发表机构
Nanjing University of Science and Technology; Inner Mongolia University of Science and Technology（南京理工大学; 内蒙古科技大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对变分量子算法中量子架构搜索评估成本高的问题，提出基于ZX演算拓扑复用的噪声感知搜索框架ZX-QAS，通过三元格雷码编码和拓扑复用机制，在基态能量估计和伊辛模型任务中实现稳定收敛并显著降低评估成本。
AI 中文摘要
变分量子算法是在含噪声中等规模量子设备上处理量子化学和多体物理问题的主要方法。其性能在很大程度上取决于参数化量子电路的结构。量子架构搜索（QAS）可以自动化拟设（ansatz）设计。然而，它需要反复训练和评估大量候选电路，导致高昂的评估成本。在这项工作中，我们提出了一种基于ZX演算拓扑复用（ZX-QAS）的噪声感知量子架构搜索框架。该框架采用三元格雷码映射对搜索空间进行编码，并将噪声感知量子神经网络与ZX演算拓扑复用机制相结合。通过基态能量估计任务和噪声条件下的一维横向场伊辛模型任务验证了该框架的有效性。结果表明，ZX-QAS表现出稳定的收敛性，并显著降低了噪声条件下昂贵评估的成本。
英文摘要
Variational quantum algorithm is a leading approach for quantum chemistry and many-body physics on noisy intermediate-scale quantum devices. The performance depends strongly on the structure of the parameterized quantum circuits. Quantum architecture search (QAS) can automate ansatz design. However, it requires repeated training and evaluation of many candidate circuits, leading to high evaluation cost. In this work, we propose a noise-aware quantum architecture search framework based on ZX-calculus topological reuse (ZX-QAS). The framework encodes the search space with a ternary Gray-code mapping and integrates a noise-aware quantum neural network with a ZX-calculus topological reuse mechanism. The effectiveness of the framework is validated through ground-state energy estimation tasks and one-dimensional transverse-field Ising model tasks under noisy conditions. The results show that the ZX-QAS exhibits stable convergence and remarkably reduces the cost of expensive evaluations under noisy conditions.
