Abstract
We introduce BenchRL-QAS, a unified benchmarking framework for systematically evaluating reinforcement learning (RL) algorithms in quantum architecture search (QAS) across diverse variational quantum algorithm tasks and system sizes ranging from 2- to 8-qubit. Our study benchmarks nine RL agents including both value-based and policy-gradient methods on representative quantum problems such as variational quantum eigensolver, variational quantum state diagonalization, quantum classification, and state preparation, spanning both noiseless and realistic noisy regimes. We propose a weighted ranking metric that balances accuracy, circuit depth, gate count, and computational efficiency, enabling fair and comprehensive comparison. Our results first reveal that RL-based quantum classifier outperforms baseline variational classifiers. Then we conclude that no single RL algorithm is universally optimal when considering a set of QAS tasks; algorithmic performance is highly context-dependent, varying with task structure, qubit count, and noise. This empirical finding provides strong evidence for the "no free lunch" principle in RL-based quantum circuit design and highlights the necessity of tailored algorithm selection and systematic benchmarking for advancing quantum circuit synthesis. This work represents the most comprehensive RL-QAS benchmarking effort to date, and BenchRL-QAS along with all experimental data are made publicly available to support reproducibility and future research this https URL.
Abstract (translated)
我们介绍了BenchRL-QAS，这是一个统一的基准框架，用于系统地评估量子架构搜索（QAS）中强化学习（RL）算法在各种变分量子算法任务和从2到8个量子位的不同规模系统的性能。我们的研究对包括基于价值的方法和策略梯度方法在内的九种RL代理进行了基准测试，这些代理在具有代表性的量子问题上进行评估，例如变分量子特征值求解器、变分量子态对角化、量子分类以及状态准备，在无噪声和现实中的有噪声环境中均进行了考虑。我们提出了一项加权排名指标，该指标平衡了准确性、电路深度、门数和计算效率，以实现公平且全面的比较。我们的研究首次揭示基于RL的量子分类器优于基线变分分类器的表现。然后我们得出结论：当考虑到一组QAS任务时，并没有一种通用的最佳RL算法；其性能高度依赖于具体环境，随着任务结构、量子位数量以及噪声程度的不同而变化。这一实证发现为基于强化学习的量子电路设计中的“不存在免费午餐”原则提供了强有力的支持，并强调了定制算法选择和系统基准测试对于推进量子电路合成的重要性。这项工作代表了迄今为止最全面的RL-QAS基准测试努力，BenchRL-QAS以及所有实验数据均向公众开放以支持可重复性和未来研究（请访问此链接获取更多信息：此 https URL）。
URL
https://arxiv.org/abs/2507.12189
