Quantum Machine Learning (QML) is a new discipline that unites artificial intelligence and quantum computing and can address computational problems of big data analysis. Traditional machine learning algorithms may be pushed to their limits in dealing with the increased complexity and scale of today's data sets and thus are unable to find useful insights within a reasonable time frame. Quantum computing, capable of tapping quantum mechanical processes like superposition and entanglement, is capable of turning this field upside down. In this paper, the concepts behind quantum computing are discussed and how machine learning could be used using the assistance of quantum algorithms in order to better deal with big data. It explains the most optimal quantum algorithms like Quantum Support Vector Machines (QSVM), Quantum Principal Component Analysis (QPCA), and Quantum k-Means Clustering, and why they are better and faster compared to their classical counterparts. It also explores actual applications in medicine, finance, and artificial intelligence. It also addresses the limits and disadvantages of existing quantum technology like hardware limitations, noise, and complexity of algorithms. Last but not least, it also considers the future direction of trends within the field, with emphasis placed on hybrid quantum-classical systems and quantum machine learning application within the construction of big data analysis.
Quantum Machine Learning (QML) is a new discipline that unites artificial intelligence and quantum computing and can address computational problems of big data analysis. Traditional machine learning algorithms may be pushed to their limits in dealing with the increased complexity and scale of today's data sets and thus are unable to find useful insights within a reasonable time frame. Quantum computing, capable of tapping quantum mechanical processes like superposition and entanglement, is capable of turning this field upside down. In this paper, the concepts behind quantum computing are discussed and how machine learning could be used using the assistance of quantum algorithms in order to better deal with big data. It explains the most optimal quantum algorithms like Quantum Support Vector Machines (QSVM), Quantum Principal Component Analysis (QPCA), and Quantum k-Means Clustering, and why they are better and faster compared to their classical counterparts. It also explores actual applications in medicine, finance, and artificial intelligence. It also addresses the limits and disadvantages of existing quantum technology like hardware limitations, noise, and complexity of algorithms. Last but not least, it also considers the future direction of trends within the field, with emphasis placed on hybrid quantum-classical systems and quantum machine learning application within the construction of big data analysis.
The explosive growth of data generation in recent years has pushed the boundaries of classical machine learning algorithms, creating a pressing need for more efficient and scalable solutions. Quantum machine learning (QML), an emerging interdisciplinary field combining quantum computing and machine learning, offers the potential to address these challenges. This paper provides a comprehensive overview of QML for big data processing, exploring its opportunities, inherent limitations, and future directions. We delve into key QML algorithms like Quantum Principal Component Analysis (QPCA), Quantum Support Vector Machines (QSVM), and Quantum Neural Networks (QNNs), analyzing their computational complexities and potential speedups over classical counterparts. We also discuss the challenges associated with practical implementation, including hardware limitations, data encoding, and the development of robust error correction techniques. Finally, we outline promising research avenues that could unlock the full potential of QML for big data processing, ultimately revolutionizing fields like drug discovery, finance, and materials science
This rapid growth in data and the increased complexity of machine learning models have made it almost impossible to continue pushing forward with classical computing, thereby creating a pressing need for new computational paradigms. The following paper will examine how quantum machine learning answers the above questions by applying the principles of quantum mechanics to the art of artificial intelligence (AI) and big data analytics. We examine some of the current Quantum Machine Learning (QML) algorithms and their applications based on a framework that employs quantum-enhanced feature spaces, variational quantum circuits, and quantum-inspired classical algorithms. Our methodology combines theoretical analysis with empirical evaluation of these methods over standard benchmark datasets, using both quantum simulators and real quantum hardware. Key findings include a set of advantages carried by QML in specific fields, such as dimensionality reduction, optimization problems, and some clustering tasks. These are the quadratic speedup for support vector machines and the exponential advantage for principal component analysis. Our results also show that quantum error correction isn’t perfect and that loading classical data into a quantum system can be hard. We conclude that, while QML holds much promise, it is, by all means, clearly still a way off from being considered a panacea. It is awaiting further breakthroughs in quantum hardware, error mitigation techniques, and algorithm design in order to fully realize its potential. This work thus presents a roadmap for future research and highlights where QML is best placed to make major impactful differences in artificial intelligence and big data analytics.
