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International Conference on Green Technologies and Sustainable Solutions (GTSS)

Quantum Machine Learning for Optimization Problems Leveraging Quantum Computing to Speed Up Large-Scale Optimization in AI Models

Ram Awtar, Rohitaswa Pandey

Published Feb 1, 2026• 0 citations
Publisher
DOI
10.1109/gtss68743.2026.11679593

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Optimization problems that are computationally intractable can be tackled with the help of AI and quantum computing. Standard optimization techniques are ill-equipped to deal with high-dimensional search spaces and non-linear constraints in domains such as logistics, finance, energy management, pharmaceutical research, and large-scale industrial scheduling. Using quantum computing with AI-driven learning, this research presents a hybrid optimization strategy that enhances the efficiency and accuracy of problem-solving. Adaptive optimization, parameter changes, and pattern discovery are all part of machine learning models, whereas quantum annealing and variational quantum eigen solvers simultaneously explore massive solution spaces. The proposed method uses quantum probabilistic computation and heuristic refining based on AI to reduce computational complexity and speed up the convergence of global optimums. While quantum computers optimize computationally expensively at the quantum-classical interface, deep learning models preprocess and extract features. Compared to metaheuristics, simulation-based performance analysis improves both the quality of solutions and the speed of convergence. The study demonstrated that AI frameworks boosted with quantum computing may solve combinatorial optimization problems and NP-hard problems. Using a computational paradigm that is both scalable and future-proof, this work reimagines optimization in the scientific, industrial, and engineering spheres.