用经典模型替代量子测量，实现高效量子误差缓解。
Sample-efficient quantum error mitigation via classical learning surrogates
- 用经典学习模型替代量子电路测量，完成零噪声外推
- 100量子比特任务中保持高精度，测量开销恒定不变
- 适合大规模参数化量子电路，可推广至其他纠错方法
近期量子处理器的实际应用受噪声严重制约。量子误差缓解（QEM）技术可在低量子开销下提升计算保真度，而全尺度量子纠错仍遥不可及。然而，现有QEM方法在处理由经典输入参数化的量子电路族时，测量开销巨大。本文聚焦广泛使用的零噪声外推（ZNE）技术，提出基于经典学习代理的S-ZNE方法，将整个电路族的ZNE过程完全转移到经典侧进行。与传统ZNE随电路数线性增长测量开销不同，S-ZNE仅需恒定测量开销，具备更优可扩展性。理论分析表明，多数实际场景下其精度与传统方法相当，100量子比特的基态能量和量子计量任务数值实验也验证了其有效性。该方法为其他量子误差缓解协议提供了可扩展的通用范式。
原文摘要 · Abstract (English)
The pursuit of practical quantum utility on near-term quantum processors is critically challenged by their inherent noise. Quantum error mitigation (QEM) techniques are leading solutions to improve computation fidelity with relatively low qubit-overhead, while full-scale quantum error correction remains a distant goal. However, QEM techniques incur substantial measurement overheads, especially when applied to families of quantum circuits parameterized by classical inputs. Focusing on zero-noise extrapolation (ZNE), a widely adopted QEM technique, here we devise the surrogate-enabled ZNE (S-ZNE), which leverages classical learning surrogates to perform ZNE entirely on the classical side. Unlike conventional ZNE, whose measurement cost scales linearly with the number of circuits, S-ZNE requires only constant measurement overhead for an entire family of quantum circuits, offering superior scalability. Theoretical analysis indicates that S-ZNE achieves accuracy comparable to conventional ZNE in many practical scenarios, and numerical experiments on up to 100-qubit ground-state energy and quantum metrology tasks confirm its effectiveness. Our approach provides a template that can be effectively extended to other quantum error mitigation protocols, opening a promising path toward scalable error mitigation.
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