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 model inspired by quantum transformer attention mechanisms. This model employs a swap-test-based correlation, together with a parameterized quantum circuit, to 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 points 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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• Machine learning
• Quantum circuits
• Quantum phase transitions