🤖 AI Summary
This work addresses the challenge that the performance of quantum reservoir computing (QRC) is highly sensitive to architectural design, yet its vast hyperparameter space lacks efficient automated design methods. The authors formulate QRC architecture search as a constrained black-box optimization problem and propose a novel hybrid search framework that, for the first time, integrates a large language model (LLM) as a high-level controller within a reproducible search loop. This framework leverages memory mechanisms, mutation, crossover, deduplication, and exploration strategies to efficiently guide architecture generation without gradient information. Experimental results on NARMA10, Mackey-Glass prediction, and temporal parity tasks demonstrate that the method significantly outperforms random search within a budget of 25 evaluations, achieving a 23.6% relative error reduction on Mackey-Glass, thereby validating the potential of generative models in orchestrating quantum machine learning architectures.
📝 Abstract
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.