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Quantum Algorithms for Compositional Natural Language Processing

arXiv 1608.01406 ↗First posted 4 Aug 2016cs.CL quant-phcited by 24

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Abstract (arXiv)

We propose a new application of quantum computing to the field of natural language processing. Ongoing work in this field attempts to incorporate grammatical structure into algorithms that compute meaning. In (Coecke, Sadrzadeh and Clark, 2010), the authors introduce such a model (the CSC model) based on tensor product composition. While this algorithm has many advantages, its implementation is hampered by the large classical computational resources that it requires. In this work we show how computational shortcomings of the CSC approach could be resolved using quantum computation (possibly in addition to existing techniques for dimension reduction). We address the value of quantum RAM (Giovannetti,2008) for this model and extend an algorithm from Wiebe, Braun and Lloyd (2012) into a quantum algorithm to categorize sentences in CSC. Our new algorithm demonstrates a quadratic speedup over classical methods under certain conditions.

Key facts. Cited by 24 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 Aug 2016.

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Citing papers per year: 2021: 7; 2022: 3; 2023: 7; 2024: 5; 2025: 2.

24 papers.

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  1. 2021QNLP in Practice: Running Compositional Models of Meaning on a Quantum Computercited by 65
  2. 2021lambeq: An Efficient High-Level Python Library for Quantum NLPcited by 43
  3. 2022Quantum Self-Attention Neural Networks for Text Classificationcited by 42
  4. 2023Applying QNLP to sentiment analysis in financecited by 23
  5. 2021Compositionality as we see it, everywhere around uscited by 11
  6. 2023Quantum-Inspired Machine Learning: a Surveycited by 11
  7. 2021Quantum Machine Learning for Financecited by 7
  8. 2021How to make qubits speakcited by 5
  9. 2021Kindergarden quantum mechanics graduates (...or how I learned to stop gluing LEGO together and love the ZX-calculus)cited by 4
  10. 2025Quantum Natural Language Processing: A Comprehensive Review of Models, Methods, and Applicationscited by 4
  11. 2023Quantum Natural Language Processing based Sentiment Analysis using lambeq Toolkitcited by 3
  12. 2023A Pipeline For Discourse Circuits From CCGcited by 3
12 more
  1. 2024Quantum Algorithms for Compositional Text Processingcited by 3
  2. 2024Quantum Artificial Intelligence: A Brief Surveycited by 3
  3. 2022A gentle introduction to Quantum Natural Language Processingcited by 2
  4. 2023Adapting Pre-trained Language Models for Quantum Natural Language Processingcited by 2
  5. 2024Quantum Transfer Learning for Acceptability Judgementscited by 2
  6. 2023Learning Quantum Processes with Memory -- Quantum Recurrent Neural Networkscited by 1
  7. 2024Property-guided Inverse Design of Metal-Organic Frameworks Using Quantum Natural Language Processingcited by 1
  8. 2021Fast Extraction of Word Embedding from Q-contextscited by 0
  9. 2022Design and Implementation of a Quantum Kernel for Natural Language Processingcited by 0
  10. 2023Implications of Deep Circuits in Improving Quality of Quantum Question Answeringcited by 0
  11. 2024Traitement quantique des langues : 'etat de l'artcited by 0
  12. 2025A Hybrid Classical-Quantum Fine Tuned BERT for Text Classificationcited by 0