Quantum Enhanced Machine Learning: A Comprehensive Review

Nimish Mishra, Manik Kapil, H R Rakesh +20 more

S. Poongothai, R. Senthil Kumar

Quantum computing (QC) has emerged as a revolutionary technology that can solve computationally rigid problems significantly faster than classical computers, potentially facilitating the development of Machine Learning (ML). This paper provides a deeper review of Quantum Machine Learning (QML), covering basic principles, quantum-classical techniques and real-world healthcare applications. The review paper evaluates the latest developments in quantum-enhanced ML models, including quantum support vector (QSVMs), quantum neural networks (QNNs), quantum principal component (QPCAs), quantum convolutional neural networks (QCNNs) and quantum K-nearest neighbor (QKNNs) techniques. Additionally, the review paper discusses complex challenges such as low computational power, huge, overfitting risk, high-dimensional and noisy datasets in classical ML techniques. Finally, the review paper summarizes the effectiveness of QML techniques compared with classical ML classification techniques based on their accuracy values. This review serves as a comprehensive resource for researchers exploring the classification of various non-communicable diseases (NCDs) on the intersection of quantum computing and machine learning.

Rattan Rana, Divyanshi Sharma

This study presents an in-depth and focused review of Quantum-based conceptual optimization Strategies and their impact in enhancing classical machine learning training. Quantum-inspired methods simulate key fundamental concepts of quantum mechanics, including superposition, entanglement, and quantum tunneling within classical computing environments to improve optimization efficiency. These techniques enable better exploration of complex search spaces, faster convergence, and improved capability to avoid local minima. The review examines major quantum-inspired algorithms, including evolutionary models, annealing-based methods, and hybrid quantum–classical frameworks, highlighting their strengths, limitations, and suitability for modern ML tasks. The study also discusses their relevance to sustainable, energy-efficient digital transformation in education and research. The goal is to present clear insights into how quantum-inspired optimization can strengthen traditional ML training without requiring quantum hardware.

Madhusudan Singh Irish Singh, Bharat S. Rawal

James C. L. Chow

Medical decision-making is increasingly integrating quantum computing (QC) and machine learning (ML) to analyze complex datasets, improve diagnostics, and enable personalized treatments. While QC holds the potential to accelerate optimization, drug discovery, and genomic analysis as hardware capabilities advance, current implementations remain limited compared to classical computing in many practical applications. Meanwhile, ML has already demonstrated significant success in medical imaging, predictive modeling, and decision support. Their convergence, particularly through quantum machine learning (QML), presents opportunities for future advancements in processing high-dimensional healthcare data and improving clinical outcomes. This review examines the foundational concepts, key applications, and challenges of these technologies in healthcare, explores their potential synergy in solving clinical problems, and outlines future directions for quantum-enhanced ML in medical decision-making.

Toran Verma, Botcha Kishore Kumar, Janga Rajendar +1 more