The last few decades have seen significant breakthroughs in the fields of deep learning and
quantum computing. Research at the junction of the two fields has garnered an increasing …

Quantum theory, originally proposed as a physical theory to describe the motions of
microscopic particles, has been applied to various non-physics domains involving human …

We tackle the crucial challenge of fusing different modalities of features for multimodal
sentiment analysis. Mainly based on neural networks, existing approaches largely model …

In linguistics, the uncertainty of context due to polysemy is widespread, which attracts much
attention. Quantum-inspired complex word embedding based on Hilbert space plays an …

One of the main challenges of cognitive science is to explain the representation of
conceptual knowledge and the mechanisms involved in evaluating the similarities between …

Since 2004, researchers have been using the mathematical framework of quantum theory in
information retrieval (IR). Quantum theory offers a generalized probability and logic …

Most of the existing quantum-inspired models are based on amplitude-phase embedding to
model natural language, which maps words into Hilbert space. In quantum-computing …

Abstract The introduction of Quantum Theory (QT) provides a unified mathematical
framework for Information Retrieval (IR). Compared with the classical IR framework, the …

Abstract Word2Vec (W2V) and Glove are popular word embedding algorithms that perform
well on a variety of natural language processing tasks. The algorithms are fast, efficient and …

Language modeling is essential in Natural Language Processing and Information Retrieval
related tasks. After the statistical language models, Quantum Language Model (QLM) has …