用少量数据实现量子设备间噪声模型迁移，提升跨设备纠错能力。
Few-Shot Cross-Device Transfer for Quantum Noise Modeling on Real Hardware
- 用残差网络学习源设备噪声映射，通过少量目标设备数据微调。
- 仅20个样本微调后KL散度降低28.6%，恢复零样本与本域差距的34.9%。
- 识别出控制-受控门误差是跨设备差异主因，适合量子硬件开发者参考。
在嘈杂中等规模量子（NISQ）时代，量子设备存在硬件特异性噪声，限制了通用纠错策略。本文探索迁移学习方法，将一个设备上学习的噪声模型应用于另一设备，仅需少量数据。基于两台IBM量子设备ibm_fez（源）和ibm_marrakesh（目标）构建真实硬件数据集，包含170个含噪与理想电路输出分布，并加入设备校准特征。在源设备上训练残差神经网络，将含噪结果映射为理想输出。零样本迁移测试中KL散度为1.6706（相比原域0.3014显著升高），体现设备特异性。使用K=20个微调样本后，KL降至1.1924，较零样本改善28.6%，恢复了零样本与本域之间差距的34.9%。消融分析表明，跨设备不匹配主要源于CX门误差，其次为读出误差。结果表明，量子噪声可仅用极少量样本学习并微调，为跨设备量子纠错提供可行路径。
原文摘要 · Abstract (English)
In the noisy intermediate-scale quantum (NISQ) regime, quantum devices contain hardware-specific noise sources which restrict device-invariant error mitigation strategies. We explore transfer learning approaches to apply noise models learned on one quantum device to a different device with the help of a small amount of data. We create a real-hardware dataset from two IBM quantum devices, ibm_fez (source) and ibm_marrakesh (target), comprising 170 noisy and ideal circuit output distributions, with device calibration features added. We train a residual neural network on the source device to map noisy to ideal outcomes. The zero-shot transfer test shows a KL divergence of 1.6706 (up from 0.3014), establishing device specificity. With K = 20 fine-tuning samples, KL drops to 1.1924 (28.6% improvement over zero-shot), recovering 34.9% of the gap between zero-shot and in-domain KL. Ablation studies reveal that the major cause of mismatches across devices is CX gate error, followed by readout error. The results show quantum noise can be learned and fine-tuned with minimal samples, and provide a plausible approach to cross-device quantum error mitigation.
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