Quantum Visual Word Sense Disambiguation: Unraveling Ambiguities Through Quantum Inference Model

• URL: http://arxiv.org/abs/2512.24687v1
• Date: Wed, 31 Dec 2025 07:47:14 GMT
• Title: Quantum Visual Word Sense Disambiguation: Unraveling Ambiguities Through Quantum Inference Model
• Authors: Wenbo Qiao, Peng Zhang, Qinghua Hu,
• Abstract summary: This paper proposes a Quantum Inference Model for Unsupervised Visual Word Sense Disambiguation (Q-VWSD)<n>It encodes multiple glosses of the target word into a superposition state to mitigate semantic biases.<n>By formalizing our method, we find that Q-VWSD is a quantum generalization of the method based on classical probability.
• Score: 51.75804843502132
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Visual word sense disambiguation focuses on polysemous words, where candidate images can be easily confused. Traditional methods use classical probability to calculate the likelihood of an image matching each gloss of the target word, summing these to form a posterior probability. However, due to the challenge of semantic uncertainty, glosses from different sources inevitably carry semantic biases, which can lead to biased disambiguation results. Inspired by quantum superposition in modeling uncertainty, this paper proposes a Quantum Inference Model for Unsupervised Visual Word Sense Disambiguation (Q-VWSD). It encodes multiple glosses of the target word into a superposition state to mitigate semantic biases. Then, the quantum circuit is executed, and the results are observed. By formalizing our method, we find that Q-VWSD is a quantum generalization of the method based on classical probability. Building on this, we further designed a heuristic version of Q-VWSD that can run more efficiently on classical computing. The experiments demonstrate that our method outperforms state-of-the-art classical methods, particularly by effectively leveraging non-specialized glosses from large language models, which further enhances performance. Our approach showcases the potential of quantum machine learning in practical applications and provides a case for leveraging quantum modeling advantages on classical computers while quantum hardware remains immature.

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