用深度学习减轻量子计算噪声，提升结果准确性。
Deep Learning Approaches to Quantum Error Mitigation
- 采用序列到序列的注意力模型处理量子输出分布
- 在5量子比特真实设备上，误差缓解效果优于基线方法
- 跨设备迁移有效，无需重新训练即可通用
我们系统研究了深度学习方法在噪声量子电路输出概率分布误差缓解中的应用。比较了从全连接网络到Transformer等多种架构，发现基于注意力的序列到序列模型在我们的数据集上表现最优。该方法在模拟数据和来自IBM超导量子处理器（QPU）的真实设备数据（最多5量子比特）上，均能持续生成更接近理想输出的缓解后分布。在不同电路深度下，本方法均优于其他基准误差缓解技术。通过一系列消融实验，分析了输入特征（电路、设备属性、噪声输出统计）对性能的影响，以及跨电路族的数据集泛化能力，并测试了在不同IBM QPU上的迁移学习效果。结果显示，相同架构下在相似设备间迁移表现良好，无需完全重新训练模型。
原文摘要 · Abstract (English)
We present a systematic investigation of deep learning methods applied to quantum error mitigation of noisy output probability distributions from measured quantum circuits. We compare different architectures, from fully connected neural networks to transformers, and we test different design/training modalities, identifying sequence-to-sequence, attention-based models as the most effective on our datasets. These models consistently produce mitigated distributions that are closer to the ideal outputs when tested on both simulated and real device data obtained from IBM superconducting quantum processing units (QPU) up to five qubits. Across several different circuit depths, our approach outperforms other baseline error mitigation techniques. We perform a series of ablation studies to examine: how different input features (circuit, device properties, noisy output statistics) affect performance; cross-dataset generalization across circuit families; and transfer learning to a different IBM QPU. We observe that generalization performance across similar devices with the same architecture works effectively, without needing to fully retrain models.
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