arXiv 论文：用 LLM 直接生成变分量子电路参数
Programming Quantum Circuit Parameters with Large Language Models
搞量子计算的朋友可以看看这篇。它测了冻结 LLM 在三种哈密顿量上生成电路参数的效果：结构化问题上有戏，随机问题上不如 Adam 优化。结论很实在，能帮你判断该不该把 LLM 用进变分算法流程里。
一篇 arXiv 论文研究了冻结的通用 LLM 能否从电路与哈密顿量的符号描述直接生成完整的变分量子电路参数向量，并提供可选的迭代能量反馈。实验覆盖三类对象：通用随机哈密顿量、结构化 parent Hamiltonian 以及分子体系。
结果显示，面对通用随机哈密顿量，LLM 生成的参数能量误差大于传统 Adam 优化，且加入能量反馈后改进有限；而在结构化哈密顿量上，LLM 能给出低能量的候选参数配置，说明问题中可识别的物理与数学结构是参数推断成立的关键。论文还指出，直接让 LLM 预测最低能量可能违反哈密顿量谱下界，而生成电路参数后可以独立计算物理上可实现能量期望值。
在盲电路解释实验中，LLM 甚至能在无法识别底层量子算法的情况下预测测量结果。对量子计算研究者来说，这项工作划清了 LLM 参数编程的适用边界，也强调了独立可验证预测的重要性。
Programming Quantum Circuit Parameters with Large Language Models
Large language models (LLMs) have recently demonstrated the ability to generate parameters for variational quantum circuits without conventional optimization. However, the conditions under which this approach is effective remain unclear. Here, we investigate a frozen, general-purpose LLM that generates complete circuit parameter vectors from symbolic circuit and Hamiltonian descriptions, with optional iterative energy feedback. We evaluate the approach on general random Hamiltonians, structured parent Hamiltonians, and molecular systems. For general Hamiltonians, LLM-generated parameters exhibit larger energy errors than conventional Adam optimization, while additional feedback generally provides limited improvement. In contrast, for structured Hamiltonians, the LLM can generate promising low-energy parameter configurations, suggesting that identifiable physical and mathematical structure facilitates parameter inference. We also show that direct LLM predictions of minimum energies can violate the Hamiltonian spectral lower bound, whereas generated circuit parameters allow independent evaluation of physically realizable energy expectation values. Finally, we investigate blind circuit interpretation and find that the LLM can predict measurement outcomes even when it fails to identify the underlying quantum algorithm. These findings highlight the role of problem structure, the limitations of iterative LLM optimization, and the importance of independently verifiable predictions in quantum parameter programming.
This report is categorized under AITOP's "Papers" desk. Key entities and technical topics under continuous tracking include LLM, 量子计算, 变分量子电路, 哈密顿量.
Source attribution: curated from arXiv cs.LG (@Tiago de Souza Farias, Caio Moraes Porto, Matheus da Silva Fonseca, Nicolas Armando Cabrera Carpi, Anderson de Araujo Oliveira, Celso Jorge Villas-Boas).