Quantum Algorithms for Optimization and Machine Learning
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V5I4P115Keywords:
Quantum Computing, Quantum Algorithms, Optimisation, Quantum Approximate Optimization Algorithm (QAOA), Quantum Annealing, Quantum Machine Learning (QML), Quantum Support Vector Machines, Quantum Neural Networks, Hybrid Quantum-Classical Systems, Quantum AdvantageAbstract
Quantum computing offers a fundamentally new paradigm for solving complex optimisation and learning tasks by exploiting superposition, entanglement, and other quantum phenomena. Over the past decade, research has increasingly focused on quantum-enhanced approaches to optimisation and machine learning (ML), showing the potential to outperform classical methods in specific settings (Zaman, 2023; Peral-García, 2024). This paper presents a comprehensive review of quantum algorithms for optimisation (e.g., the Quantum Approximate Optimization Algorithm and quantum annealing) and quantum machine learning frameworks (such as quantum support vector machines and quantum neural networks), analysing their theoretical underpinnings, implementation status, and applicability to real-world tasks. Key challenges including hardware noise, limited qubit counts, and algorithmic scalability are examined (Chen, 2024). We also explore hybrid quantum-classical architectures as a near-term route to quantum advantage and propose future research directions aimed at bridging the current gap between quantum algorithm theory and large-scale deployment. Through this synthesis, we aim to provide both academic and practitioner audiences with a clear roadmap for leveraging quantum algorithms in optimisation and ML workflows
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