# 将量子算子与大语言模型对齐

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-06-11 08:00
- AIHOT 分数：50
- AIHOT 链接：https://aihot.virxact.com/items/cmqhgiko3047asle1we4x55pc
- 原文链接：https://arxiv.org/abs/2606.13811

## AI 摘要

大语言模型虽在数学与符号推理上表现出色，但无法理解量子表示（如酉矩阵）。本文提出将酉算子映射到LLM潜在空间，实现量子输入与语言输入的联合建模。在Clifford+T电路合成上，该模型达到与最先进方法竞争的性能，且随训练数据规模扩展持续提升，未见饱和迹象。方法还支持语言条件合成，允许以自然语言指定训练中未见过的门约束。这项工作为构建原生理解量子运算的量子感知基础模型铺平道路，可能对量子编译与算法发现产生广泛影响。

## 正文

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.