Quantum Computing for Developers

This practical, developer-focused guide takes you from first principles to writing quantum programs with Python and Qiskit. Designed for engineers, programmers, and data professionals, it combines clear explanations with hands-on examples to help you build working applications.

You’ll begin with qubits, superposition, and entanglement, then set up your environment. Building on these foundations, you’ll explore gates, circuits, and measurement, run your first circuits on simulators, and implement algorithms such as Deutsch–Jozsa, Grover’s search, and the Quantum Fourier Transform.

You’ll then move into quantum machine learning and optimization by encoding classical data, building hybrid quantum-classical models, and using variational algorithms. You’ll apply the Quantum Approximate Optimization Algorithm (QAOA) to QUBO problems before running experiments on IBM Quantum hardware, comparing noisy results with ideal simulations, and applying error-mitigation techniques.

By the end, you’ll have the skills to build and test quantum applications, along with an understanding of the challenges ahead for fault-tolerant quantum computing.

What you will learn:

• Build, simulate, and visualize quantum gates and circuits
• Implement algorithms such as Deutsch–Jozsa, Grover’s search, and the Quantum Fourier Transform
• Create hybrid quantum-classical models to solve real classification problems
• Solve combinatorial optimization problems such as Max-Cut using QAOA
• Run programs on IBM Quantum hardware and compare noisy results with ideal simulations

Who this book is for:

This book is aimed at data scientists, ML engineers, and software developers seeking to become quantum-aware professionals. It also appeals to ambitious university students, including students and PhD candidates in computer science, engineering, and physics.