Quantum computation and natural language processing

Yousra Bouakba, Hacene Belhadef

Joseph C. H. Chen

A. Jenifus Selvarani, D. Kesavaraja

In recent years Quantum computers have been widely used for image manipulation. Similarly, NLP helps understand text and spoken language as humans do. The application of quantum computing to Natural Language Processing has yielded a new field of research, which is known as quantum natural language processing. This paper focuses on listing all the NLP approaches and categorize them based on theoretical work and those implemented on classical or quantum hardware; and also based on task, i.e., general purpose syntax-semantic representation or specific NLP tasks, like sentiment analysis or question answering,and finally by the resources used in the evaluation phase, i.e., whether a benchmark dataset or a custom one has been used.

Charles Varmantchaonala Moudina, Jean Louis Ebongue Kedieng Fendji, Julius Schöning +1 more

By using the quantum mechanics phenomenon, quantum computers provide a new dimension of computational power that drastically accelerates solving complex and resource-intensive problems. One of the most evolving but, due to the computation power of standard computers, reasonably limited application domain is natural language processing (NLP). NLP seeks to give interactive systems the ability to understand and manipulate human language. Making systems understand and manipulate human languages requires large amounts of data and computational power during learning as well as during the execution of NLP. For handling these amounts of data like text and audio recordings and the complexity of classical NLP algorithms, quantum computation has emerged as a promising solution. This work gives an extensive overview of this new field, known as quantum natural language processing (QNLP). Introducing the basics of quantum computing, we discuss its use in NLP by explaining the different proposed embedding models, quantum algorithms, and other methods of QNLP. As QNLP is still in its infancy, this comprehensive overview is the foundation that points to the upcoming research direction.

Dominic Widdows, Willie Aboumrad, Dohun Kim +2 more

Ashish Arya, Arti Ranjan

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.