Abstract
QNLP is a quite new and emerging field of inquiry that aims to utilize the
principles of quantum computing to achieve NLP tasks. QNLP aims to enhance the
accuracy of natural language processing by utilizing the quantum properties of matter
known as superposition, interference, and, most importantly, entanglement. This book
chapter introduces the basics of QNLP, including a brief overview of concepts used in
quantum computing and techniques of NLP. We have explored the potential benefits of
QNLP, such as faster and more accurate processing of natural language data. We also
examine the challenges and limitations of QNLP, such as the need for quantum
hardware and the integration with classical NLP techniques. In addition, this chapter
covers recent advances in QNLP, including quantum algorithms for language
modeling, machine translation, and sentiment analysis. We also discuss the
development of hybrid quantum-classical algorithms and the potential applications of
QNLP in industry and academia. Overall, this chapter provides a comprehensive
overview of QNLP and its potential to revolutionize natural language processing.