让大模型理解量子门操作，实现自然语言控制电路合成。
Aligning Quantum Operators with Large Language Models
- 将量子幺正算符映射到大模型隐空间，统一处理量子与语言输入。
- 在保罗旋转门集上达到顶尖合成效果，训练数据增多时性能持续提升。
- 支持自然语言指定新门约束，适合量子算法设计与编译研究者。
大语言模型能否理解并推理量子算符？尽管在数学和符号推理方面表现卓越，但现有大模型仍无法感知量子表示（如酉矩阵）。本文提出一种方法，将酉算符映射至大模型的隐空间，实现量子与语言输入的统一建模。我们在保罗旋转门集合上的克利福德+T电路综合任务中验证该方法，模型性能媲美当前最优方案，且随训练数据增长持续提升，无饱和迹象。该方法还支持语言条件合成，可直接通过自然语言指定训练中未见的门约束。本工作为构建能原生理解与推理量子操作的量子感知基础模型开辟路径，对量子编译与算法发现具有深远意义。
原文摘要 · Abstract (English)
Can Large Language Models (LLMs) understand and reason about quantum operators? Despite their remarkable capabilities in mathematics and symbolic reasoning, LLMs remain inherently blind to quantum representations such as unitary matrices. In this work, we take a step toward bridging this gap by introducing an approach that maps unitary operators into the latent space of an LLM, enabling unified modeling over quantum and linguistic inputs. We instantiate this idea on Clifford+T circuit synthesis over a Pauli rotation gate set, where our model achieves results competitive with state-of-the-art methods and scales consistently with training data, with no signs of saturation. Our approach further enables language-conditioned synthesis, allowing gate constraints unseen during training to be specified directly in natural language. This work suggests a path toward quantum--aware foundation models that can natively interpret and reason about quantum operations, which could have broader implications reaching across quantum compilation and algorithm discovery.
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