量子误差缓解中由模型违背导致的系统误差界限
Bounding the systematic error in quantum error mitigation due to model violation
AI总结：
本研究提出一种无需额外实验的方法，高效计算量子误差缓解中误差模型不准确性影响的上界，并在IBM量子处理器上验证，同时揭示深度噪声电路中误差并非总由Clifford电路最大化。
摘要：
量子误差缓解是实现量子效用并在近期内可能实现量子优势的一条有前景的途径。许多最先进的误差缓解方案利用量子处理器中误差的知识，这引发了一个问题：误差模型的不准确性在多大程度上影响误差缓解的性能。在这项工作中，我们开发了一种方法论，以高效计算误差缓解中误差模型不准确性影响的上界。我们的协议不需要额外的实验，而是依赖于误差模型与生成该模型的误差学习数据之间的比较。我们通过在IBM量子超导量子比特量子处理器上部署该方法论，并通过标准误差模型的数值模拟，展示了其有效性。我们表明，我们估计的上界通常接近随机电路上误差缓解的最差观测性能。我们的方法论也可以被理解为一种具有操作意义的度量，用于评估误差模型的质量，并且我们进一步扩展了我们的方法论，以允许误差模型之间的比较。最后，与人们可能预期的相反，我们表明在足够深度的噪声分层电路中，可观测误差并不总是由Clifford电路最大化，这可能具有独立的意义。
英文摘要：
Quantum error mitigation is a promising route to achieving quantum utility, and potentially quantum advantage in the near-term. Many state-of-the-art error mitigation schemes use knowledge of the errors in the quantum processor, which opens the question to what extent inaccuracy in the error model impacts the performance of error mitigation. In this work, we develop a methodology to efficiently compute upper bounds on the impact of error-model inaccuracy in error mitigation. Our protocols require no additional experiments, and instead rely on comparisons between the error model and the error-learning data from which the model is generated. We demonstrate the efficacy of our methodology by deploying it on an IBM Quantum superconducting qubit quantum processor, and through numerical simulation of standard error models. We show that our estimated upper bounds are typically close to the worst observed performance of error mitigation on random circuits. Our methodology can also be understood as an operationally meaningful metric to assess the quality of error models, and we further extend our methodology to allow for comparison between error models. Finally, contrary to what one might expect we show that observable error in noisy layered circuits of sufficient depth is not always maximized by a Clifford circuit, which may be of independent interest.
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