Quantum-Based Self-Attention Mechanism for Hardware-Aware Differentiable Quantum Architecture Search

• URL: http://arxiv.org/abs/2512.02476v1
• Date: Tue, 02 Dec 2025 07:07:10 GMT
• Title: Quantum-Based Self-Attention Mechanism for Hardware-Aware Differentiable Quantum Architecture Search
• Authors: Yuxiang Liu, Sixuan Li, Fanxu Meng, Zaichen Zhang, Xutao Yu,
• Abstract summary: We introduce the Quantum-Based Self-Attention for Differentiable Quantum Architecture Search (QBSA-DQAS)<n>The framework employs a two-stage quantum self-attention module that computes contextual dependencies by mapping architectural parameters through quantum circuits.<n>A post-search optimization stage applies gate commutation, fusion, and elimination to reduce circuit complexity.
• Score: 21.477208706334608
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The automated design of parameterized quantum circuits for variational algorithms in the NISQ era faces a fundamental limitation, as conventional differentiable architecture search relies on classical models that fail to adequately represent quantum gate interactions under hardware noise. We introduce the Quantum-Based Self-Attention for Differentiable Quantum Architecture Search (QBSA-DQAS), a meta-learning framework featuring quantum-based self-attention and hardware-aware multi-objective search. The framework employs a two-stage quantum self-attention module that computes contextual dependencies by mapping architectural parameters through parameterized quantum circuits, replacing classical similarity metrics with quantum-derived attention scores, then applies position-wise quantum transformations for feature enrichment. Architecture search is guided by a task-agnostic multi-objective function jointly optimizing noisy expressibility and Probability of Successful Trials (PST). A post-search optimization stage applies gate commutation, fusion, and elimination to reduce circuit complexity. Experimental validation demonstrates superior performance on VQE tasks and large-scale Wireless Sensor Networks. For VQE on H$_2$, QBSA-DQAS achieves 0.9 accuracy compared to 0.89 for standard DQAS. Post-search optimization reduces discovered circuit complexity by up to 44% in gate count and 47% in depth without accuracy degradation. The framework maintains robust performance across three molecules and five IBM quantum hardware noise models. For WSN routing, discovered circuits achieve 8.6% energy reduction versus QAOA and 40.7% versus classical greedy methods, establishing the effectiveness of quantum-native architecture search for NISQ applications.

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