Recently, machine learning is benefiting from advan-tages of quantum computing which has resulted in a new stream of algorithms known as quantum machine learning algorithms. This paper presents the literature describing implementations of quantum machine learning algorithms in the various quantum machine learning frameworks. In addition, for each of the ob-served algorithms and frameworks, the literature in which they are described is stated. To the best of our knowledge, this is so far the most comprehensive overview of the existing QML algorithms with their corresponding implementation frameworks.
Recently, machine learning is benefiting from advan-tages of quantum computing which has resulted in a new stream of algorithms known as quantum machine learning algorithms. This paper presents the literature describing implementations of quantum machine learning algorithms in the various quantum machine learning frameworks. In addition, for each of the ob-served algorithms and frameworks, the literature in which they are described is stated. To the best of our knowledge, this is so far the most comprehensive overview of the existing QML algorithms with their corresponding implementation frameworks.
Quantum machine learning combines quantum computing with machine learning to solve complex computational problems more efficiently than classical approaches. This survey provides an introduction to the foundations, algorithms, frameworks, data and applications of quantum machine learning, serving as a resource for researchers and practitioners. We begin by reviewing existing surveys to identify gaps that this work addresses, followed by a detailed discussion of the foundational principles of quantum mechanics and machine learning essential for quantum machine learning. Key algorithms are examined, highlighting their mechanisms, advantages, and applications across various domains. Current frameworks and platforms for implementing quantum machine learning algorithms are explored, emphasizing their unique features and suitability for different contexts. Existing quantum datasets for practical usage are also reported and commented on. This survey also reviews over 135 articles, categorized into theoretical and practical contributions, to identify key advances, limitations, and application areas within quantum machine learning. Critical challenges such as hardware limitations, error rates, and scalability are analyzed to detect the obstacles that must be addressed for practical deployment. By synthesizing these elements into a structured overview, this survey aims at serving as both an introduction and a guide for advancing research and development in this disruptive field.
Quantum Machine Learning (QML) represents a promising frontier of research where the principles of quantum mechanics are combined with machine learning to offer potentially superior performance over classical counterparts. This review article explores the current advancements in QML, specifically focusing on the performance improvement and comparison between standard classical algorithms and their quantum implementations. We initiate with an overview of fundamental quantum algorithms that have found applicability within machine learning contexts, such as quantum principal component analysis and quantum support vector machines. The review then delves into a critical assessment of these quantum approaches, highlighting case studies where quantum implementations have demonstrated computational speedups or improved accuracy. Furthermore, we investigate the inherent challenges and limitations that currently hinder the widespread adoption of QML technologies, including the issues of decoherence, error rates, and quantum resource requirements. In addition, the review provides insights into the current state of quantum hardware and its scalability for machine learning tasks. Throughout, comparisons are made to outline scenarios in which quantum machine learning presents a genuine advantage over classical techniques, supported by performance metrics and benchmark datasets. The article concludes by discussing future directions in QML research, with an emphasis on algorithmic innovation and hardware improvement, to realize the full potential of quantum-enhanced machine learning.
Quantum Machine Learning (QML) is the emerging confluence of quantum computing and artificial intelligence that promises to solve computational problems inaccessible to classical systems. Using quantum principles such as superposition, entanglement, and interference, QML promises exponential speed-ups and new paradigms for data processing in machine learning tasks. This review gives an overview of QML, from advancements in quantum-enhanced classical ML to native quantum algorithms and hybrid quantum-classical frameworks. It varies from applications in optimization, drug discovery, and quantum-secured communications, showcasing how QML can change healthcare, finance, and logistics industries. Even though this approach holds so much promise, significant challenges remain to be addressed-noisy qubits, error correction, and limitations in data encoding-that must be overcome by interdisciplinary research soon. The paper tries to collate the state of the art of QML in theoretical underpinnings, practical applications, and directions into the future.
QML is a budding interdisciplinary arena that associates quantum computing and regular machine learning methods to progress computational efficacy and provide solutions for complex problems. With the fast development of quantum technologies, QML has received a lot of devotion for its high possible applications in AI, cybersecurity, healthcare, and financial modeling. This review covers all QML frameworks, with IBM Qiskit, Google Cirq, PennyLane, TensorFlow Quantum, and Microsoft Q\#. We thoroughly classify current QML algorithms, including QSVM, QNN, VQC, and Quantum Boltzmann Machines (QBM), highlighting their advantages and some restrictions. Additionally, we explore significant barriers to extensive QML acceptance, like hardware constraints, scalability matters, quantum noise, and the need for explainability. We also deliberate auspicious research directions, which contain hybrid quantum-classical learning models, quantum federated learning, and QML framework consistency. This article wants to serve as an introductory reference for researchers and experts interested in the improvement of QML by providing a properly planned review of existing work and recognizing future opportunities.
This paper presents an overview of quantum machine learning (QML) techniques, their approach, and their landscape. QML is a rapidly evolving field of study which has recently generated lots of interest due to improvements it promises over the classical machine learning approaches. This paper presents an overview of the application of quantum information theory on machine learning algorithms. It also discusses various designs, techniques, and algorithms of classical machine learning and quantum information theory and how they can be fused with each other. The goal of this paper is to analyze algorithms and techniques used in QML. The paper also presents some of the promising algorithms used in QML, which can be executed on a real quantum computer over cloud computing platforms with the aim to evaluate its performance based on given parameters.
Quantum machine learning (QML) combined with classical machine learning provides hitherto unexplored computational capabilities for problem resolution. The goal of this chapter is to present a thorough analysis of the ideas and uses of quantum computing capacity on machine learning methods. A full analysis of popular QML algorithms, such as quantum principal component analysis, quantum support vector machines, and quantum neural networks, is conducted after an explanation of the foundations of the quantum concept and its distinguishing features. The critical assessment considers robustness, interpretability, and scalability in addition to evaluating the benefits and drawbacks of each method. The primary focus is on the merging of classical and quantum models, looking at areas of convergence that make use of the most advantageous aspects of both paradigms. This work addresses challenges associated with putting quantum algorithms into practice, including hardware constraints and error-reduction strategies. This chapter evaluates the state of quantum computing technologies and their applicability to real-world quantum machine learning applications, in addition to reviewing particular algorithms. This is jam-packed with cutting-edge discoveries and trends that shed light on where QML research will go in the future. The final section of this chapter discusses potential future paths for QML, emphasizing the need for unified benchmarks, enhanced quantum hardware, and cooperative efforts between the machine learning and quantum computing groups. This concise review attempts to serve as a useful reference for scholars, professionals, and hobbyists navigating the quickly evolving field of quantum-enhanced machine learning by providing an extensive overview of QML algorithms.