Quantum Computing Applications in Optimization and Data Analytics

  • Authors

    • John McCarthy Professor of Computer Science, Stanford University, United States Author
    • Marvin Minsky Professor, Massachusetts Institute of Technology, United States Author

    DOI:

    https://doi.org/10.67228/30715628/IJMIET-2025PII7J2V

    Published 10-05-2025

  • Quantum Computing, Quantum Optimization, Data Analytics, Quantum Machine Learning, Quantum Approximate Optimization Algorithm (QAOA), Quantum Annealing, Variational Quantum Algorithms, Hybrid Quantum-Classical Computing, Artificial Intelligence, Big Data Analytics

    Issue

    Vol. 8 No. 2 (2025)

    Section

    Articles

    How to Cite

    [1]
    J. McCarthy and M. Minsky, “Quantum Computing Applications in Optimization and Data Analytics”, ijmiet, vol. 8, no. 2, pp. 01–17, Oct. 2025, doi: 10.67228/30715628/IJMIET-2025PII7J2V.

  • Abstract

    Quantum computing leverages quantum phenomena such as superposition and entanglement to solve complex optimization and data analytics problems more efficiently than classical computing. Recent advances in quantum algorithms, including QAOA, Quantum Annealing, VQE, and hybrid quantum-classical models, have enabled applications in finance, healthcare, logistics, manufacturing, cybersecurity, and artificial intelligence. This paper surveys quantum computing applications, proposes a taxonomy, and presents a hybrid quantum-classical framework integrating quantum optimization with quantum-enhanced machine learning. Mathematical formulation, algorithmic representation, and experimental evaluation demonstrate improved optimization accuracy, computational efficiency, predictive performance, and solution quality over conventional approaches, highlighting quantum computing's potential for next-generation intelligent decision-making systems.

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