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Neural Networks

Quantum mixed-state self-attention network

Chen Fu, Qinglin Zhao, Li Feng, Chuangtao Chen, Yangbin Lin, Jianhong Lin

Published Jan 8, 2025• 28 citations
Publisher
DOI
10.1016/j.neunet.2025.107123

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Attention mechanisms have revolutionized natural language processing. Combining them with quantum computing aims to further advance this technology. This paper introduces a novel Quantum Mixed-State Self-Attention Network (QMSAN) for natural language processing tasks. Our model leverages quantum computing principles to enhance the effectiveness of self-attention mechanisms. QMSAN uses a quantum attention mechanism based on mixed state, allowing for direct similarity estimation between queries and keys in the quantum domain. This approach leads to more effective attention coefficient calculations. We also propose an innovative quantum positional encoding scheme, implemented through fixed quantum gates within the circuit, improving the model's ability to capture sequence information without additional qubit resources. In numerical experiments of text classification tasks on public datasets, QMSAN outperforms Quantum Self-Attention Neural Network (QSANN). Furthermore, we demonstrate QMSAN's robustness in different quantum noise environments, highlighting its potential for near-term quantum devices.
Abstract Particle flow classification is essential in high-energy and nuclear physics. This work proposes the quantum complete graph self-attention network (QCGAT), which selects particles with high transverse momentum and uses a quantum self-attention mechanism for feature extraction. Unlike previous methods, the proposed QCGAT framework introduces a non-measurement quantum self-attention mechanism within an entangled quantum network, enabling the direct embedding of attention coefficients into quantum state amplitudes and thereby avoiding quantum-to-classical conversion. Furthermore, this work introduces a quantum distributed dimensionality reduction architecture that specifically enables tunable cross-scale data adaptation. Experiments on the Top Quark Tagging dataset show that QCGAT achieves 83.5% accuracy and 88.7% AUC, surpassing hybrid models by 1% and 0.4%. Compared with traditional quantum encoder baseline, it improves accuracy by 0.7%; when compared with traditional quantum encoder baselines having similar or fewer parameter counts, it achieves up to 1.6% higher accuracy. These results highlight the effectiveness and potential of QCGAT in quantum machine learning for particle physics.