Curated Reading List
The definitive reading list, from popular science to graduate textbooks
Online courses build intuition and practical skills quickly, but books offer something different: depth, mathematical rigour, and a lasting reference you can return to at any point in your learning. A good textbook works through the proofs rather than skipping them, gives you a complete picture of a topic rather than a curated highlight reel, and holds up over years as your understanding deepens. This list covers the best books at every level, from popular science titles that require no prerequisites through to graduate references used in university research groups.
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No prerequisites
These books require no mathematics or physics background. They are the best starting point for curious readers, executives, and anyone who wants to understand what quantum computing is and why it matters before diving into the technical details.
The clearest non-technical introduction available. Bernhardt builds up qubits, superposition, entanglement, teleportation, and the major algorithms using only high-school maths, introducing just enough vector notation to make the ideas precise rather than mystical.
A short, self-published gem that teaches genuine quantum reasoning through a simple visual system of black and white balls. By the end you can follow how quantum computers actually compute, with no equations at all. Written by a leading quantum physicist.
An accessible popular science account of how quantum computing emerged from the strange physics of superposition and entanglement. Gribbin traces the history from Schrodinger's cat through to early quantum processors.
A witty and intellectually demanding tour through quantum computing, complexity theory, and the nature of knowledge itself. Based on a popular lecture series, it is rigorous without requiring formal physics prerequisites.
A broad survey of quantum technologies including computing, cryptography, sensing, and communication, written for general audiences. Good for building intuition about the quantum technology landscape as a whole.
A popular science overview of the quantum computing landscape and its potential societal impact, covering computing, cryptography, AI, and medicine. Kaku surveys the current state of the industry and speculates on near-future breakthroughs.
Framework: IBM Qiskit
Qiskit is the most widely used quantum SDK, and almost every framework-specific book on the market targets it. These titles teach quantum computing by writing and running real Qiskit circuits in Python, from your first gate through to algorithms on IBM Quantum hardware. They are the fastest route for developers who want to learn by building.
A genuinely complete bridge from intuition to working knowledge, taught with Qiskit. Sutor starts with everyday intuition and gradually builds the linear algebra, gates, and algorithms needed to understand quantum computing properly, then puts them to work in code. The second edition adds error correction, the latest hardware, and current algorithms.
A project-driven introduction to quantum computing using Python and Qiskit. Covers quantum gates, circuits, algorithms, and IBM Quantum hardware access through hands-on projects. Written by an IBM Quantum advocate.
A current, hands-on path through quantum computing with Qiskit, taking you from the basics of qubits and circuits to running real algorithms on simulators and IBM hardware. Strong on the practical workflow that modern Qiskit developers actually use.
A developer-oriented guide to programming cloud quantum machines using Python, quantum assembly language, and IBM Q Experience. It walks through running gates and small algorithms on real hardware, with a focus on the tooling rather than the theory.
Requires linear algebra
These textbooks assume familiarity with linear algebra and basic probability. Some assume a little physics; others are written specifically for computer scientists. They are the right level for upper-division undergraduates and self-taught engineers who have done the maths prerequisites.
The definitive reference textbook for the field. It covers quantum circuits, algorithms, complexity, error correction, and quantum information theory from first principles, with rigorous proofs throughout. Every researcher owns a copy.
A self-contained textbook that starts from classical logic gates and binary arithmetic before building up to quantum gates, interference, and major algorithms including Deutsch-Jozsa, Grover, and Shor. Every step is shown in full - no hand-waving. Available free on the author's website.
Written for programmers and theorists rather than physicists. It develops the linear algebra you need from scratch, then works through architecture, algorithms, and the theory of computation in language that computer scientists find natural. A favourite first textbook in CS departments.
A widely used MIT Press textbook that balances mathematical rigour with readable explanation. It covers quantum states, circuits, the major algorithms, and an unusually thorough treatment of error correction, all paced gently enough for self-study.
A practical introduction combining conceptual explanation with real code in Qiskit and Cirq. Covers linear algebra foundations, major quantum algorithms, and hands-on circuit implementation. One of the few textbooks that keeps the code central rather than an afterthought.
A concise, high-school-and-up introduction developed at Fermilab. It introduces qubits, gates, entanglement, and key protocols with worked examples and exercises, while keeping the mathematics light. Freely available as an open-access PDF.
A clear and compact undergraduate textbook covering quantum circuits, key algorithms (Deutsch, Simon, Shor, Grover), and an introduction to error correction. The physics prerequisites are kept to a minimum.
A CS-focused introduction written by a physicist who wanted to spare computer scientists unnecessary physics baggage. The notation and framing are tailored for programmers and theorists rather than physicists.
Covers the complete mathematical foundation: linear algebra, tensor products, probability, quantum mechanics formalism, quantum circuits, and major algorithms. Very explicit about the maths, making it ideal for bridging the gap between theory and implementation.
Graduate level
These are research-level references for those working at the frontier of quantum computing. They assume strong backgrounds in linear algebra, probability theory, and in some cases quantum mechanics. Recommended after you have worked through an undergraduate textbook.
A rigorous graduate treatment of quantum Shannon theory, channel capacities, entropy measures, and quantum information protocols. Covers classical and quantum data compression, channel coding, and entanglement theory with full proofs.
A comprehensive graduate reference on quantum error correction, covering stabilizer codes, fault-tolerant computation, decoherence-free subspaces, and topological codes. Each chapter is written by leading researchers in that sub-area.
A compact graduate primer that reaches the major quantum algorithms using linear algebra alone, deliberately skipping the physics. It builds Deutsch, Simon, Shor, and Grover up cleanly from matrices and vectors, making the algorithmic core unusually transparent.
A theoretical computer science and mathematical physics approach to quantum computation, with a strong focus on complexity theory and the algebraic structures underlying quantum algorithms. Dense and rigorous.
Bridges the gap between abstract quantum information theory and physical hardware realizations. Covers NMR, ion trap, and solid-state qubits alongside the algorithms. More physics-heavy than most texts but uniquely hardware-grounded.
Specialised topic
Quantum machine learning sits at the intersection of quantum computing and classical ML. These books cover quantum linear algebra subroutines, variational quantum circuits as ML models, and hybrid classical-quantum approaches.
The modern treatment of quantum ML with PennyLane examples throughout. Covers quantum kernels, variational circuits, quantum neural networks, and the debate around genuine quantum advantage in ML tasks.
A hands-on tour of modern quantum algorithms for machine learning and optimisation, with runnable code in Qiskit, PennyLane, and other frameworks. Covers QAOA, VQE, quantum kernels, and variational classifiers, run on both simulators and real hardware.
The foundational text on quantum ML: covers quantum linear algebra, quantum PCA, quantum support vector machines, and early hybrid approaches. Written before variational circuits dominated, so it focuses on near-ideal quantum speedups.
Specialised topic
Post-quantum cryptography designs classical cryptographic schemes that remain secure against quantum attacks. With NIST having published its first finalised post-quantum standards (FIPS 203, 204, and 205) in 2024, this area is increasingly important for security engineers and cryptographers.
The academic reference for post-quantum cryptographic schemes, covering lattice-based, code-based, hash-based, and multivariate polynomial systems. Each chapter is written by the researchers who developed the relevant scheme.