將量子運算子與大型語言模型對齊

摘要

大型語言模型能否理解並推理量子運算子？儘管在數學與符號推理方面表現卓越，大型語言模型對於量子表徵（如酉矩陣）本質上仍存在盲點。本研究透過提出一種將酉算子映射至大型語言模型潛空間的方法，朝彌合此差距邁進一步，實現對量子輸入與語言輸入的統一建模。我們將此構想實例化於Clifford+T電路合成任務中，該任務基於Pauli旋轉閘集合，成果不僅與最先進方法競爭力相當，且隨著訓練資料穩定擴展而未出現飽和跡象。此方法進一步實現了語言條件化合成，使訓練中未見的閘極限制能直接以自然語言指定。本研究揭示了通往具備量子感知能力之基礎模型的路徑，該模型能原生解讀並推理量子運算，對量子編譯與演算法探索等領域具有廣泛影響。

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.