Quantum-Inspired Machine Learning Algorithms for Complex Industrial Process Optimization

  • Authors

    • Dr. Ibrahim Yusuf Professor, University of Lagos, Nigeria. Author

    DOI:

    https://doi.org/10.67228/3142788X/IJMLPA-2021PI2Q9V

    Published 04-04-2021

  • Quantum-Inspired Algorithms, Industrial Process Optimization, Machine Learning, Evolutionary Computation, Quantum Annealing, Smart Manufacturing, Industry 4.0, Metaheuristics, Hybrid AI Systems, Complex Systems Optimization

    Issue

    Vol. 4 No. 1 (2021)

    Section

    Articles

    How to Cite

    [1]
    I. Yusuf, “Quantum-Inspired Machine Learning Algorithms for Complex Industrial Process Optimization”, IJMLPA, vol. 4, no. 1, pp. 01–14, Apr. 2021, doi: 10.67228/3142788X/IJMLPA-2021PI2Q9V.

  • Abstract

    In the era of Industry 4.0, optimizing complex industrial processes requires advanced techniques that can navigate high-dimensional, nonlinear, and dynamic problem spaces. While classical machine learning (ML) methods have achieved considerable success, they often struggle with global optimization and real-time adaptability. This paper investigates the potential of quantum-inspired machine learning algorithms (QIA)—which mimic quantum computation principles without requiring quantum hardware—for industrial process optimization. We explore various QIA frameworks, including quantum-inspired genetic algorithms, neural networks, and annealing strategies, and apply them to real-world case studies in manufacturing and logistics. Our results demonstrate superior convergence speed, enhanced solution quality, and improved robustness over classical counterparts. We also discuss integration strategies, challenges, and the role of QIA as a practical precursor to full-scale quantum computing in industrial environments. This study offers both theoretical insights and actionable pathways for deploying QIA in complex industrial systems.

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