基于上下文多臂老虎机方法的自适应零噪声外推量子噪声抑制
Quantum Noise Mitigation with Adaptive Zero-Noise Extrapolation: A Contextual Multi-Armed Bandits Approach
AI总结：
本研究针对NISQ系统的动态时变噪声问题，提出结合CMAB的自适应ZNE框架，可减少量子电路执行开销并提升估计量保真度，性能优于现有方法。
AI中文摘要：
变分量子电路（VQC）是近期量子计算的核心，但其实际部署受到噪声的严重阻碍。现有误差抑制方法如零噪声外推（ZNE）通常假设噪声是静态的，但实际有噪声中等规模量子（NISQ）系统呈现出动态时变噪声，这一问题在很大程度上未得到解决。为克服这一关键缺口，本研究提出一种适用于VQC的新型自适应噪声抑制框架，将ZNE与上下文多臂老虎机（CMAB）相结合，能够根据近似参数（如深度、参数数量）和不断变化的噪声环境，动态选择电路折叠层级。与固定折叠或启发式ZNE不同，该方法采用在线自适应以提升ZNE的精度并减少冗余量子电路执行。在真实量子硬件上开展的大量模拟和实验揭示了以下重要特性：（i）更深的VQC会积累噪声，降低精度并增加量子电路执行次数；（ii）当折叠层级选择恰当时，ZNE可恢复估计量保真度；（iii）在10 Mbps带宽预算下，与固定折叠和网格搜索ZNE相比，CMAB引导的折叠可将量子电路执行往返次数最多减少40%，交换字节数最多减少35%，端到端成本最多降低30%，在CIFAR-10、深度3、噪声带η=0.05的条件下，估计量保真度最高提升6.9%。这些结果表明，与现有噪声抑制方法相比，本方法实现了显著的性能提升，凸显了其在为VQC提供稳健噪声抑制方面的有效性。源代码也已公开发布以支持可重复性。
英文摘要：
Variational quantum circuits (VQCs) are central to near-term quantum computing, yet their practical deployment is severely hindered by noise. While existing error mitigation methods, such as zero-noise extrapolation (ZNE), typically assume static noise, real noisy intermediate-scale quantum (NISQ) systems exhibit dynamic, time-varying noise that remains largely unaddressed. To overcome this critical gap, our work introduces a novel adaptive noise mitigation framework for VQCs that integrates ZNE with contextual multi-armed bandits (CMAB), enabling dynamic, context-aware selection of circuit-folding levels based on ansatz parameters (e.g., depth, parameter count) and the evolving noise environment. Unlike fixed-fold or heuristic ZNE, our approach uses online adaptation to improve the accuracy of ZNE and reduce redundant quantum circuit executions. Our extensive simulations and experiments on real quantum hardware reveal the following important properties: (i) deeper VQCs accumulate noise, degrading accuracy and increasing the number of quantum circuit executions; (ii) ZNE restores estimator fidelity when the folding level is chosen appropriately; and (iii) CMAB-guided folding cuts quantum circuit execution round trips by up to 40\%, bytes exchanged by up to 35\%, and end-to-end cost by up to 30\% under a 10~Mbps budget, with up to 6.9\% higher estimator fidelity (CIFAR-10, depth 3, noise band $η=0.05$), versus fixed-fold and grid-search ZNE. These results demonstrate substantial performance gains over existing noise mitigation methods, underscoring the effectiveness of our design in supporting robust noise mitigation for VQCs. The source code is also publicly released to support reproducibility.
