arXiv：cs.LG· Lukas Thei{\ss}inger, Thore Gerlach, Christian Bauckhage·· 4 小时前AI 评分31
可微量子架构搜索（DQAS）的测量高效优化：面向组合优化的测量成本降低方案
Measurement-Efficient Differentiable Quantum Architecture Search for Combinatorial Optimization
AI 导读
研究提出一种测量缩减方案，可在不改变优化目标的前提下，将可微量子架构搜索（DQAS）在组合优化问题中的梯度测量成本降低约 39% 至 41%，且仅引入可忽略的经典后处理开销。该方案针对常用旋转门参数化进行了理论推导，并在 3-SAT 与 MaxCut 基准问题上完成实验验证，论文已被 2026 IEEE QAI 会议接收。
正文
Abstract: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.
| Comments: | 6 pages, 2 figures. Accepted at the 2026 IEEE 2nd International Conference on Quantum Artificial Intelligence (QAI). Code and data: this https URL |
| Subjects: | Quantum Physics (quant-ph); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.10351 [quant-ph] |
| (or arXiv:2610.10351v1 [quant-ph] for this version) | |
| https://doi.org/10.48550/arXiv.2610.10351 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Lukas Theißinger [view email]
[v1]
Wed, 7 Oct 2026 16:27:15 UTC (156 KB)
来源：arXiv：cs.LG · arxiv.org