Quantum-Inspired Optimization Techniques for Enhancing Classical Machine Learning Performance: A Comprehensive Review
Rattan Rana, Divyanshi Sharma
This study presents an in-depth and focused review of Quantum-based conceptual optimization Strategies and their impact in enhancing classical machine learning training. Quantum-inspired methods simulate key fundamental concepts of quantum mechanics, including superposition, entanglement, and quantum tunneling within classical computing environments to improve optimization efficiency. These techniques enable better exploration of complex search spaces, faster convergence, and improved capability to avoid local minima. The review examines major quantum-inspired algorithms, including evolutionary models, annealing-based methods, and hybrid quantum–classical frameworks, highlighting their strengths, limitations, and suitability for modern ML tasks. The study also discusses their relevance to sustainable, energy-efficient digital transformation in education and research. The goal is to present clear insights into how quantum-inspired optimization can strengthen traditional ML training without requiring quantum hardware.
CRC Press eBooks•May 8, 2026•0 citations