用于量子储层架构设计的混合大语言模型引导搜索
Hybrid LLM-Guided Search for Quantum Reservoir Architecture Design
- QuantumAI Lab, Fractal Analytics India（量子AI实验室，Fractal Analytics印度）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
研究量子储层计算架构设计问题，提出基于模拟器的基准测试\method，比较五种策略，其中\hybrid策略表现出色，在多任务中优于随机搜索，证明生成模型嵌入混合搜索循环可作高级控制器。
AI中文摘要：
量子储层计算（QRC）使用固定量子动力学作为高维时间特征映射，仅训练轻量级经典读出。其性能强烈依赖架构选择。本文介绍了一种基于模拟器的基准测试\method，将QRC设计表述为受限黑箱架构搜索，评估大语言模型能否作为搜索问题的提议控制器。该基准在相同评估预算下比较了五种策略。在NARMA10、Mackey-Glass预测和时间奇偶性任务中，\hybrid是最稳定的策略，在25次评估预算及三个种子下，\hybrid在所有任务上优于随机搜索。结果表明生成模型嵌入有效、可重复的混合搜索循环时可作为有用的高级控制器。
英文摘要：
Quantum reservoir computing (QRC) uses fixed quantum dynamics as a high-dimensional temporal feature map and trains only a lightweight classical readout. QRC is attractive for near-term quantum machine learning, but its performance depends strongly on architecture choices such as input encoding, reservoir depth, entanglement topology, measurement features, state-reset policy, feature construction, and readout regularization. We introduce \method, a simulator-based benchmark that formulates QRC design as constrained black-box architecture search and evaluates whether large language models can act as proposal controllers for this search problem. The benchmark compares five policies under identical evaluation budgets: random search, evolutionary search, Bayesian/TPE optimization, a feedback-based LLM agent, and \hybrid, which combines LLM proposals with memory, mutation, crossover, duplicate avoidance, and exploration. On NARMA10, Mackey-Glass forecasting, and temporal parity, \hybrid{} is the most consistent policy: it ranks first on NARMA10 and temporal parity and second on Mackey-Glass, narrowly behind evolutionary search. Under a 25-evaluation budget and three seeds, \hybrid{} improves over random search on all tasks, including a 23.6\% relative reduction in Mackey-Glass error. The results do not show that LLMs are universal QRC optimizers; rather, they show that generative models can be useful high-level controllers when embedded inside validated, reproducible hybrid search loops.
