弱监督量子错误缓解

中文摘要 AI 辅助

量子错误缓解的监督方法学习从含噪声电路输出到理想输出的映射，因此需要理想输出。产生这些理想输出需要无噪声的经典模拟，其成本随系统规模呈指数增长，因此在错误缓解最为关键的场景中，监督信号恰恰不可用。我们探究能否利用从电路结构和硬件校准中获得的廉价、个体不可靠的信号来替代理想标签。我们组装了十六个启发式标签函数（稳定子与奇偶校验约束、弛豫与读出特性、局部深度、门计数和邻近活动），通过概率标签模型调和它们之间的分歧，并将得到的每量子位错误概率解读为读出通道，其逆操作用于缓解测量分布。训练路径中不涉及任何理想输出。在IBM两台设备上执行的147,000个五量子位电路上，该方法消除了24.3%（Algiers）和28.8%（Hanoi）的Kullback-Leibler散度（相对于理想分布），而最强的已发表分析基线仅消除了15.4%和21.5%，这一优势在两台设备上均保持，且远超出其bootstrap区间。在存在理想标签的情况下，基于理想分布训练的监督神经模型仍然更强，我们量化了这一差距而非回避它；该方法的主张适用于不存在理想标签的场景，因为对于需要错误缓解的电路，这些标签所需的计算无法完成。代码将很快发布。

英文摘要

Supervised approaches to quantum error mitigation learn a map from noisy circuit outputs to ideal ones, and therefore require the ideal outputs. Producing those ideal outputs demands noiseless classical simulation, whose cost grows exponentially with system size, so supervision is unavailable in exactly the regime where mitigation matters most. We ask whether cheap, individually unreliable signals drawn from circuit structure and hardware calibration can take the place of ideal labels. We assemble sixteen heuristic labeling functions (stabilizer and parity constraints, relaxation and readout characteristics, local depth, gate counts, and neighboring activity), reconcile their disagreements with a probabilistic label model, and read the resulting per-qubit error probabilities as a readout channel whose inverse mitigates the measured distribution. No ideal output enters the training path. On $147{,}000$ five-qubit circuits executed on two IBM devices, the method removes $24.3\%$ (Algiers) and $28.8\%$ (Hanoi) of the Kullback-Leibler divergence to the ideal distribution, against $15.4\%$ and $21.5\%$ for the strongest published analytical baseline, a margin that holds on both devices and lies far outside its bootstrap interval. Supervised neural models trained on ideal distributions remain stronger where such labels exist, and we quantify that gap rather than setting it aside; the method's claim is to the regime where they do not, since the labels they require cannot be computed for the circuits mitigation is needed for. The codes will be released shortly.

发表机构

• Vision-Guided and Intelligent Robotics Lab (ViGIR)（视觉引导与智能机器人实验室）
• University of Missouri（密苏里大学）

机构由 AI 辅助整理，请以论文原文为准。