This book provides a comprehensive, up-to-date survey of quantum algorithms and how they connect to practical, concrete applications of quantum computing. Its modular, encyclopedic structure facilitates usage as a reference for researchers. This title is also available as Open Access on Cambridge Core.
This book provides a comprehensive, up-to-date survey of quantum algorithms and how they connect to practical, concrete applications of quantum computing. Its modular, encyclopedic structure facilitates usage as a reference for researchers. This title is also available as Open Access on Cambridge Core.
Alexander M. Dalzell is a Research Scientist at the Amazon Web Services Center for Quantum Computing where he works on quantum algorithms. Following undergraduate studies at MIT, he received a Ph.D. in physics from Caltech, where he was awarded an NSF Graduate Research Fellowship. He currently serves as an editor for the journal 'Quantum.'
Inhaltsangabe
Part I. Areas of Application: 1. Condensed matter physics 2. Quantum chemistry 3. Nuclear and particle physics 4. Combinatorial optimization 5. Continuous optimization 6. Cryptanalysis 7. Solving differential equations 8. Finance 9. Machine learning with classical data Part II. Quantum Algorithmic Primitives: 10. Quantum linear algebra 11. Hamiltonian simulation 12. Quantum Fourier transform 13. Quantum phase estimation 14. Amplitude amplification and estimation 15. Gibbs sampling 16. Quantum adiabatic algorithm 17. Loading classical data 18. Quantum linear system solvers 19. Quantum gradient estimation 20. Variational quantum algorithms 21. Quantum tomography 22. Quantum interior point methods 23. Multiplicative weights update method 24. Approximate tensor network contraction Part III. Fault-Tolerant Quantum Computing: 25. Basics of fault tolerance 26. Quantum error correction with the surface code 27. Logical gates with the surface code Appendix References Index.
