Current quantum computers suffer from non-stationary noise channels with high
error rates, which undermines their reliability and reproducibility. We propose
a Bayesian inference-based adaptive algorithm that can learn and mitigate
quantum noise in response to changing channel conditions. Our study emphasizes
the need for dynamic inference of critical channel parameters to improve
program accuracy. We use the Dirichlet distribution to model the stochasticity
of the Pauli channel. This allows us to perform Bayesian inference, which can
improve the performance of probabilistic error cancellation (PEC) under
time-varying noise. Our work demonstrates the importance of characterizing and
mitigating temporal variations in quantum noise, which is crucial for
developing more accurate and reliable quantum technologies. Our results show
that Bayesian PEC can outperform non-adaptive approaches by a factor of 4.5x
when measured using Hellinger distance from the ideal distribution.

提出基于贝叶斯推断的自适应算法来学习和减轻量子噪声以应对不断变化的信道条件，强调动态推断关键信道参数以提高编程准确性，并展示了贝叶斯概率误差消除在噪声变化时的优越性，结果显示贝叶斯概率误差消除在理想分布的 Hellinger 距离度量下胜过非自适应方法约 4.5 倍。

时变量子噪声的自适应缓解

Adaptive mitigation of time-varying quantum noise

The inevitable accumulation of errors in near-future quantum devices
represents a key obstacle in delivering practical quantum advantages,
motivating the development of various quantum error-mitigation methods. Here,
we derive fundamental bounds concerning how error-mitigation algorithms can
reduce the computation error as a function of their sampling overhead. Our
bounds place universal performance limits on a general error-mitigation
protocol class. We use them to show (1) that the sampling overhead that ensures
a certain computational accuracy for mitigating local depolarizing noise in
layered circuits scales exponentially with the circuit depth for general
error-mitigation protocols and (2) the optimality of probabilistic error
cancellation among a wide class of strategies in mitigating the local dephasing
noise on an arbitrary number of qubits. Our results provide a means to identify
when a given quantum error-mitigation strategy is optimal and when there is
potential room for improvement.

该研究提出了一种基于采样开销的错误纠正算法的性能上下限，并应用其分析了量子计算中错误依赖问题的改善策略，结果表明概率性错误取消策略是最优的。