mlsanityData to 14 Oct 2026

Quantum Natural Language Processing

arXiv 2403.19758 ↗First posted 28 Mar 2024quant-ph cs.AI cs.CLcited by 6

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Abstract (arXiv)

Language processing is at the heart of current developments in artificial intelligence, and quantum computers are becoming available at the same time. This has led to great interest in quantum natural language processing, and several early proposals and experiments. This paper surveys the state of this area, showing how NLP-related techniques have been used in quantum language processing. We examine the art of word embeddings and sequential models, proposing some avenues for future investigation and discussing the tradeoffs present in these directions. We also highlight some recent methods to compute attention in transformer models, and perform grammatical parsing. We also introduce a new quantum design for the basic task of text encoding (representing a string of characters in memory), which has not been addressed in detail before. Quantum theory has contributed toward quantifying uncertainty and explaining "What is intelligence?" In this context, we argue that "hallucinations" in modern artificial intelligence systems are a misunderstanding of the way facts are conceptualized: language can express many plausible hypotheses, of which only a few become actual.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 42 of its 99 references that are in the map. First posted on arXiv 28 Mar 2024.

Builds on

42 of 99 references.

References · 42

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30 more
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Built on by

Cited by year

Citing papers per year: 2024: 3; 2025: 3.

6 papers.

  1. 2024Quantum Artificial Intelligence: A Brief Surveycited by 3
  2. 2024Property-guided Inverse Design of Metal-Organic Frameworks Using Quantum Natural Language Processingcited by 1
  3. 2024Learning Complex Word Embeddings in Classical and Quantum Spacescited by 1
  4. 2025Quantum Methods for Managing Ambiguity in Natural Language Processingcited by 0
  5. 2025Supervised Quantum Machine Learning: A Future Outlook from Qubits to Enterprise Applicationscited by 0
  6. 2025Superpositional Gradient Descent: Harnessing Quantum Principles for Model Trainingcited by 0