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.
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.
Quantum machine learning (QML) has emerged as a promising interdisciplinary field that integrates principles of quantum computing with machine learning techniques to address complex computational challenges. By leveraging quantum phenomena such as superposition and entanglement, QML aims to enhance learning efficiency, improve model performance, and enable the exploration of high-dimensional feature spaces that are intractable for classical methods. This paper presents a comprehensive review of recent developments in QML, covering fundamental concepts, algorithmic taxonomies, data encoding techniques, implementation challenges, and real-world applications. Key approaches, including quantum support vector machines (QSVM), variational quantum circuits (VQC), and quantum neural networks (QNN), are systematically analyzed. Furthermore, critical challenges, including noisy intermediate-scale quantum (NISQ) limitations, barren plateaus, data encoding bottlenecks, and the lack of demonstrated quantum advantage, are discussed in detail. The review also highlights emerging applications in material informatics, energy systems, healthcare, and optimization problems. Finally, future research directions are outlined, emphasizing the need for advancements in quantum hardware, scalable algorithms, hybrid frameworks, and standardized benchmarking. This work aims to provide a structured perspective on the current state of QML and to identify opportunities in deploy it effectively in solve real-world problems.
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 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.
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
The burgeoning field of Quantum Machine Learning (QML) represents a transformative convergence of artificial intelligence and quantum computing. Harnessing the unique capabilities of quantum mechanics, QML aims to develop novel algorithms that outperform classical machine learning approaches in specific tasks, potentially revolutionizing fields such as drug discovery, materials science, finance, and cybersecurity. This paper explores the fundamental principles of QML, examines key algorithms and architectures like Quantum Support Vector Machines (QSVMs), Variational Quantum Eigensolver (VQE) for Machine Learning, and Quantum Generative Adversarial Networks (QGANs), and discusses the current challenges and future opportunities in realizing the full potential of this exciting interdisciplinary field. We also critically analyze the potential of QML to address computationally intractable problems and provide a roadmap for future research directions
Two of the most disruptive technologies of the 21st century are quantum computing and artificial intelligence. Their intersection has led to the emergence of a new discipline referred to as Quantum Machine Learning (QML), which aims to enhance the capabilities of classical machine learning by leveraging the computational advantages of quantum devices. This paper provides a survey of the most advanced Quantum Machine Learning (QML) algorithms, including Quantum Support Vector Machines (QSVMs), Quantum k-nearest Neighbours (QkNN), Quantum Principal Component Analysis (QPCA), Quantum Neural Networks (QNNs), and Quantum Reinforcement Learning (QRL). The theoretical and practical status, as well as the empirical performance, of these algorithms, were summarised using a structured review method. The findings reveal a potential for speed-ups in classification, clustering, and optimisation among a range of applications, particularly for perfect quantum systems. However, hardware constraints, software irregularities, and training issues, such as barren plateaus, have limited the practical utility of this approach. Applications of QML in areas such as disaster preparedness and management, drug discovery, environmental sustainability, urban planning methodology, NLP, and finance demonstrate both the potential and current limitations of QML, with most applications still at the proof-of-concept level. In this review, we conclude that QML could be revolutionary, but its feasibility ultimately relies on improvements in physical hardware, the robustness of algorithms, and the standardisation of benchmarks.