This book explores the intersection of quantum computing and machine learning. It details classical machine learning concepts, such as supervised learning, various models (linear, nonlinear, probabilistic), and training methods (gradient descent). It then introduces quantum computing fundamentals, including qubits, quantum gates, and algorithms like Grover's search. The core focus is on how quantum algorithms can be applied to machine learning tasks, including inference and training, examining different encoding techniques (amplitude, basis, Hamiltonian) and their implications for efficiency and feasibility. Finally, it discusses hybrid classical-quantum approaches where classical and quantum computations work together.

Link to book: https://www.amazon.com/Supervised-Learning-Quantum-Computers-Technology/dp/3319964232

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