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Supervised Question Classification on SelQA Dataset Using Variational Quantum Classifiers

  • 2023
  • OriginalPaper
  • Chapter
Published in:
Authors
Pragya Katyayan
Nisheeth Joshi
Published in
International Conference on Innovative Computing and Communications
Publisher
Springer Nature Singapore

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Abstract

The chapter delves into the application of variational quantum classifiers (VQCs) for question classification on the SelQA dataset, a task crucial in natural language processing. It discusses the extraction of features from questions, the encoding of classical data into quantum states, and the use of variational quantum circuits for classification. The paper presents experimental results, analyzing the impact of circuit depth and feature sets on classification accuracy. It concludes by highlighting the potential of quantum computing in solving complex problems in the field of NLP and suggests future research directions.
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Title
Supervised Question Classification on SelQA Dataset Using Variational Quantum Classifiers
Authors
Pragya Katyayan
Nisheeth Joshi
Copyright Year
2023
Publisher
Springer Nature Singapore
Book
International Conference on Innovative Computing and Communications

Print ISBN: 978-981-19-3678-4

Electronic ISBN: 978-981-19-3679-1

Copyright Year: 2023

DOI
https://doi.org/10.1007/978-981-19-3679-1_60
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