发表机构
Delft University of Technology; QuTech; Indian Institute of Technology (BHU); Halmstad University（代尔夫特理工大学; QuTech量子计算研究机构; 印度理工学院（贝拿勒斯印度教大学）; 哈尔姆斯塔德大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
GenQAS通过张量网络引导的强化学习与生成式回放，缓解量子架构搜索中的样本匮乏，提高成功概率并减少资源消耗。
AI 中文摘要
强化学习（RL）可以自动化量子架构搜索，但当有用电路轨迹在快速扩展的搜索空间中变得稀少时，其可扩展性受到限制。现有的回放机制重用观察到的转移；所提出的学习模型从真实状态-动作种子生成额外的预测一步转移。在此，我们引入GenQAS，一种张量网络引导的强化学习框架，结合固定矩阵乘积态热启动与优先生成式回放。一个学习的局部转移模型按需生成合成电路转移，并在Double Deep Q-Network更新期间将其与真实经验混合。在随机探索分析下，近基态电路占据可访问状态空间中快速收缩的区域。我们研究真实数据锚定的合成回放是否能改善该机制下的有效训练信号。在6到12量子比特的化学哈密顿量基准测试中，GenQAS提高了固定预算的成功概率，并以有竞争力的能量误差识别出紧凑电路。在12量子比特时，它将最终成功概率比被动回放提高高达$7.0\ imes$。在15量子比特横向场Ising模型上，GenQAS将成功概率从$12\%$提高到$21\%$。在噪声6量子比特BeH$_2$转移实验中，生成式回放将达到化学精度的步骤减少了$92.7\%$。这些结果表明，生成式回放可以缓解量子架构搜索中的样本匮乏，并支持更资源高效的电路发现。
英文摘要
Reinforcement learning (RL) can automate quantum architecture search, but its scalability is limited when useful circuit trajectories become rare in the rapidly expanding search space. Existing replay mechanisms reuse observed transitions; the proposed learned model produces additional predicted one step transitions from real state-action seeds. Here we introduce GenQAS, a tensor network-guided RL framework that combines a fixed matrix product state warm-start with prioritized generative replay. A learned local transition model generates synthetic circuit transitions on demand and mixes them with real experience during Double Deep Q-Network updates. Under a random exploration analysis, near ground state circuits occupy a rapidly shrinking region of the accessible state space. We investigate whether real data anchored synthetic replay can improve the effective training signal in this regime. Across chemical Hamiltonian benchmarks from 6 to 12 qubits, GenQAS improves fixed-budget success probability and identifies compact circuits at competitive energy error. At 12 qubits, it improves final success probability by up to $7.0\times$ over passive replay. On a 15-qubit transverse field Ising model, GenQAS increases success probability from $12\%$ to $21\%$. In a noisy 6-qubit BeH$_2$ transfer experiment, generative replay reduces the steps to chemical accuracy by $92.7\%$. These results show that generative replay can mitigate sample starvation in quantum architecture search and support more resource efficient circuit discovery.
CommentsGenQAS: 38 pages, 7 figures, 2 tables and 1 algorithm in main text
