Quantum Natural Language Processing based Sentiment Analysis using
  lambeq Toolkit
Srinjoy Ganguly Sai Nandan Morapakula Luis Miguel Pozo Coronado
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.