Quantum Text Classifier -- A Synchronistic Approach Towards Classical
  and Quantum Machine Learning
Dr. Prabhat Santi Kamakhya Mishra Sibabrata Mohanty
Although it will be a while before a practical quantum computer is available,
there is no need to hold off. Methods and algorithms are being developed to
demonstrate the feasibility of running machine learning (ML) pipelines in QC
(Quantum Computing). There is a lot of ongoing work on general QML (Quantum
Machine Learning) algorithms and applications. However, a working model or
pipeline for a text classifier using quantum algorithms isn't available. This
paper introduces quantum machine learning w.r.t text classification to readers
of classical machine learning. It begins with a brief description of quantum
computing and basic quantum algorithms, with an emphasis on building text
classification pipelines. A new approach is introduced to implement an
end-to-end text classification framework (Quantum Text Classifier - QTC), where
pre- and post-processing of data is performed on a classical computer, and text
classification is performed using the QML algorithm. This paper also presents
an implementation of the QTC framework and available quantum ML algorithms for
text classification using the IBM Qiskit library and IBM backends.