Multiclass classification using variational quantum circuit on benchmark dataset

Abstract Today, classification is a significant task in data science, and many industries, including healthcare, transport, and banking sectors, are required to classify the data. In this NISQ era, quantum computers are capable of solving complex data challenges and can predict results with minimum features. The quantum neural network is being studied extensively for machine learning problems. In this paper, we have performed the multiclass classification using variational quantum circuits on benchmark datasets. A combination of quantum and classical neural networks is used to build the quantum circuit and optimize the parameters. The quantum circuit is used for the feedforward architecture, while in back-propagation, parameters are updated using a classical optimizer on classical computers. We have successfully shown classification using the proposed approach in benchmark datasets, such as the Iris flower and the MNIST Digit data set. Our results show that VQC is a promising candidate for classification problems with fewer features. To perform our experiments we have used IBM Quantum hardware and simulators.