Quantum Machine Learning: Accelerating AI Models with Quantum Computing

Abstract

QML explores new paradigms for accelerating AI model training and inference using quantum computing and AI. This study discusses QML and how quantum methods like quantum annealing, variational quantum circuits, and quantum kernel estimation might improve machine learning. quantum computing's ability to solve high-dimensional data and complicated optimization problems faster than classical approaches, promising speedups in data clustering, model training, and feature selection. Current QML issues include hardware limits, error rates, and the necessity for hybrid quantum-classical algorithms, which combine quantum computing's benefits with traditional machine learning methods. Thru simulations and comparative analyzes, we evaluate quantum-enhanced algorithms and their potential for AI acceleration. QML has the potential to change AI, enabling quicker, more efficient model construction powered by quantum computing.