Build Quantum AI skills with Python and Qiskit. Create quantum circuits, implement Grover's search, explore quantum kernels, VQCs, and QNNs, and connect classical ML with hybrid quantum-classical optimization workflows.
Key Features:
- Build AI and machine learning foundations with Python, scikit-learn, TensorFlow, and PyTorch
- Create quantum circuits and explore core algorithms, gates, and measurements using Qiskit
- Prototype quantum kernels, variational classifiers, QNNs, and hybrid optimization workflows
Book Description:
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.
What You Will Learn:
- Build, train, and evaluate ML models with Python and Scikit-Learn
- Train neural networks and image classifiers with TensorFlow and PyTorch
- Explain qubits, superposition, entanglement, measurement, and quantum gates
- Create and simulate quantum circuits and core algorithms using Qiskit
- Implement Grover's search and explore QFT and quantum phase estimation
- Encode classical data with quantum feature maps and kernel methods
- Build variational quantum classifiers and introductory QNNs
- Explore hybrid quantum-classical optimization through applied use cases
Who this book is for:
This book is for software developers, data professionals, ML practitioners, students, and technical learners who want a practical introduction to AI and quantum computing. It also suits readers with basic programming knowledge who are new to quantum computing or quantum machine learning. No prior AI or quantum experience is required. Familiarity with high school algebra, probability, and basic linear algebra will help. Python knowledge is useful, but the book provides enough foundation to follow the examples and progress into Qiskit-based quantum circuits and hybrid QML workflows.
Table of Contents
- Introducing Artificial Intelligence
- Building models with machine learning and AI frameworks
- Training deep learning and reinforcement learning models
- Understanding the foundations of quantum computing
- Building quantum circuits and algorithms
- Applying quantum computing to AI
- Exploring AI-quantum use cases and optimization
- Learning Python from scratch
