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.

翻译：大型语言模型（LLM）能否理解并推理量子算子？尽管它们在数学和符号推理方面展现出卓越能力，LLM对幺正矩阵等量子表示本质上仍存在盲点。本研究通过提出一种将幺正算子映射至LLM潜在空间的方法，向弥合这一差距迈出一步，从而实现对量子与语言输入的联合建模。我们以Pauli旋转门集上的Clifford+T电路综合为例实现该构想，实验表明：该模型在性能上可与目前最先进方法相媲美，且随训练数据量增长保持稳定扩展趋势，未出现饱和迹象。本方法进一步实现了语言条件式综合，使得训练过程中未见的门约束可直接通过自然语言指定。这项工作为构建原生理解并推理量子操作的量子感知基础模型开辟了路径，对量子编译与算法发现等领域具有深远潜在影响。