This tutorial covers a hands-on introduction to quantum machine learning. Foundational concepts of quantum information science (QIS) are presented (qubits, single and multiple qubit gates, measurements, and entanglement). Building on that, foundational concepts of quantum machine learning (QML) are introduced (parametrized circuits, data encoding, and feature mapping). Then, QML models are discussed (quantum support vector machine, quantum feedforward neural network, and quantum convolutional neural network). All the aforementioned topics and concepts are examined using codes run on a quantum computer simulator. All the covered materials assume a novice audience interested in learning about QML. Further reading and software packages and frameworks are shared with the audience.
This tutorial covers a hands-on introduction to quantum machine learning. Foundational concepts of quantum information science (QIS) are presented (qubits, single and multiple qubit gates, measurements, and entanglement). Building on that, foundational concepts of quantum machine learning (QML) are introduced (parametrized circuits, data encoding, and feature mapping). Then, QML models are discussed (quantum support vector machine, quantum feedforward neural network, and quantum convolutional neural network). All the aforementioned topics and concepts are examined using codes run on a quantum computer simulator. All the covered materials assume a novice audience interested in learning about QML. Further reading and software packages and frameworks are shared with the audience.
This tutorial offers a hands-on introduction into the captivating field of quantum machine learning (QML). Beginning with the bedrock of quantum information science (QIS)-including essential elements like qubits, single and multiple qubit gates, measurements, and entanglement-the session swiftly progresses to foundational QML concepts. Participants will explore parameterized or variational circuits, data encoding or embedding techniques, and quantum circuit design principles. Delving deeper, attendees will examine various QML models, including the quantum support vector machine (QSVM), quantum feed-forward neural network (QNN), and quantum convolutional neural network (QCNN). Pushing boundaries, the tutorial delves into cutting-edge QML models such as quantum recurrent neural networks (QRNN) and quantum reinforcement learning (QRL), alongside privacy-preserving techniques like quantum federated machine learning, bolstered by concrete programming examples. Throughout the tutorial, all topics and concepts are brought to life through practical demonstrations executed on a quantum computer simulator. Designed with novices in mind, the content caters to those eager to embark on their journey into QML. Attendees will also receive guidance on further reading materials, as well as software packages and frameworks to explore beyond the session.
This tutorial introduces key concepts in Quantum Machine Learning (QML), covering qubits, gates, entanglement, parameterized circuits, and quantum neural networks (QNNs). It highlights recent advances in quantum-enhanced model compression and quantum architecture search (QAS), which improve QML efficiency and scalability. Attendees will gain hands-on experience with QML implementations on quantum simulators and receive guidance on tools for continued learning. Designed for beginners, the tutorial aims to foster cross-disciplinary innovation at the intersection of quantum computing and AI.
Quantum Machine Learning (QML) is the convergence of two cutting-edge technologies—quantum computing and artificial intelligence—dedicated to rewriting computational horizons of pattern recognition, optimization, and prediction based on data. The present chapter is an introductory tutorial and cutting-edge coverage of theory foundations, algorithmic developments, and pedagogical models needed to bring QML into secondary and undergraduate classrooms. We then dispel magic from fundamental quantum-computing concepts such as qubits, superposition, entanglement, and quantum gates and demonstrate how they enable quantum algorithms such as the Quantum Fourier Transform, amplitude amplification, variational circuits, and quantum support-vector machines. From these building blocks, the chapter follows classical machine-learning pipelines—data encoding, feature extraction, model training, inference, and evaluation—and demonstrates how they map to their quantum equivalents, with emphasis on hybrid quantum-classical paradigms that are implementable on near-term noisy intermediate-scale quantum (NISQ) devices.
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) is an emerging interdisciplinary field that combines the principles of quantum mechanics with machine learning algorithms, offering promising solutions to complex computational problems. This paper provides an extensive introduction to quantum machine learning, exploring the synergies between artificial intelligence and quantum technology. It begins by elucidating the foundational concepts of quantum computing, including quantum bits (qubits), quantum gates, superposition, and entanglement. Subsequently, the paper delves into the basic principles of machine learning and the challenges faced by classical methods in handling large-scale data and solving computationally intensive tasks. With a strong foundation in place, the focus shifts towards the integration of quantum mechanics with machine learning, showcasing the advantages and potential breakthroughs QML offers over classical approaches. The exploration of quantum algorithms for machine learning, such as quantum support vector machines (QSVM), quantum neural networks (QNN), and quantum variational algorithms, reveals the power of quantum-enhanced techniques in data classification, regression, and clustering.
In the current noisy intermediate-scale quantum (NISQ) era, quantum machine learning is emerging as a dominant paradigm to program gate-based quantum computers. In quantum machine learning, the gates of a quantum circuit are parametrized, and the parameters are tuned via classical optimization based on data and on measurements of the outputs of the circuit. Parametrized quantum circuits (PQCs) can efficiently address combinatorial optimization problems, implement probabilistic generative models, and carry out inference (classification and regression). This monograph provides a self-contained introduction to quantum machine learning for an audience of engineers with a background in probability and linear algebra. It first describes the necessary background, concepts, and tools necessary to describe quantum operations and measurements. Then, it covers parametrized quantum circuits, the variational quantum eigensolver, as well as unsupervised and supervised quantum machine learning formulations.