Multi-class classification using quantum transfer learning

Bidisha Dhara, Monika Agrawal, Sumantra Dutta Roy

Abstract: Image classification is one of the most important machine learning tasks, especially in this digital era. Though there exists classical algorithms which have performed quite well in multi-class classification tasks, classification using quantum architectures have mostly been limited to 2 or 3 classes. As the number of classes increased, the existing architectures did not achieve good accuracy. In this work, we aim to classify the MNIST dataset into 10 corresponding classes, using classical-to-quantum transfer learning. We performed both binary as well as multi-class classification using the hybrid architecture which yielded a maximum accuracy of approximately 100 and 90.4% respectively.

External IDs:dblp:journals/qip/DharaAR24