Quantum Natural Language Processing based Sentiment Analysis using lambeq Toolkit

• URL: http://arxiv.org/abs/2305.19383v1
• Date: Tue, 30 May 2023 19:54:02 GMT
• Title: Quantum Natural Language Processing based Sentiment Analysis using lambeq Toolkit
• Authors: Srinjoy Ganguly, Sai Nandan Morapakula, Luis Miguel Pozo Coronado
• Abstract summary: Quantum natural language processing (QNLP) is a young and gradually emerging technology which has the potential to provide quantum advantage for NLP tasks. We show the first application of QNLP for sentiment analysis and achieve perfect test set accuracy for three different kinds of simulations and a decent accuracy for experiments ran on a noisy quantum device.
• Score: 0.5735035463793007
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Sentiment classification is one the best use case of classical natural language processing (NLP) where we can witness its power in various daily life domains such as banking, business and marketing industry. We already know how classical AI and machine learning can change and improve technology. Quantum natural language processing (QNLP) is a young and gradually emerging technology which has the potential to provide quantum advantage for NLP tasks. In this paper we show the first application of QNLP for sentiment analysis and achieve perfect test set accuracy for three different kinds of simulations and a decent accuracy for experiments ran on a noisy quantum device. We utilize the lambeq QNLP toolkit and $t|ket>$ by Cambridge Quantum (Quantinuum) to bring out the results.

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