发表机构
Pacific Northwest National Laboratory; University of Michigan（太平洋西北国家实验室; 密歇根大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出RL-Adapt，一种基于单次强化学习的自适应耗散算法，通过优化辅助量子比特频率和相互作用算子选择，无需系统频谱先验知识即可加速基态制备并提升收敛鲁棒性。
AI 中文摘要
耗散算法通过模拟量子系统与大型低温热环境接触时的自然热化过程，来解决基态制备问题。该环境可以通过一个具有可变能隙的辅助量子比特高效模拟，该辅助量子比特反复与系统量子比特耦合以生成耗散通道，并在每次相互作用后重置。本文提出了一种耗散算法的自适应实现方案RL-Adapt，该方案利用单次强化学习来优化辅助量子比特频率和系统-浴相互作用算子的选择，以最大化能量耗散，且无需依赖系统频谱的先验知识。这种自适应实现显著缩短了收敛时间，并且即使对于使用非自适应、均匀随机算子和辅助量子比特频率选择无法收敛的非理想算子池，也能成功找到基态。
英文摘要
Dissipative algorithms approach the problem of ground state preparation by mimicking the natural thermalization of a quantum system in contact with a large, low-temperature thermal environment. The environment can be efficiently simulated by a single ancilla qubit with a variable energy gap that is repeatedly coupled to the system qubits to generate a dissipative channel, and then reset after each interaction. Here we present an adaptive implementation of the dissipative algorithm, RL-Adapt, that uses single-shot reinforcement learning to optimize the selection of ancilla frequency and system-bath interaction operator to maximize energy dissipation without relying on a priori knowledge of the system spectrum. The adaptive implementation results in significantly reduced convergence times and can successfully find the ground state even for non-ideal operator pools that fail to converge using non-adaptive, uniform random operator and ancilla frequency selection.
