基于大语言模型驱动的量子最优控制跨范式设计
LLM-Driven Cross-Paradigm Design for Quantum Optimal Control
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中文总结 AI 辅助
研究针对量子最优控制实际应用的瓶颈，提出由大语言模型驱动的QOC-Workbench工作流程，可跨范式设计协议，能自主解析文献、积累控制模式，在多场景验证中表现出色，建立了自主量子控制的跨范式方法。
中文摘要 AI 辅助
量子最优控制是绝热量子计算、量子退火和量子态工程的基础，但实际应用受硬件限制和设计协议需大量专家工作的瓶颈。为此引入QOC-Workbench，这是一种可审计的、由大语言模型驱动的工作流程，能作为自动量子协科学家进行跨范式协议设计。大语言模型超越传统数值优化器，自主解析文献、提出假设并编写代码验证。通过跨任务积累控制模式支持跨范式设计，在三个不同设置中验证，能发现优于文献基线的控制方法，解决变分反绝热驱动的计算瓶颈，建立了自主量子控制的跨范式方法。
英文摘要
Quantum optimal control (QOC) underpins adiabatic quantum computation, quantum annealing, and quantum state engineering, yet practical deployment is fundamentally bottlenecked by strict hardware constraints and substantial expert effort required to design protocols for each problem instance. To overcome this, we introduce QOC-Workbench, an auditable, large language model (LLM)-driven workflow that acts as an automated quantum co-scientist for cross-paradigm protocol design. Going beyond traditional numerical optimizers that merely tune parameters within a fixed formula, the LLM autonomously parses physics literature, proposes structural hypotheses, and writes code to validate them by direct simulation. This workflow supports cross-paradigm design by accumulating control motifs across tasks. We demonstrate this approach across three distinct settings: Case 1, Rydberg-atom maximum-independent-set arrays; Case 2, interacting XXZ spin chains; and Case 3, random transverse-field Ising models. In Cases 1 and 2, the workflow autonomously discovers hardware-compliant auxiliary controls, target catalysts, and schedule deformations that outperform literature baselines. In Case 3, it addresses the computational bottleneck of variational counterdiabatic driving by escalating from per-instance optimization to an amortized graph-neural-network generator, successfully transferring learned coefficient paths to larger unseen systems. By actively bridging the gap between theoretical algorithms and experimental restrictions across distinct control paradigms and Hamiltonian families, QOC-Workbench establishes a continuously evolving, cross-paradigm methodology for autonomous quantum control.
