Learn Quantum Computing with Qiskit Textbook

The Qiskit Textbook began as IBM’s primary written resource for learning quantum computing through code, and it remains one of the most comprehensive free references in the field. The material runs from first principles (superposition, measurement, and single-qubit gates) all the way through advanced topics like quantum phase estimation, variational algorithms, and the theory of quantum error-correcting codes. Every chapter is backed by runnable Jupyter notebooks so you can execute the circuits yourself and inspect the outputs rather than taking results on faith.

The curriculum is structured for readers who arrive with some linear algebra background but no prior quantum mechanics. Early chapters introduce the circuit model and the Qiskit API side by side, so you learn the physics and the implementation simultaneously. Mid-tier chapters cover the major quantum algorithms in depth: Grover’s search algorithm is derived from scratch, Shor’s factoring algorithm is explained with full mathematical detail, and the quantum Fourier transform is developed as a building block for both. The error correction section covers stabilizer codes, the surface code, and fault-tolerant gate sets, which is more depth than most introductory resources provide.

The textbook also includes chapters on quantum machine learning topics (variational quantum eigensolvers, quantum approximate optimization, and kernel-based methods), making it useful even after you have cleared the foundational material. One important caveat: the GitHub repository was archived in January 2024 and is no longer maintained. IBM’s actively updated successor material lives on IBM Quantum Learning, and the notebooks here were written for older Qiskit versions, so some code requires minor updates to run on a current Qiskit installation.

What you’ll learn

• Quantum circuit fundamentals: single-qubit and multi-qubit gates, measurement, and the Qiskit circuit API
• Core quantum algorithms: Grover’s search, Shor’s factoring, quantum phase estimation, and the quantum Fourier transform
• Variational algorithms: VQE and QAOA, including how to construct ansatz circuits and run optimization loops
• Quantum error correction: stabilizer formalism, CSS codes, and an introduction to the surface code
• Quantum machine learning: quantum kernels, data encoding strategies, and hybrid classical-quantum models

Who is this for?

• Beginners with basic linear algebra who want a thorough, code-first introduction to quantum computing
• Students who prefer self-paced reading over video lectures and want runnable examples alongside the theory
• Practitioners who want to learn Qiskit deeply rather than through isolated tutorials
• Anyone who has taken an introductory course and wants a single reference covering algorithms, hardware, and error correction together