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Publisher:
Cambridge University Press
Online publication date:
May 2025
Print publication year:
2025
Online ISBN:
9781009639651
Creative Commons:
Creative Common License - CC Creative Common License - BY Creative Common License - NC Creative Common License - ND
This content is Open Access and distributed under the terms of the Creative Commons Attribution licence CC-BY-NC-ND 4.0 https://creativecommons.org/creativelicenses

Book description

The 1994 discovery of Shor's quantum algorithm for integer factorization—an important practical problem in the area of cryptography—demonstrated quantum computing's potential for real-world impact. Since then, researchers have worked intensively to expand the list of practical problems that quantum algorithms can solve effectively. 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. For each quantum algorithm considered, the book clearly states the problem being solved and the full computational complexity of the procedure, making sure to account for the contribution from all the underlying primitive ingredients. Separately, the book provides a detailed, independent summary of the most common algorithmic primitives. It 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.

Reviews

‘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

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Contents

Full book PDF

Page 1 of 2


  • Frontmatter
    pp i-iv
  • Contents
    pp v-viii
  • Preface
    pp ix-xiv
  • Part I - Areas of application
    pp 1-3
  • 1 - Condensed matter physics
    pp 4-28
  • 2 - Quantum chemistry
    pp 29-50
  • 3 - Nuclear and particle physics
    pp 51-61
  • 4 - Combinatorial optimization
    pp 62-77
  • 5 - Continuous optimization
    pp 78-97
  • 6 - Breaking cryptosystems
    pp 98-110
  • 7 - Solving differential equations
    pp 111-129
  • 8 - Finance
    pp 130-147
  • 9 - Machine learning with classical data
    pp 148-184
  • Part II - Quantum algorithmic primitives
    pp 185-187
  • 10 - Quantum linear algebra
    pp 188-209
  • 11 - Hamiltonian simulation
    pp 210-224
  • 12 - Quantum Fourier transform
    pp 225-227
  • 13 - Quantum phase estimation
    pp 228-234
  • 14 - Amplitude amplification and estimation
    pp 235-242
  • 15 - Gibbs sampling
    pp 243-249
  • 16 - Quantum adiabatic algorithm
    pp 250-254
  • 17 - Loading classical data
    pp 255-269
  • 18 - Quantum linear system solvers
    pp 270-275
  • 19 - Quantum gradient estimation
    pp 276-280
  • 20 - Variational quantum algorithms
    pp 281-285
  • 21 - Quantum tomography
    pp 286-290
  • 22 - Quantum interior point methods
    pp 291-298
  • 23 - Multiplicative weights update method
    pp 299-304
  • 24 - Approximate tensor network contraction
    pp 305-310
  • Part III - Fault-tolerant quantum computing
    pp 311-313

Page 1 of 2


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