用量子注意力机制识别多体系统相变，数据少且稳定。
Quantum Phase Recognition via Quantum Attention Mechanism
- 用交换测试和参数化量子电路实现量子注意力
- 9~15比特系统下用少于100组数据达到高准确率
- 能捕捉相变特征和物理尺度，适合小样本复杂系统
多体系统中的量子相变由复杂的关联结构决定，传统方法在大系统中面临计算挑战。为此，我们提出一种混合量子-经典注意力模型，利用交换测试与参数化量子电路实现注意力机制，从量子态中提取关联信息并完成基态分类。在9和15量子比特的簇-伊辛模型上进行基准测试，仅需少于100组训练数据即实现高分类精度，并对训练集变化具有鲁棒性。进一步分析表明，该模型成功捕获了相敏感特征和特征物理长度尺度，为复杂多体系统中的量子相识别提供了一种可扩展、数据高效的方法。
原文摘要 · Abstract (English)
Quantum phase transitions in many-body systems are fundamentally characterized by complex correlation structures, which pose computational challenges for conventional methods in large systems. To address this, we propose a hybrid quantum-classical attention model. This model uses an attention mechanism, realized through swap tests and a parameterized quantum circuit, to extract correlations within quantum states and perform ground-state classification. Benchmarked on the cluster-Ising model with system sizes of 9 and 15 qubits, the model achieves high classification accuracy with less than 100 training data and demonstrates robustness against variations in the training set. Further analysis reveals that the model successfully captures phase-sensitive features and characteristic physical length scales, offering a scalable and data-efficient approach for quantum phase recognition in complex many-body systems.
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