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Full Description
This introduction to quantum computing from a classical programmer's perspective is meant for students and practitioners alike. More than 50 quantum techniques and algorithms are explained with mathematical derivations and code for simulation, using an open-source code base 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. Coverage includes algorithms exploiting entanglement, black-box algorithms, the quantum Fourier transform, phase estimation, quantum walks, and foundational QML algorithms. Readers will find detailed, easy-to-follow derivations and implementations of Shor's algorithm, Grover's algorithm, SAT3, graph coloring, the Solovay-Kitaev algorithm, Moettoenen's algorithm, quantum mean, median, and minimum finding, Deutsch's algorithm, Bernstein-Vazirani, quantum teleportation and superdense coding, the CHSH game, and, from QML, the HHL algorithm, Euclidean distance, and PCA. The book also discusses productivity issues like quantum noise, error correction, quantum programming languages, compilers, and techniques for transpilation.
Contents
Acknowledgments; Introduction; 1. The mathematical minimum; 2. Quantum computing fundamentals; 3. Simulation infrastructure; 4. Quantum tools and techniques; 5. Beyond classical; 6. Algorithms exploiting entanglement; 7. State similarity tests; 8. Black-box algorithms; 9. State preparation; 10. Algorithms using amplitude amplification; 11. Algorithms using quantum Fourier transformation; 12. Quantum walk algorithms; 13. Optimization algorithms; 14. Quantum machine learning; 15. Quantum error correction; 16. Quantum languages, compilers, and tools; Appendix: Sparse implementation.
