发表机构
University of Bonn; European Space Agency; Lamarr Institute（波恩大学; 欧洲空间局; 拉马尔研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对DQAS在量子硬件上测量成本高的问题，提出一种测量缩减方案，在不改变优化目标的前提下，将梯度测量成本降低约39%-41%，并在3-SAT和MaxCut基准上验证了其有效性。
AI 中文摘要
可微量子架构搜索（DQAS）是一种用于量子电路自动化设计的很有前景的框架，尤其适用于变分量子优化算法。然而，其在实际量子硬件上的部署受到优化过程中所需的大量电路测量的限制，使得硬件执行成本高昂。在这项工作中，我们证明，对于一大类组合优化问题和常用的旋转门参数化，在不改变优化目标的情况下，DQAS的测量成本可以显著降低。我们从理论上推导了所提出的测量缩减方案，并在3-SAT和MaxCut基准问题上进行了实验验证。我们的方法将所需的梯度测量成本降低了约39%至41%，同时仅引入可忽略的经典后处理开销，从而降低了在量子硬件上执行DQAS的实际成本。
英文摘要
Differentiable quantum architecture search (DQAS) is a promising framework for the automated design of quantum circuits, particularly for variational quantum optimization algorithms. However, its practical deployment on quantum hardware is limited by the large number of circuit measurements required during optimization, making hardware execution costly. In this work, we show that for a broad class of combinatorial optimization problems and commonly used rotational gate parameterizations, the measurement cost of DQAS can be significantly reduced without changing the optimization objective. We derive the proposed measurement reduction scheme theoretically and validate it experimentally on 3-SAT and MaxCut benchmark problems. Our approach reduces the requested gradient measurement cost by about 39 to 41% while introducing only negligible classical post-processing overhead, lowering the practical cost of executing DQAS on quantum hardware.
Comments6 pages, 2 figures. Accepted at the 2026 IEEE 2nd International Conference on Quantum Artificial Intelligence (QAI). Code and data: https://github.com/Newida/ME-DQAS
