评测强化学习在量子电路搜索中的表现，发现无通用最优算法。
BenchRL-QAS: Benchmarking reinforcement learning algorithms for quantum architecture search
- 构建统一框架，对比9种强化学习算法在多种量子任务上的表现。
- 不同任务、比特数和噪声下算法表现差异大，验证‘无免费午餐’原则。
- 选对算法可提升量子分类器性能，适合量子机器学习研究者参考。
我们提出BenchRL-QAS，一个统一的基准测试框架，用于评估强化学习（RL）在2至8量子比特系统上多种变分量子算法任务中的表现。系统性地比较了9种不同的RL代理，涵盖基于值和策略梯度的方法，在变分本征值求解、量子态对角化、变分量子分类（VQC）和态制备等任务中，分别在无噪声和有噪声环境下进行测试。为确保公平比较，提出一种加权排名指标，综合考虑精度、电路深度、门数量和训练时间。结果表明，无单一RL方法在所有场景下均占优，性能依赖于任务类型、量子比特数和噪声条件，有力支持了强化学习在量子架构搜索中不存在通用最优解的‘无免费午餐’原则。作为副产品，发现精心选择的基于RL的VQC方法优于基线模型。BenchRL-QAS是目前最全面的基于强化学习的量子架构搜索基准，代码与实验数据已公开，便于复现与未来研究。
原文摘要 · Abstract (English)
We present BenchRL-QAS, a unified benchmarking framework for reinforcement learning (RL) in quantum architecture search (QAS) across a spectrum of variational quantum algorithm tasks on 2- to 8-qubit systems. Our study systematically evaluates 9 different RL agents, including both value-based and policy-gradient methods, on quantum problems such as variational eigensolver, quantum state diagonalization, variational quantum classification (VQC), and state preparation, under both noiseless and noisy execution settings. To ensure fair comparison, we propose a weighted ranking metric that integrates accuracy, circuit depth, gate count, and training time. Results demonstrate that no single RL method dominates universally, the performance dependents on task type, qubit count, and noise conditions providing strong evidence of no free lunch principle in RL-QAS. As a byproduct we observe that a carefully chosen RL algorithm in RL-based VQC outperforms baseline VQCs. BenchRL-QAS establishes the most extensive benchmark for RL-based QAS to date, codes and experimental made publicly available for reproducibility and future advances.
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