基于量子的自注意力机制用于硬件感知的可微量子架构搜索
Quantum-Based Self-Attention Mechanism for Hardware-Aware Differentiable Quantum Architecture Search
AI总结：
本研究提出QBSA-DQAS框架，通过量子自注意力机制和硬件感知多目标搜索，提升NISQ时代量子电路设计的性能和效率。
摘要：
在NISQ时代，为变分算法设计参数化量子电路的自动化设计面临根本性限制，因为传统可微架构搜索依赖于经典模型，无法充分表示量子门交互在硬件噪声下的情况。我们引入了量子基于自注意力机制的可微量子架构搜索（QBSA-DQAS），这是一种具有量子自注意力和硬件感知多目标搜索的元学习框架。该框架采用两阶段量子自注意力模块，通过参数化量子电路将架构参数映射以计算上下文依赖性，用量子衍生的注意力分数取代传统相似性度量，然后应用位置-wise量子变换进行特征丰富。架构搜索由任务无关的多目标函数引导，联合优化噪声表达性和成功试验的概率（PST）。搜索后优化阶段应用门交换、融合和消除以减少电路复杂性。实验验证显示在VQE任务和大规模无线传感器网络上表现优异。对于H₂上的VQE，QBSA-DQAS达到0.9的准确率，相比标准DQAS的0.89。搜索后优化阶段在门数上将发现的电路复杂性减少高达44%，深度减少47%，而无精度下降。该框架在三种分子和五个IBM量子硬件噪声模型上保持稳健性能。对于WSN路由，发现的电路在能量减少方面相比QAOA达到8.6%，相比经典贪心方法达到40.7%，证明了量子原生架构搜索在NISQ应用中的有效性。
英文摘要：
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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