A Practical Guide to Quantum Machine Learning and Quantum Optimization

This book provides a practical introduction to modern quantum computing algorithms in the fields of quantum machine learning and quantum optimization. It begins with the fundamentals of quantum computing and programming tools, then explores topics such as optimization problem formulation using QUBO and Ising models, quantum annealing, and algorithms including QAOA, VQE, and Grover Adaptive Search (GAS). It also covers quantum machine learning models, including quantum support vector machines (QSVMs), quantum neural networks, and quantum generative adversarial networks (QGANs), as well as hybrid classical–quantum architectures. Through programming examples, the book introduces the use of quantum simulators and real quantum hardware, along with tools such as Qiskit, PennyLane, and D-Wave Leap. The appendices review relevant mathematical foundations, including complex numbers and linear algebra.