Meaning Representations as Variational Quantum Circuits

Large vision-language models struggle with a "compositionality gap" due to their reliance on unstructured statistical approximations to capture complex relations. We propose CCG-VQC, a quantum framework that explicitly maps linguistic syntax into parametrised quantum circuits. To separate the impact of architectural design from scale, we introduce MicroCLIP, a parameter-matched classical transformer. While CCG-VQC achieved 71.19 % on ARO-Attribution, MicroCLIP struggled to surpass random chance (50.85 %), and even standard CLIP lags behind at 61.00 %. This demonstrates that in low-parameter regimes, explicit linguistic structure outperforms statistical approximation.