Classification Using a Two-Qubit Quantum Chip

Abstract

Quantum computing has great potential for advancing machine learning algorithms beyond classical reach. Even though full-fledged universal quantum computers do not exist yet, its expected benefits for machine learning can already be shown using simulators and already available quantum hardware. In this work, we focus on distance-based classification using actual early stage quantum hardware. We extend earlier work and present a distance-based classification algorithm using only two qubits. We show that the results are similar to the theoretically expected results.