Quantum Machine Learning Algorithms for Optimization Problems: Theory, Implementation, and Applications
Keywords:
Quantum Computing, Machine Learning, Optimization Problems, Quantum Optimization Algorithms, Quantum Annealing, QAOA, Variational Algorithms, Implementation, ApplicationsAbstract
Quantum computing has the potential to transform a number of industries, including machine learning and optimization. This work investigates the relationship between quantum computing and machine learning, with particular attention on the creation, use, and applications of quantum machine learning algorithms for optimization issues. We present a thorough analysis of the theoretical foundation of quantum optimization algorithms, talk about how they are practically implemented on quantum computing platforms, and investigate real-world applications in a several fields. We also highlight upcoming research directions and issues in the realm of quantum machine learning.
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