Meaning Representations as Variational Quantum Circuits

Abstract

Large language and vision-language models (VLMs) struggle with a ‘compositionality gap’. They treat language as a sequence of tokens lacking any structure and thus rely on a large number of parameters making them computationally expensive. To address these issues, we propose CCG-VQC, a quantum framework that unifies statistical distributions with linguistic structure. Guided by Combinatory Categorial Grammar, our model maps syntactic rules into parametrised quantum circuits and models sentences as quantum states. We evaluate CCG-VQC on structural VLM benchmarks such as ARO and SVO-Swap. Our experiments show that CCG-VQC consistently outperforms a quantum bag-of-words model, as well as classical VLMs such as CLIP and OpenCLIP. CCG-VQC achieved 71.19% accuracy on ARO-Attribution, significantly outperforming the parameter-matched MicroCLIP, which struggled to surpass random chance with a maximum performance of 50.85%.

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