AI 中文总结
该研究提出物理引导线性映射器（PGLM），基于电路复杂度等7种可解释特征实现量子误差缓解，在多量子比特基准电路上降低误差，可集成到变分算法中，为NISQ设备提供低延迟替代方案。
AI 中文摘要
我们提出一种新型物理引导线性映射器（PGLM）用于量子误差缓解，该模型使用从电路复杂度和设备校准数据中提取的7种不同可解释特征。其目标是为含噪声中等规模量子（NISQ）设备提供一种数据高效、可解释且低延迟的替代方案，替代量子误差缓解中的黑箱机器学习方法。在52个模拟基准电路（1-4量子比特）上评估显示，PGLM在噪声累积区域表现出强性能：3量子比特电路的RMSE降低50.1%，4量子比特电路降低32.3%，而单量子比特电路性能有所下降。电路大小感知部署策略实现了32.6%的整体提升，亚毫秒级推理使其可集成到变分算法中，对学习系数的分析表明，电路深度和CNOT数量主导误差预测，与退相干机制一致。结果基于带理想化噪声模型的模拟器，硬件验证仍是未来的重要工作。
英文摘要
We introduce a novel physics-guided linear mapper (PGLM) for quantum error mitigation that uses seven distinct interpretable features derived from circuit complexity and device calibration data. The goal is to provide a data-efficient, interpretable, and low-latency alternative to the black-box machine learning for quantum error mitigation in noisy-intermediate scale quantum devices. Evaluated on 52 simulated benchmark circuits (1--4 qubits), PGLM demonstrates strong performance in noise-accumulation regimes: 50.1% RMSE reduction on 3-qubit circuits and 32.3% on 4-qubit circuits, while single-qubit circuits show degraded performance. A circuit-size-aware deployment policy achieves 32.6% aggregate improvement. Sub-millisecond inference enables integration into variational algorithms, and analysis of learned coefficients reveals that circuit depth and CNOT count dominate error prediction, consistent with decoherence mechanisms. Results are simulator-based with idealized noise models; hardware validation remains essential future work.
Comments18 pages, 7 figures. Published in Intelligent Computing: Proceedings of the 2026 Computing Conference, Lecture Notes in Networks and Systems, vol. 1949, Springer, 2026
Journal refIntelligent Computing, Lecture Notes in Networks and Systems, vol. 1949, pp. 749-765, Springer, 2026
DOI：10.1007/978-3-032-24804-6_43
