This chapter introduces the field of quantum machine learning (QML) by outlining its motivations, foundational concepts, and scope. It provides a high-level overview of how quantum computing principles can be leveraged to enhance or reframe learning tasks, and presents a taxonomy of QML approaches based on the type of data and model used. By bridging classical learning theory with quantum computational models, this chapter sets the stage for deeper exploration of algorithms, architectures, and applications in the chapters that follow.
This chapter introduces the field of quantum machine learning (QML) by outlining its motivations, foundational concepts, and scope. It provides a high-level overview of how quantum computing principles can be leveraged to enhance or reframe learning tasks, and presents a taxonomy of QML approaches based on the type of data and model used. By bridging classical learning theory with quantum computational models, this chapter sets the stage for deeper exploration of algorithms, architectures, and applications in the chapters that follow.
Quantum Machine Learning (QML) has emerged as a promising interdisciplinary field that combines quantum computing with machine learning to address complex computational problems. This survey provides a comprehensive overview of the theoretical foundations, unified taxonomy, and key methodologies in QML. We discuss quantum data encoding techniques, variational quantum models, neural-inspired quantum architectures, and quantum data learning approaches, along with their associated challenges and limitations. The survey further examines evaluation strategies, the concept of quantum advantage, and the role of current quantum hardware and software platforms in advancing QML research. In addition, major application domains and the growing importance of trustworthy QML, including interpretability, robustness, and security, are explored. Finally, we highlight open challenges and future research directions to provide insights into the evolving landscape of Quantum Machine Learning.
Recent advancements in quantum computing (QC) and machine learning (ML) have fueled significant research efforts aimed at integrating these two transformative technologies. Quantum machine learning (QML), an emerging interdisciplinary field, leverages quantum principles to enhance the performance of ML algorithms. Concurrently, the exploration of systematic and automated approaches for designing high-performance quantum circuit architectures for QML tasks has gained prominence, as these methods empower researchers outside the quantum computing domain to effectively utilize quantum-enhanced tools. This tutorial will provide an in-depth overview of recent breakthroughs in both areas, highlighting their potential to expand the application landscape of QML across diverse fields.
Quantum machine learning (QML) is a computational paradigm that seeks to apply quantum-mechanical resources to solve learning problems. As such, the goal of this framework is to leverage quantum processors to tackle optimization, supervised, unsupervised and reinforcement learning, and generative modeling-among other tasks-more efficiently than classical models. Here we offer a high level overview of QML, focusing on settings where the quantum device is the primary learning or data generating unit. We outline the field's tensions between practicality and guarantees, access models and speedups, and classical baselines and claimed quantum advantages-flagging where evidence is strong, where it is conditional or still lacking, and where open questions remain. By shedding light on these nuances and debates, we aim to provide a friendly map of the QML landscape so that the reader can judge when-and under what assumptions-quantum approaches may offer real benefits.
The integration of quantum computing with machine learning emerged as an approach to solving complex computational problems. This chapter provides an overview of quantum machine learning (QML), exploring the fundamental concepts, algorithms, and applications at the intersection of quantum computing and machine learning. We begin by introducing the basic principles of quantum mechanics relevant to QML, including qubits, quantum gates, and quantum parallelism. Then, we explore key topics such as quantum entanglement, superposition, and their role in quantum algorithms. We then discuss the landscape of QML, contrasting classical and quantum approaches and highlighting the potential of quantum computing to enhance machine learning algorithms. Furthermore, we discuss prominent QML algorithms, such as quantum support vector machines (QSVM) and quantum neural networks (QNN), elucidating their mathematical formulations and differences from classical counterparts. Real-world examples showcasing quantum speedup in machine learning tasks are presented to underscore the transformative potential of quantum computing. Finally, an overview of QML tools and frameworks is provided, enabling researchers to embark on their journey in exploring the exciting field of QML.
Quantum machine learning (QML) represents the fusion of quantum computing algorithms with machine learning techniques. A prominent application of QML involves employing machine learning algorithms to analyze classical data using a quantum computer, known as quantum-enhanced machine learning. This interdisciplinary field merges concepts from quantum physics and machine learning to pioneer new algorithms and methodologies for data processing and analysis. QML seeks to harness unique quantum properties like superposition and entanglement to enhance the performance of machine learning models. It explores the synergy between quantum computing and machine learning, aiming to leverage quantum systems’ inherent characteristics for advanced data processing capabilities. This chapter provides an in-depth exploration of QML’s comprehensive approach.
Quantum Machine Learning (QML) represents an emerging interdisciplinary field that harnesses quantum computing principles to enhance machine learning algorithms and develop quantum-native learning paradigms. This convergence exploits fundamental quantum mechanical phenomena superposition, entanglement, and quantum interference—to potentially achieve exponential computational advantages over classical approaches for specific algorithmic tasks. The theoretical foundation of QML rests on quantum systems' ability to encode information in exponentially scaling state spaces, enabling complex data representation in logarithmically fewer qubits compared to classical bits. Key quantum advantage mechanisms include quantum parallelism through superposition states, entanglement based correlations for efficient encoding of data relationships, and quantum interference effects that amplify optimal solutions while suppressing suboptimal ones. Contemporary QML approaches encompass several algorithmic frameworks. Variational Quantum Algorithms (VQAs) represent the most promising near-term strategy, utilizing hybrid quantum-classical optimization with parameterized quantum circuits optimized through classical feedback loops. Quantum Neural Networks extend classical architectures into quantum domains using trainable quantum gates, while quantum kernel methods leverage quantum feature maps to project data into high-dimensional Hilbert spaces for enhanced classification and regression tasks.