Quantum extreme learning machine for classification tasks

Abstract

Quantum Computing is one of the most researched areas in computer science and physics, however, current quantum computers are influenced by unwanted noise from environmental factors. Quantum Extreme Learning Machine (QELM) is a hybrid classical-quantum framework that is intended to take advantage of these complex and rich dynamics of noisy intermediate-scale quantum (NISQ) devices to improve learning capacity. The objective of this work is to explore the power of a contemporary gate-based QELM relative to current classical binary classification problem solvers.