The emerging field of Quantum Machine learning (QML), which offers noticeably faster processing speed, is the result of the merger of Quantum Computing (QC) and Machine Learning (ML). Superposition and Entanglement are the fundamental concepts of QC, which increases computational scalability. This comprehensive overview examines QML foundations, algorithms, applications, and challenges of QML. When applied to the Cleveland Heart Disease Dataset, the Quantum Support Vector Classifier (QSVC) achieves an accuracy of 0.75 whereas the Variational Quantum Classifier (VQC) yields an accuracy of 0.62, thus showing that QSVC outperforms VQC. The main issue in QML that calls for effort and investment is noise. QML has the potential to advance technology and has its application in a variety of domains.
The emerging field of Quantum Machine learning (QML), which offers noticeably faster processing speed, is the result of the merger of Quantum Computing (QC) and Machine Learning (ML). Superposition and Entanglement are the fundamental concepts of QC, which increases computational scalability. This comprehensive overview examines QML foundations, algorithms, applications, and challenges of QML. When applied to the Cleveland Heart Disease Dataset, the Quantum Support Vector Classifier (QSVC) achieves an accuracy of 0.75 whereas the Variational Quantum Classifier (VQC) yields an accuracy of 0.62, thus showing that QSVC outperforms VQC. The main issue in QML that calls for effort and investment is noise. QML has the potential to advance technology and has its application in a variety of domains.
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 represents a paradigm shift at the intersection of Quantum Computing and Machine Learning, leveraging quantum phenomena such as superposition, entanglement, and quantum parallelism to address the limitations of classical approaches in processing high-dimensional and large-scale datasets. This survey provides a comprehensive analysis of Quantum Machine Learning, detailing foundational concepts, algorithmic advancements, and their applications across domains such as healthcare, finance, and quantum chemistry. Key techniques, including Quantum Support Vector Machine, Quantum Neural Network, Quantum Decision Trees, and hybrid quantum-classical models, are explored with a focus on their theoretical foundations, computational benefits, and comparative performance against classical counterparts. While the potential for exponential speedups and enhanced efficiency is evident, the field faces significant challenges, including hardware constraints, noise, and limited qubit coherence in the current era of Noisy Intermediate-Scale Quantum devices. Emerging solutions, such as error mitigation techniques, hybrid frameworks, and advancements in quantum hardware, are discussed as critical enablers for scalable and fault-tolerant Quantum Machine Learning systems. By synthesizing state-of-the-art developments and identifying research gaps, this survey aims to provide a foundational resource for advancing Quantum Machine Learning toward practical, real-world applications in tackling computationally intensive problems.
Abstract Quantum Machine Learning (QML) is an emerging interdisciplinary field that merges quantum computing with classical machine learning (ML) to enhance computational efficiency, scalability, and accuracy in data-driven tasks. By leveraging quantum properties such as superposition, entanglement, and quantum parallelism, QML has the potential to solve complex problems that are intractable for classical models. This survey provides a comprehensive, systematic review of recent advancements in QML, conducted following the PRISMA 2020 guidelines and covering fundamental encoding techniques that enable quantum computers to process classical data, as well as essential tools, datasets, and evaluation metrics used in quantum model development. Furthermore, it explores various QML approaches, including supervised, semi-supervised, unsupervised, and reinforcement learning, alongside quantum deep learning (QDL) models such as Quantum Neural Networks (QNNs) and Variational Quantum Circuits (VQCs). The paper also discusses the challenges in QML, including hardware limitations, scalability issues, and the need for improved quantum error correction. A dual-axis taxonomy simultaneously classifies algorithms by learning paradigm and quantum-classical integration level – a structure not found in prior surveys. The survey also covers Quantum Transfer Learning, Quantum Graph Neural Networks, and hardware-aware encoding selection, and introduces the first structured adversarial robustness taxonomy and Explainable QML treatment in a comprehensive QML survey. Together, these contributions position this work as both a reference and a methodological guide for advancing QML research and applications.
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.
The exponential growth of complex datasets has exposed the limitations of classical machine learning algorithms in speed, scalability, and pattern detection capabilities. Quantum Machine Learning (QML) integrates quantum computation principles—including superposition, entanglement, and quantum interference—into machine learning pipelines to accelerate data processing and optimize pattern recognition. This paper analyzes the theoretical foundations, algorithmic frameworks, performance advantages, hardware feasibility, and real-world applications of QML models, particularly in high-complexity systems such as genomics, financial analytics, cybersecurity, and climate modeling. Through analytical comparisons, datasets, and quantum simulator performance benchmarks, results demonstrate that QML models outperform classical ML by factors ranging from 10× to 1000× in processing speed and accuracy for high-dimensional nonlinear datasets. Critical challenges including qubit instability, data encoding overheads, and hardware scalability are discussed along with emerging solutions like variational quantum circuits and hybrid quantum-classical architectures. The findings establish QML as a foundational technology for future artificial intelligence advancements in ultra-large-scale data environments.
Quantum Machine Learning (QML) is an emerging discipline at the intersection of quantum computing and artificial intelligence, providing new approaches to address the increasing computational and analytical challenges in healthcare. This chapter explores how quantum principles, such as superposition, entanglement, and interference, can enhance the efficiency and accuracy of machine learning when applied to complex biomedical data. It presents an overview of the fundamental concepts of QML, compares its capabilities with those of traditional machine learning systems, and examines important algorithmic frameworks, including Quantum Support Vector Machines, Quantum Neural Networks, and Variational Quantum Algorithms. Practical examples are explored across several healthcare domains—drug discovery, medical imaging, genomic research, patient monitoring, and hospital resource management—showing how quantum methods are beginning to improve data interpretation and clinical decision-making. The chapter also outlines current limitations related to hardware capacity, data preparation, interpretability, and regulatory standards, while emphasizing ethical and cybersecurity considerations that accompany quantum-enabled systems. Finally, it looks ahead to a future in which accessible quantum computing, open-source development, and collaboration among scientists, clinicians, and technologists drive a new generation of personalized, secure, and data-driven healthcare.