Machine Learning and Quantum Machine Learning: A Comprehensive Review of Algorithms, Applications, and Future Directions |
| ( Vol-13,Issue-1,January 2026 ) OPEN ACCESS |
| Author(s): |
Loveleen Kumar, Rajesh Rajaan, Nilam Choudhary, Aakriti Sharma |
| Keywords: |
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Machine Learning, Quantum Machine Learning, Variational Quantum Circuits, Quantum Neural Networks, NISQ, Barren Plateaus, Error Mitigation, Hybrid Quantum-Classical, Quantum Advantage. |
| Abstract: |
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The rapid advancement of artificial intelligence has positioned Machine Learning (ML) as a cornerstone technology across scientific and industrial domains. In parallel, the emergence of quantum computing has catalyzed a new interdisciplinary field—Quantum Machine Learning (QML)—which promises to transcend the computational barriers faced by classical algorithms. This review paper systematically examines the evolution of ML from foundational statistical models to contemporary transformer architectures, and provides a structured analysis of QML paradigms including variational quantum circuits (VQCs), quantum kernel methods, and quantum neural networks (QNNs). Drawing from 95+ peer-reviewed publications indexed in Scopus and Science Citation Index (SCI) journals—with particular emphasis on 2025–2026 publications in IEEE Access, Nature Communications, Nature Computational Science, and Physical Review Letters—we analyze performance benchmarks, identify hardware constraints, and outline algorithmic innovations shaping the near-term quantum advantage landscape. Our findings indicate that while classical ML retains superiority in large-scale perception tasks, QML demonstrates significant advantages in combinatorial optimization and quantum chemistry simulation. Recent 2025 advances in error mitigation on superconducting qubits and improved VQC barren-plateau mitigation are narrowing this gap rapidly. The paper concludes with a forward-looking research agenda covering fault-tolerant QML, hybrid architectures, and quantum NLP. |
| Article Info: |
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Received: 18 Dec 2025, Received in revised form: 16 Jan 2026, Accepted: 23 Jan 2026, Available online: 28 Jan 2026 |
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Advanced Engineering Research and Science