将量子算符与大型语言模型进行对齐
Aligning Quantum Operators with Large Language Models
摘要
大型语言模型是否能够理解和推理与量子运算相关的内容呢？尽管这些模型在数学和符号推理方面拥有出色的能力，但它们本质上仍然无法理解诸如酉矩阵这样的量子表示方式。在这项研究中，我们试图通过一种方法来实现这一突破——即将酉算子映射至大型语言模型的潜在空间之中，从而实现对量子信息和语言信息的统一建模。我们在Clifford+T电路合成框架下应用了这一思想，并利用Pauli旋转门集来构建模型。结果表明，我们的模型所取得的效果可与最先进的方法相媲美，且随着训练数据的增加，模型的性能也持续提升，没有出现饱和现象。此外，我们的方法还支持基于语言的合成操作，使得那些在训练过程中无法被直接观察到的门约束条件能够用自然语言来指定。这项工作为构建能够自然理解和推理量子运算的基础模型提供了可能，这可能会带来更广泛的影响，包括量子编译器和算法发现领域。
English Abstract
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.