Quantum Phase Recognition via Quantum Attention Mechanism
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Abstract (arXiv)
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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References · 17
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- 2018Barren plateaus in quantum neural network training landscapescited by 624
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- 2021Connecting ansatz expressibility to gradient magnitudes and barren plateauscited by 180
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- 2021Provably efficient machine learning for quantum many-body problemscited by 91
- 2022Quantum Vision Transformerscited by 48
5 more
- 2022Quantum Self-Attention Neural Networks for Text Classificationcited by 42
- 2024Quantum Mixed-State Self-Attention Networkcited by 11
- 2024Hybrid Quantum Vision Transformers for Event Classification in High Energy Physicscited by 10
- 2024Quantum Vision Transformers for Quark-Gluon Classificationcited by 7
- 2025HQViT: Hybrid Quantum Vision Transformer for Image Classificationcited by 7
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