QUANTUM-INSPIRED MACHINE LEARNING ALGORITHMS FOR CLASSICAL COMPUTERS: EXPLORING QUANTUM PRINCIPLES TO IMPROVE CLASSICAL MACHINE LEARNING PERFORMANCE

Abstract

Quantum computing has demonstrated substantial promise in revolutionizing computation which includes machine learning and exploiting of quantum principles like superposition and entanglement. This research explores the development of quantum-inspired machine learning algorithms that leverage quantum concepts, such as amplitude amplification and quantum parallelism, but are designed to run on classical hardware. We present an analysis of how effective and scalable machine learning models are inspired by quantum principles. We specifically look at methods like tensor networks, variational circuits that mimic quantum systems, and quantum-inspired optimization strategies. Our observations imply that these quantum-inspired models can significantly improve computing efficiency, optimization quality, and learning capabilities even though genuine quantum speedup is still unattainable for classical machines. This study further discusses potential applications and the future direction of hybrid quantum classical machine learning systems.