间的相关误差。该方法可集成到现有泡利噪声学习技术中,以可扩展地表征和基准测试包含MCMs的各类电路。

英文摘要:

Current benchmarks for mid-circuit measurements (MCMs) are limited in scalability or the types of error they can quantify, necessitating new techniques for quantifying their performance. Here, we introduce a theory for learning Pauli noise in MCMs and use it to create MCM cycle benchmarking, a scalable method for benchmarking MCMs. MCM cycle benchmarking extracts detailed information about the rates of errors in randomly compiled layers of MCMs and Clifford gates, and we demonstrate how its results can be used to quantify correlated errors during MCMs on current quantum hardware. Our method can be integrated into existing Pauli noise learning techniques to scalably characterize and benchmark wide classes of circuits containing MCMs.

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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