The convergence of machine learning (ML) and quantum computing represents one of the most transformative frontiers in modern computational science. This paper presents a comprehensive survey and experimental analysis of ML approaches applied to quantum computing and quantum information processing (QIP). We systematically review three primary paradigms: (1) classical ML techniques employed to optimize quantum circuits and error mitigation; (2) quantum-enhanced ML algorithms that exploit quantum superposition and entanglement to achieve computational advantages; and (3) hybrid classical-quantum architectures including variational quantum eigensolvers (VQE) and quantum approximate optimization algorithms (QAOA). Using standardized benchmark datasets—including the QASMbench suite, IBM Quantum Network datasets, and synthetic quantum circuit repositories—we conduct comparative performance analysis across metrics such as circuit depth reduction, fidelity improvement, and convergence rate. Our experimental results demonstrate that reinforcement learning-based quantum circuit optimization reduces two-qubit gate counts by up to 34.7% on average, while quantum support vector machines (QSVM) achieve classification accuracy of 96.3% on molecular property datasets with a 2.8x speedup over classical counterparts on near-term quantum hardware. Furthermore, variational quantum neural networks (VQNN) exhibit promising scalability on up to 127-qubit systems. We conclude with a structured roadmap identifying open challenges in noise resilience, qubit coherence, and the design of fault-tolerant ML-quantum hybrid pipelines.

Keywords: quantum computing, machine learning, quantum information processing, variational quantum eigensolver, quantum circuit optimization, quantum machine learning, quantum error mitigation, hybrid quantum-classical algorithms.