Quantum machine learning (QML), which integrates quantum computing and machine learning techniques, has attracted much research attention in recent years. A previous study proposed a four-class classification for QML techniques based on whether the data and computations are in quantum or classical forms. Based on the four-class classification, I introduced examples and applications for each class of QML techniques. Two of the four classes have more research results than the other two. They are the CC class, where classical data are processed by classical models with the quantum computing flavor, and the CQ class, where classical data are processed by quantum-based models. More research results related to the CC and CQ classes are presented in this article. The introduced techniques include quantum-inspired binary classifiers, quantum-inspired multi-class classifiers, quantum-inspired support vector machines, quantum-inspired linear regression, and quantum-inspired recommendation systems of the CC class. They also include quantum support vector machines, quantum neural networks, quantum convolutional neural networks, and quantum autoencoders of the CQ class. Finally, possible research directions are suggested for each class, especially for the CC and CQ classes. The suggested research directions encompass hybrid classical-quantum machine learning, quantum K-nearest neighbor, quantum clustering, quantum generative adversarial network, and quantum reinforcement learning techniques.
Quantum machine learning (QML), which integrates quantum computing and machine learning techniques, has attracted much research attention in recent years. A previous study proposed a four-class classification for QML techniques based on whether the data and computations are in quantum or classical forms. Based on the four-class classification, I introduced examples and applications for each class of QML techniques. Two of the four classes have more research results than the other two. They are the CC class, where classical data are processed by classical models with the quantum computing flavor, and the CQ class, where classical data are processed by quantum-based models. More research results related to the CC and CQ classes are presented in this article. The introduced techniques include quantum-inspired binary classifiers, quantum-inspired multi-class classifiers, quantum-inspired support vector machines, quantum-inspired linear regression, and quantum-inspired recommendation systems of the CC class. They also include quantum support vector machines, quantum neural networks, quantum convolutional neural networks, and quantum autoencoders of the CQ class. Finally, possible research directions are suggested for each class, especially for the CC and CQ classes. The suggested research directions encompass hybrid classical-quantum machine learning, quantum K-nearest neighbor, quantum clustering, quantum generative adversarial network, and quantum reinforcement learning techniques.
Quantum Machine Learning (QML) and Quantum Deep Learning (QDL) are emerging research areas that combine quantum computing with machine learning and deep learning techniques.These approaches aim to improve computational speed, feature representation, optimisation, and learning performance in complex data-driven problems.This review paper presents a structured discussion of recent developments in QML and QDL, focusing on major algorithms, application areas, implementation approaches, current limitations, and future research directions.The review covers important quantum-based learning methods, including quantum support vector machines, quantum k-nearest neighbours, quantum principal component analysis, quantum neural networks, variational quantum classifiers, quantum convolutional neural networks, quantum reinforcement learning, quantum generative models, and hybrid quantum-classical models.The paper also discusses how these methods are being explored in healthcare, finance, cybersecurity, communication networks, image processing, natural language processing, and optimisation.However, the review shows that most QML and QDL models are still in the early stages of research.Current challenges include noisy quantum hardware, limited qubit count, data encoding issues, circuit design problems, difficulties with model training, and a lack of standardised benchmarks.The review concludes that QML and QDL have strong future potential, but their practical success depends on improved hardware, stronger algorithms, reliable frameworks, and fair comparison with classical models.
Quantum Machine Learning (QML) is an emerging interdisciplinary field that integrates quantum computing with classical machine learning techniques to enhance computational efficiency and solve complex problems beyond the capabilities of classical systems. This paper explores fundamental QML algorithms, including quantum-enhanced data processing, quantum neural networks, and quantum support vector machines. We discuss how quantum speedup can be achieved through quantum parallelism and entanglement, leading to improvements in optimization and data classification tasks. Additionally, we highlight applications of QML in areas such as drug discovery, financial modeling, and cryptography. While current quantum hardware imposes limitations, ongoing advancements in quantum algorithms and error correction techniques suggest a promising future for QML. We conclude with a discussion on the challenges and future directions in the field, emphasizing the need for hybrid quantum-classical approaches and scalable quantum hardware.
