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Recent Research Reviews Journal

Review of Quantum Machine Learning Integration in Modern Computing

Open Access
Published: 12 September, 2026
Pages: 318-331
  • Joy Iong Zong Chen Joy Iong Zong Chen
    Professor, Department of Electrical Engineering, Da-Yeh University, Changhua County, Taiwan
    Professor, Department of Electrical Engineering, Da-Yeh University, Changhua County, Taiwan
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How to Cite

Chen, Joy Iong Zong. 2026. “Review of Quantum Machine Learning Integration in Modern Computing”. Recent Research Reviews Journal 5 (2): 318-31. https://doi.org/10.36548/rrrj.2026.2.005.
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Keywords

Quantum Computing
Quantum Machine Learning (QML)
Quantum Neural Networks (QNN)
Machine Learning
Quantum Algorithms
Decoherence
Qubit Scalability
Error Correction
Data Encoding
Cybersecurity

Abstract

Quantum Machine Learning (QML) is a new multidisciplinary field that exploits the computational power of quantum computing for machine learning problems. Current research provides an overview of important QML algorithms, such as Quantum Support Vector Machines (QSVM), Quantum Neural Networks (QNN), Variational Quantum Eigensolvers (VQE), Quantum Approximate Optimization Algorithm (QAOA), and hybrid quantumclassical computing techniques, which have recently become more popular owing to hardware constraints in the NISQ period. Analysis reveals that QML provides an edge in working with high-dimensional data, optimization, and probabilistic modeling. Applications of the technology include various areas such as diagnostics, drug discovery, financial models, cybersecurity, and quantum cryptography (Quantum Key Distribution, QKD). Nevertheless, QML technology suffers from multiple difficulties, including the lack of scalable qubits, noise and decoherence, inefficient encoding methods, absence of standard benchmarking, and interpretability of models. Moreover, hybrid computing architecture and nascent quantum software development are also barriers.

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