Quantum Natural Language Processing based Sentiment Analysis using lambeq Toolkit
This demonstrates a potential quantum advantage for NLP tasks, though it is incremental as it builds on existing QNLP tools.
The paper tackles sentiment analysis by applying quantum natural language processing (QNLP) for the first time, achieving perfect test set accuracy in simulations and decent accuracy on a noisy quantum device.
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.