Using quantum physics for word representation has the potential to help including more features for words; more specifically, quantum/quantum-inspired algorithms can better help model word ambiguity and hyponymy relations

Word representation modeling targets of quantum NLP

Algorithms based on quantum physics can help model processes related to the composition of lexical semantic units given the meaning of a word. This includes modeling term dependencies and modeling grammar

Composition-based modeling targets of quantum NLP

Both quantum-inspired algorithms and quantum algorithms can model different features in the language; these are broken down into word representation and composition

Carleton College

The basic idea of integrating quantum computing in NLP is to make use the power of quantum mechanics for solving computational problems

Quantum Computing NLP

Natural Language Processing Meets Quantum Physics: A Survey and Categorization(https://aclanthology.org/2021.emnlp-main.254) (Wu et al., EMNLP 2021)

Natural Language Processing Meets Quantum Physics: A Survey and Categorization (Wu et al.)

Quantum NLP algorithms can be implemented either on quantum computers (quantum algorithms) or classical computers (quantum-inspired or quantum-like models)

Quantum NLP algorithms

Modeling Targets of Quantum NLP

Quantum NLP shows comparable or better performance compared with strong baselines for various tasks. These include:
- information retrieval
- question answering
- text classification
- speech recognition

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