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.
Abstract - The exponential growth of large-scale data has created significant challenges in storage, processing, and optimization that classical computing systems struggle to address efficiently. Quantum Machine Learning (QML) has emerged as a promising paradigm that integrates quantum computing concepts with machine learning techniques to tackle complex big data optimization problems. By utilizing quantum features such as superposition, entanglement, and parallelism, QML algorithms can accelerate tasks like clustering, classification, and dimensionality reduction, which are traditionally computationally demanding. Recent advancements indicate strong potential for quantum-inspired optimization in fields including cybersecurity, financial portfolio management, Industry 4.0, and healthcare analytics. This paper provides a comprehensive survey of QML algorithms designed for big data optimization, examining their advantages, limitations, and application domains. Additionally, it discusses the performance trade-offs associated with hybrid quantum–classical frameworks and highlights future research directions aimed at creating secure, scalable, and resource-efficient quantum solutions for large-scale, data-driven decision-making. As data volumes continue to expand at extremely rapid rates, processing big data with traditional machine learning (ML) techniques has become increasingly challenging. ML models often struggle with issues related to computational power, efficient parameter tuning, accurate model selection, and maintaining high performance when dealing with massive datasets. Deep learning approaches—such as convolutional neural networks (CNNs)—demand substantial computational resources to train on large-scale supervised learning tasks. Moreover, the difficulty of training these networks grows significantly as the size and complexity of the datasets increase. In response to these limitations, Quantum Machine Learning (QML) has emerged as a promising research domain at the intersection of quantum computing and machine learning. Quantum computers operate fundamentally differently from classical systems, using the principles of quantum mechanics to encode and process information. As a result, quantum computational methods have the potential to address certain problems that are computationally intensive or infeasible for classical machines. Index Terms— Quantum Machine Learning (QML); Big Data Optimization; Quantum Algorithms; Hybrid Quantum-Classical Models; Quantum Computing; Data Analytics
Quantum computing and machine learning (ML) are converging to form a new paradigm in computational science, offering transformative capabilities in data processing, optimization, and pattern recognition. Quantum systems leveraging superposition, entanglement, and parallelism enable theoretical speedups for complex ML tasks that challenge classical architectures. Emerging quantum algorithms, such as quantum support vector machines (QSVMs), quantum principal component analysis (QPCA), and variational quantum circuits (VQCs), show promise for addressing high-dimensional, computationally intensive problems. Despite current limitations posed by noisy qubits, limited coherence times, and data encoding constraints, progress is accelerating. Hybrid quantum–classical models, noise-resilient techniques, and expanding quantum cloud infrastructures are enhancing the practical reach of quantum machine learning (QML). Applications in molecular modeling, optimization, and cybersecurity are among the early areas showing tangible potential. With the eventual advent of fault–tolerant quantum processors, QML could enable breakthroughs across disciplines, from drug discovery and materials science to next-generation artificial intelligence (AI). As the volume and complexity of data continue to grow, QML is positioned to redefine the future of intelligent computation.
Quantum machine learning (QML) represents a cutting-edge convergence of quantum computing and artificial intelligence, aiming to leverage quantum phenomena such as superposition, entanglement, and quantum parallelism to enhance machine learning algorithms. By exploiting the computational advantages of quantum systems, QML offers the potential for exponential speedups in data processing, optimization, and pattern recognition tasks, particularly for high-dimensional and complex datasets. This article explores the foundational principles, algorithmic approaches, and practical applications of quantum machine learning across domains including finance, chemistry, drug discovery, optimization, and natural language processing. It discusses quantum-enhanced supervised and unsupervised learning, quantum neural networks, variational quantum algorithms, and hybrid quantum-classical architectures. Additionally, the article addresses challenges related to hardware limitations, noise, scalability, and interpretability, as well as the emerging trends in near-term quantum devices, quantum simulators, and integration with classical AI pipelines. Quantum machine learning holds the promise of revolutionizing computational intelligence, offering transformative capabilities for solving problems that are currently intractable for classical systems.