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.
As the limits of classical machine learning (ML) become increasingly evident—especially in terms of computational scalability and data processing—quantum computing emerges as a promising frontier capable of addressing these challenges. Quantum Machine Learning (QML) represents the intersection of quantum computing and traditional ML, aiming to leverage quantum phenomena such as superposition, entanglement, and quantum parallelism to enhance learning capabilities, accelerate training, and solve complex problems that are intractable for classical algorithms. This paper provides a comprehensive overview of the evolving field of QML, beginning with the theoretical foundations of quantum computing and its synergy with classical machine learning principles. We explore key QML algorithms, including quantum support vector machines (QSVM), variational quantum classifiers (VQC), quantum neural networks (QNN), and hybrid quantum-classical approaches that exemplify the practical integration of both paradigms. We further examine real-world applications of QML across domains such as drug discovery, finance, climate modeling, and optimization, where quantum enhancements have begun to show early but promising results. Despite its theoretical potential, QML faces substantial limitations, including quantum hardware instability, limited qubit scalability, noise sensitivity, and unresolved challenges in quantum data encoding and interpretability. In synthesizing recent advancements and persistent obstacles, this paper highlights the importance of continued interdisciplinary research, algorithmic refinement, and hardware evolution. We also outline future directions that could lead QML from experimental promise to practical utility, including developments in quantum error correction, benchmarking standards, and application-specific quantum architectures
Quantum Machine Learning (QML) has emerged as a paradigm that combines the computational advantages of quantum computing with the predictive capabilities of machine learning to address complex data classification problems. As data dimensionality increases rapidly and classical learning algorithms face scalability constraints, QML leverages quantum parallelism, entanglement, and high-dimensional Hilbert space representations to enhance learning performance. This paper reviews and analyses advanced QML algorithms, including Quantum Support Vector Machines (QSVM), Variational Quantum Circuits (VQC), Quantum Neural Networks (QNN), and Quantum Kernel Estimation, for optimizing both binary and multi-class classification tasks under high-complexity conditions. The proposed QML framework is evaluated on benchmark datasets like MNIST, the Breast Cancer Wisconsin (BCW) dataset, and synthetic nonlinear datasets, and is compared against classical machine learning baselines, including Support Vector Machines (SVM), Random Forests (RF), and Deep Neural Networks (DNN). The results demonstrate notable improvements in classification accuracy (up to 96.8%), decision margins, and computational efficiency in quantum-suitable data regimes, while also highlighting current limitations related to noise, circuit depth, and hardware constraints. Overall, the study presents a unified QML framework, theoretical formulations, and experimental evaluations that illustrate the potential of quantum algorithms for next-generation classification tasks.
Quantum machine learning (QML) provides a transformative approach to data analysis by integrating the principles of quantum computing with classical machine learning methods. With the exponential growth of data and the increasing complexity of computational tasks, quantum algorithms offer tremendous advantages in terms of processing speed, memory efficiency, and the ability to resolve issues intractable for classical systems. In this work, the use of QML techniques for both supervised and unsupervised learning problems is explored. Quantum-enhanced models such Quantum Support Vector Machines (QSVMs) and Quantum Neural Networks (QNNs) show outstanding performance in classification and regression tasks by using quantum kernels and entanglement in supervised learning. Moreover, hybrid quantum-classical solutions offer useful implementations on noisy intermediate-scale quantum (NISQ) devices, hence bridging the gap between present quantum technology and practical uses. By means of comparative analysis, this paper emphasizes the possible benefits and drawbacks of QML, thereby providing understanding of its future importance in sectors including material science, finance, and healthcare. In the end, QML opens the path for a new era of intelligent data processing and solves until unthinkable difficult challenges.