Quantum Computing Applications in Optimization and Data Analytics
TL;DR
This paper surveys quantum computing applications, proposes a taxonomy, and presents a hybrid quantum-classical framework integrating quantum optimization with quantum-enhanced machine learning, highlighting quantum computing's potential for next-generation intelligent decision-making systems.
Abstract
Quantum computing leverages quantum phenomena such as superposition and entanglement to solve complex optimization and data analytics problems more efficiently than classical computing. Recent advances in quantum algorithms, including QAOA, Quantum Annealing, VQE, and hybrid quantum-classical models, have enabled applications in finance, healthcare, logistics, manufacturing, cybersecurity, and artificial intelligence. This paper surveys quantum computing applications, proposes a taxonomy, and presents a hybrid quantum-classical framework integrating quantum optimization with quantum-enhanced machine learning. Mathematical formulation, algorithmic representation, and experimental evaluation demonstrate improved optimization accuracy, computational efficiency, predictive performance, and solution quality over conventional approaches, highlighting quantum computing's potential for next-generation intelligent decision-making systems.