Abstract

Optimization problems play a crucial role in science, engineering, logistics, and artificial intelligence. Classical optimization
algorithms often face scalability and computational limitations when dealing with large and complex problem spaces. Recent
advances in quantum computing have opened new possibilities for addressing these challenges. This paper proposes a hybrid
Quantum-AI optimization framework that integrates quantum algorithms with classical artificial intelligence techniques to efficiently solve complex optimization problems. The proposed approach combines quantum variational circuits with classical machine learning-based optimizers to enhance solution quality and convergence speed. Experimental analysis on benchmark optimization problems demonstrates that the hybrid approach outperforms traditional classical methods in terms of accuracy and computational efficiency, highlighting its potential for next-generation optimization systems.