Quantum Machine Learning for Complex Optimization: Emerging Applications in Industry and Scientific Research

Kate Cooney

Quantum Machine Learning for Complex Optimization: Emerging Applications in Industry and Scientific Research

Author(s)	Kate Cooney
Country	United States
Abstract	Quantum machine learning (QML) represents an emerging interdisciplinary field at the intersection of quantum computing, artificial intelligence and mathematical optimisation. As industrial and scientific problems become increasingly complex, conventional optimisation approaches can face challenges associated with large search spaces, nonlinear relationships, combinatorial complexity and computational scalability. Quantum machine learning introduces quantum computational principles such as superposition, entanglement and quantum interference into machine-learning and optimisation workflows, creating new approaches for addressing selected classes of complex problems. This paper examines the foundations of QML for complex optimisation and investigates emerging applications across industrial and scientific domains. It proposes an integrated Quantum Optimisation Intelligence Framework consisting of problem formulation, classical preprocessing, quantum representation, hybrid quantum-classical optimisation, solution evaluation and iterative refinement. Applications in logistics, supply-chain optimisation, portfolio optimisation, energy systems, manufacturing, drug discovery, materials science, traffic management and scientific simulation are examined. Particular attention is given to variational quantum algorithms, quantum approximate optimisation algorithms, quantum kernels, quantum neural networks and hybrid optimisation architectures. The paper also evaluates current limitations, including noisy intermediate-scale quantum hardware, limited qubit availability, data-loading challenges, optimisation landscapes, measurement overhead, error mitigation and uncertainty concerning practical quantum advantage. The study argues that near-term QML is most realistically positioned as a hybrid computational paradigm rather than a complete replacement for classical machine learning and optimisation. Future progress will depend on advances in quantum hardware, algorithms, error correction, quantum-aware data representation and benchmark development. The paper concludes that QML offers promising new research directions for complex optimisation, particularly where quantum algorithms can be closely matched with problem structure and integrated with powerful classical computing resources.
Keywords	Quantum Machine Learning, Quantum Computing, Complex Optimisation, QAOA, Quantum Neural Networks, Hybrid Computing, Artificial Intelligence, Optimisation, Quantum Algorithms, Scientific Computing.
Field	Engineering
Published In	Volume 6, Issue 6, November-December 2024
Published On	2024-12-26

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American Journal of Advanced Multidisciplinary Research and Innovation

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Quantum Machine Learning for Complex Optimization: Emerging Applications in Industry and Scientific Research

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