Latest Developments in Quantum Optimization for Machine Learning

With the potential for exponential speedups and higher solution quality in high-dimensional, non-convex landscapes, quantum optimization uses quantum concepts like superposition, entanglement, and tunnelling to improve machine learning workflows. This chapter summarizes the latest developments (2020–2024) in quantum optimization methods, such as variational quantum algorithms, quantum annealing, and quantum-enhanced sampling, and investigates how to incorporate them into machine learning pipelines. We discuss applications in supervised and unsupervised learning, such as support vector machines, neural network training, feature selection, and clustering, and present empirical findings from 15 important studies. We present formal mathematical models for quantum-enhanced optimization and detailed pseudocode for 4 pivotal algorithms. Future directions for obtaining a practical quantum advantage in machine learning are described at the end of the chapter.