发表机构
McMaster University; Université de Montréal; Brockhouse Institute for Materials Research, McMaster University（麦克马斯特大学; 蒙特利尔大学; 麦克马斯特大学布罗克豪斯材料研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出奖励驱动的生成框架Grinch，自底向上采样光学图，无需训练数据，实现多体纠缠态及非局域门的高保真设计，并引入硬件约束探索替代拓扑。
AI 中文摘要
设计量子光学实验需要在离散的电路拓扑结构和连续参数空间中进行搜索，且同一目标态往往存在多种实现方式。基于图的方法通常通过优化稠密图并将其剪枝为单一电路来解决该问题。我们提出了Grinch，一种自底向上的奖励驱动生成框架，它直接从基于保真度的奖励中学习采样光学图，无需依赖预先存在的训练数据集。我们在多体纠缠态、图态和簇态以及非局域光子门目标上展示了该框架，获得了多个高保真光学图。对于无法由图精确生成的状态，我们识别出渐近解，以及需要两个辅助比特的非局域Toffoli门构造。我们的工作引入了硬件约束目标，当辅助节点之间的连接受到限制时，这些目标导致了CNOT和Toffoli门的替代构造。这些发现展示了一种奖励驱动的量子光学逆设计方法，在搜索过程中直接纳入连通性约束，探索替代电路拓扑结构。
英文摘要
Designing quantum optical experiments requires searching over discrete circuit topologies and continuous parameters, often with multiple realizations of the same target state. Graph-based methods commonly address this problem by optimizing a dense graph and pruning it toward a single circuit. We introduce \texttt{Grinch}, a bottom-up reward-driven generative framework that learns to sample optical graphs directly from fidelity-based rewards without relying on a pre-existing training dataset. We demonstrate the framework on multipartite entangled states, graph and cluster states, and nonlocal photonic-gate targets, obtaining multiple high-fidelity optical graphs. We identify asymptotic solutions for states that cannot be generated exactly by graphs, as well as nonlocal Toffoli gate constructions requiring two ancillas. Our work presents hardware-constraint objectives, which lead to alternative constructions of CNOT and Toffoli gates when connections between ancilla nodes are restricted. These findings illustrate a reward-driven approach to quantum optical inverse design, exploring alternative circuit topologies while directly incorporating connectivity constraints into the search process.
Comments18 pages, 8 figures, 4 tables
