Quantum Machine Learning for Optimization Problems: A Comparative Study of Classical and Quantum Approaches
Abstract
Quantum computing has emerged as a promising paradigm for solving complex optimization problems in fields such as logistics, finance, and drug discovery. This paper presents a comparative study between classical optimization algorithms (e.g., genetic algorithms, simulated annealing) and quantum machine learning (QML) techniques, including quantum annealing and variational quantum circuits. We evaluate the efficiency and scalability of these approaches on combinatorial optimization benchmarks. Our findings reveal that QML offers significant advantages in specific problem domains, highlighting the potential of quantum-enhanced AI for next-generation computational challenges.
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