Implications of Deep Circuits in Improving Quality of Quantum Question
  Answering
Pragya Katyayan Nisheeth Joshi
Question Answering (QA) has proved to be an arduous challenge in the area of
natural language processing (NLP) and artificial intelligence (AI). Many
attempts have been made to develop complete solutions for QA as well as
improving significant sub-modules of the QA systems to improve the overall
performance through the course of time. Questions are the most important piece
of QA, because knowing the question is equivalent to knowing what counts as an
answer (Harrah in Philos Sci, 1961 [1]). In this work, we have attempted to
understand questions in a better way by using Quantum Machine Learning (QML).
The properties of Quantum Computing (QC) have enabled classically intractable
data processing. So, in this paper, we have performed question classification
on questions from two classes of SelQA (Selection-based Question Answering)
dataset using quantum-based classifier algorithms-quantum support vector
machine (QSVM) and variational quantum classifier (VQC) from Qiskit (Quantum
Information Science toolKIT) for Python. We perform classification with both
classifiers in almost similar environments and study the effects of circuit
depths while comparing the results of both classifiers. We also use these
classification results with our own rule-based QA system and observe
significant performance improvement. Hence, this experiment has helped in
improving the quality of QA in general.