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.

翻译：视觉词义消歧聚焦于多义词，其候选图像容易混淆。传统方法使用经典概率计算图像与目标词每个释义的匹配似然，并将这些似然求和以形成后验概率。然而，由于语义不确定性的挑战，来自不同来源的释义不可避免地带有语义偏差，这可能导致有偏差的消歧结果。受量子叠加态在建模不确定性方面的启发，本文提出了一种用于无监督视觉词义消歧的量子推理模型（Q-VWSD）。该模型将目标词的多个释义编码为叠加态以减轻语义偏差。随后执行量子电路并观察结果。通过对我们的方法进行形式化分析，我们发现Q-VWSD是基于经典概率方法的量子推广。在此基础上，我们进一步设计了Q-VWSD的启发式版本，该版本能够在经典计算上更高效地运行。实验表明，我们的方法优于最先进的经典方法，特别是通过有效利用来自大语言模型的非专用释义，进一步提升了性能。我们的方法展示了量子机器学习在实际应用中的潜力，并为在量子硬件尚未成熟时利用量子建模优势于经典计算机提供了一个案例。