Implement Quantum Machine Learning Classifier using MNIST Dataset

Abstract: Quantum computers might be more potent than the normal classical computers and Supercomputers. Some of the specific applications like Quantum simulation, Cryptography, Optimization etc. Normal classical computers are worked based on the binary system (0,1) Whereas in the Quantum computers are worked as Quantum bit also termed as Qubit. Quantum computers use qubit, which can represent 0, 1, or any superposition of these states. This property enables quantum computers to process information in unique ways. The Qubit state can be 0&1 at the same time. When we observe that it can collapses into one of the possible states. We propose to implement the predictive capability of the Quantum Machine Learning (QML) classifier on the MNIST Handwritten Digits dataset. We deploy the model on the IBM Quantum computer using Qiskit.

Keywords: Classical Computers, Quantum Computers, Qubit, MNIST Dataset, QML.