Quantum Algorithms
Quantum Algorithms
Sam McArdle, AWS Center for Quantum Computing, Pasadena
Mario Berta, RWTH Aachen University
Przemyslaw Bienias, AWS Center for Quantum Computing, Pasadena
Chi-Fang Chen, University of California, Berkeley
András Gilyén, Alfréd Rényi Institute of Mathematics, Budapest
Connor T. Hann, AWS Center for Quantum Computing, Pasadena
Michael J. Kastoryano, University of Copenhagen
Emil T. Khabiboulline, University of Maryland, College Park
Aleksander Kubica, Yale University, Connecticut
Grant Salton, AWS Center for Quantum Computing, Pasadena
Samson Wang, Caltech
Fernando G. S. L. Brandão, AWS Center for Quantum Computing, Pasadena
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Ever since Shor's quantum algorithm for factoring integers was discovered three decades ago, showing that quantum algorithms could solve a problem relevant to everyday cryptography, researchers have been working to expand the list of real-world problems to which quantum computing can be applied. This book surveys the fruits of this effort, covering proposed quantum algorithms for concrete problems in many application areas, including quantum chemistry, optimization, finance, and machine learning. The book clearly states the problem being solved and the full computational complexity of the quantum algorithm, making sure to account for the contribution from all the underlying primitive ingredients. Separately, the book also provides a detailed, independent summary of the most common algorithmic primitives. The book has a modular, encyclopedic format to facilitate navigation of the material, and to provide a quick reference for designers of quantum algorithms and quantum computing researchers. This title is also available as open access on Cambridge Core.
- Bridges the disconnect between the theoretical analysis of quantum algorithmic primitives and their real-world application domains
- Distills three decades of research into an encyclopedic reference
- This book is also available as open access
Reviews & endorsements
'This timely and forward-looking survey captures the state-of-the-art in quantum computing. Focusing on cutting-edge applications and recent advances in quantum primitives, it serves as an essential resource for understanding the rapidly evolving role of quantum algorithms in scientific discovery.' Lin Lin, University of California, Berkeley
Product details
May 2025Hardback
9781009639644
0 pages
Not yet published - available from May 2025
Table of Contents
- 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.
