Ebook details

Quantum machine learning sits at the intersection of two fields, yet most learners encounter artificial intelligence and quantum computing as separate subjects. This book bridges that gap with a beginner-friendly, code-led path that explains the classical AI foundations first, then shows how quantum methods are introduced through Python and Qiskit.

You will start with machine learning, model evaluation, neural networks, deep learning, and reinforcement learning using scikit-learn, TensorFlow, and PyTorch. The journey then moves into qubits, superposition, entanglement, measurement, quantum gates, and circuit construction before exploring Grover's search, the Quantum Fourier Transform, phase estimation, and the ideas behind Shor's algorithm.

With these foundations in place, you will explore quantum feature maps, quantum kernels, variational quantum classifiers, quantum neural networks, and hybrid quantum-classical models. The book also introduces QAOA, VQE, quantum annealing, and applied examples in portfolio optimization, quantum chemistry, and cybersecurity. Rather than promising automatic quantum advantage, it helps you understand what these techniques do, how to prototype them, and where they fit alongside classical AI. By the end, you will be able to build and explain practical QML workflows with Python and Qiskit.