Quantum machine learning for natural language processing application

Shyambabu Pandey, Nihar Jyoti Basisth, Tushar Sachan, Neha Kumari and Partha Pakray

Physica A: Statistical Mechanics and its Applications, 2023, vol. 627, issue C

Abstract: Quantum computing is a speedily emerging area that applies quantum mechanics properties to solve complex problems that are difficult for classical computing. Machine learning is a sub-field of artificial intelligence which makes computers learn patterns from experiences. Due to the exponential growth of data, machine learning algorithms may be insufficient for big data, whereas on other side quantum computing can do fast computing. A combination of quantum computing and machine learning gave rise to a new field known as quantum machine learning. Quantum machine learning algorithms take advantage of the fast processing of quantum computing and show speedup compared to their classical counterpart. Natural language processing is another area of artificial intelligence that enables the computer to understand human languages. Now, researchers are trying to take advantage of quantum machine learning speedup in natural language processing applications. In this paper, first, we discuss the path from quantum computing to quantum machine learning. Then we review the state of the art of quantum machine learning for natural language processing applications. We also provide classical and quantum-based long short-term memory for parts of speech tagging on social media code mixed language. Our experiment shows that quantum-based long short-term memory performance is better than classical long short-term memory for parts of speech tagging of code-mixed datasets.

Keywords: Quantum computing; Quantum machine learning; Natural language processing; POS tagging (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:627:y:2023:i:c:s0378437123006787

DOI: 10.1016/j.physa.2023.129123

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