Quantum natural language processing
Quantum natural language processing (QNLP) is the application of quantum computing to natural language processing (NLP). It computes word embeddings as parameterised quantum circuits that can solve NLP tasks faster than any classical computer.[1][2] It is inspired by categorical quantum mechanics and the DisCoCat framework, making use of string diagrams to translate from grammatical structure to quantum processes.[3][4]
Theory[edit]
The first quantum algorithm for natural language processing used the DisCoCat framework and Grover's algorithm to show a quadratic quantum speedup for a text classification task.[1] It was later shown that quantum language processing is BQP-Complete,[2] i.e. quantum language models are more expressive than their classical counterpart, unless quantum mechanics can be efficiently simulated by classical computers.
These two theoretical results assume fault-tolerant quantum computation and a QRAM, i.e. an efficient way to load classical data on a quantum computer. Thus, they are not applicable to the noisy intermediate-scale quantum (NISQ) computers available today.
Experiments[edit]
The algorithm of Zeng and Coecke[1] was adapted to the constraints of NISQ computers and implemented on IBM quantum computers to solve binary classification tasks.[5][6] Instead of loading classical word vectors onto a quantum memory, the word vectors are computed directly as the parameters of quantum circuits. These parameters are optimised using methods from quantum machine learning to solve data-driven tasks such as question answering,[5] machine translation[7] and even algorithmic music composition.[8]
See also[edit]
- Categorical quantum mechanics
- Natural language processing
- Quantum machine learning
- Applied category theory
- String diagram
References[edit]
- ^ a b c Zeng, William; Coecke, Bob (2016-08-02). "Quantum Algorithms for Compositional Natural Language Processing". Electronic Proceedings in Theoretical Computer Science. 221: 67–75. arXiv:1608.01406. doi:10.4204/EPTCS.221.8. ISSN 2075-2180. S2CID 14897915.
- ^ a b Wiebe, Nathan; Bocharov, Alex; Smolensky, Paul; Troyer, Matthias; Svore, Krysta M. (2019-02-13). "Quantum Language Processing". arXiv:1902.05162 [quant-ph].
- ^ Coecke, Bob; de Felice, Giovanni; Meichanetzidis, Konstantinos; Toumi, Alexis (2020-12-07). "Foundations for Near-Term Quantum Natural Language Processing". arXiv:2012.03755 [quant-ph].
- ^ Ganguly, Srinjoy; Morapakula, Sai Nandan; Bertel, Luis Gerardo Ayala, "An Introduction to Quantum Natural Language Processing (QNLP)", Coded Leadership, CRC Press, pp. 1–23, retrieved 2022-11-11
- ^ a b Meichanetzidis, Konstantinos; Toumi, Alexis; de Felice, Giovanni; Coecke, Bob (2023). "Grammar-aware sentence classification on quantum computers". Quantum Machine Intelligence. 5. arXiv:2012.03756. doi:10.1007/s42484-023-00097-1. S2CID 256832721.
- ^ Lorenz, Robin; Pearson, Anna; Meichanetzidis, Konstantinos; Kartsaklis, Dimitri; Coecke, Bob (2023). "QNLP in Practice: Running Compositional Models of Meaning on a Quantum Computer". Journal of Artificial Intelligence Research. 76: 1305–1342. arXiv:2102.12846. doi:10.1613/jair.1.14329. S2CID 232046044.
- ^ Vicente Nieto, Irene (2021). Towards Machine Translation with Quantum Computers (PDF). Master thesis, Stockholm University, Faculty of Science, Department of Physics.
- ^ Miranda, Eduardo Reck; Yeung, Richie; Pearson, Anna; Meichanetzidis, Konstantinos; Coecke, Bob (2022), Miranda, Eduardo Reck (ed.), "A Quantum Natural Language Processing Approach to Musical Intelligence", Quantum Computer Music: Foundations, Methods and Advanced Concepts, Cham: Springer International Publishing, pp. 313–356, arXiv:2111.06741, doi:10.1007/978-3-031-13909-3_13, ISBN 978-3-031-13909-3, retrieved 2022-11-07
External links[edit]
- DisCoPy, a Python toolkit for computing with string diagrams
- lambeq, a Python library for quantum natural language processing
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