Aligning Quantum Operators with Large Language Models
Summary
This paper introduces an approach to map unitary operators into the latent space of an LLM, enabling quantum circuit synthesis and language-conditioned gate constraint specification, achieving competitive results on Clifford+T circuit synthesis.
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Paper page - Aligning Quantum Operators with Large Language Models
Source: https://huggingface.co/papers/2606.13811
Abstract
Large language models can be adapted to understand quantum operators by mapping unitary matrices into their latent space, enabling quantum circuit synthesis and language-conditioned gate constraint specification.
CanLarge Language Models(LLMs) understand and reason aboutquantum operators? Despite their remarkable capabilities in mathematics and symbolic reasoning, LLMs remain inherently blind toquantum representationssuch asunitary matrices. In this work, we take a step toward bridging this gap by introducing an approach that maps unitary operators into thelatent spaceof an LLM, enabling unified modeling over quantum and linguistic inputs. We instantiate this idea onClifford+T circuit synthesisover aPauli 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 acrossquantum compilationand algorithm discovery.
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