Supervised Question Classification on SelQA Dataset Using Variational Quantum Classifiers

Machine learning and quantum computing fuse together to form quantum machine learning. Although the phenomenon is new, it has already proved its worth in various fields like finance and chemistry. The potential of quantum computing and its extraordinary properties enable us to process data in a way classical computer can never think of. When machine learning gets the power of quantum computing, information processing is enhanced significantly. In this paper, we have used variational quantum classifiers to classify questions from two domains of SelQA dataset. We keep the focus on the implications of circuit-depth in different experiments and analyze the results. VQC performs well with 11 features on lowest circuit depths and gives a testing accuracy of 58%.