量子算符与大型语言模型的对齐

摘要

大型语言模型（LLM）能否理解并推理量子算符？尽管LLM在数学和符号推理方面展现出卓越能力，但它们对幺正矩阵等量子表示形式本质上仍存在认知盲区。本研究通过提出一种将幺正算符映射至LLM潜在空间的方法，在弥合这一鸿沟方面迈出了重要一步，从而实现对量子输入与语言输入的统一建模。我们以泡利旋转门集上的Clifford+T电路合成为实例验证了这一构想，该模型不仅取得了与现有最优方法相匹敌的结果，而且其性能随训练数据量呈线性增长，未呈现任何饱和迹象。该方法进一步实现了语言条件驱动的合成能力，使得训练阶段未见过的门约束可直接通过自然语言进行指定。这项研究为构建具备量子感知能力的基础模型开辟了新路径，此类模型能够原生性地理解并推理量子操作，其潜在影响将广泛涉及量子编译与算法发现等领域。

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.