Quantum computing (QC) has emerged as a disruptive technology with the potential to revolutionize machine learning (ML) by solving computationally intractable problems exponentially faster than classical computers. This paper provides an in-depth review of quantum machine learning (QML), covering fundamental principles, key algorithms, hybrid quantum-classical approaches, and real-world applications. We analyze the latest advancements in quantum-enhanced ML models, including quantum neural networks (QNNs), quantum support vector machines (QSVMs), and quantum optimization techniques. Additionally, we discuss critical challenges such as qubit de-coherence, error correction, and scalability in noisy intermediate-scale quantum (NISQ) devices. Finally, we outline future research directions, including fault-tolerant quantum computing and quantum data encoding strategies. This review serves as a comprehensive resource for researchers exploring the intersection of quantum computing and machine learning.
Quantum computing, machine learning, quantum machine learning (QML), quantum algorithms, hybrid quantum-classical models, NISQ devices, quantum error correction
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