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This introduction to quantum computing from a classical programmer’s perspective is meant for students and practitioners alike. About 50 fundamental algorithms are explained with full mathematical derivations and classical code for simulation, using an open-source code base developed from the ground up in Python and C++. New material throughout this fully revised and expanded second edition includes new chapters on Quantum Machine Learning, State Preparation, and Similarity Tests. After presenting the basics of quantum computing and the software infrastructure used for simulation, a section on modeling classical logic with quantum gates prepares for the quantum supremacy experiment. With this background, the following sections discuss, derive, and implement with working code algorithms exploiting entanglement, blackbox algorithms, algorithms for state preparation and state similarity tests, algorithms based on amplitude amplification, the quantum Fourier transform and phase estimation, several quantum optimization algorithms, quantum walks, and a short section on foundational quantum machine learning algorithms. The list of algorithms includes Shor’s algorithm, Grover’s algorithm, SAT3, graph coloring, the Solovay–Kitaev algorithm, Möttönen’s algorithm, quantum mean, median, and minimum finding, Deutsch’s algorithm, Bernstein–Vazirani, quantum teleportation and superdense coding, and the CHSH game. From the field of quantum machine learning, the book discusses Euclidean distance, principal component analysis, and the HHL algorithm. The book also addresses issues around programmer productivity, including quantum noise, error correction, quantum programming languages, compilers, and techniques for transpilation.