Quantum Algorithms for Optimizers

From Core Principles to AI Applications

Giacomo Nannicini

$183

Paperback

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English

Society for Industrial & Applied Mathematics,U.S.
31 December 2025

Optimisation; Applied mathematics; Quantum physics (quantum mechanics & quantum field theory); Algorithms & data structures; Mathematical theory of computation

Series: MOS-SIAM Series on Optimization

This book is a self-contained introduction to quantum algorithms with an emphasis on quantum optimization, that is, quantum algorithms to solve optimization problems. The book provides all the tools necessary to understand the benefits and drawbacks of quantum optimization algorithms, paying particular attention to provable guarantees and computational complexity.

The first comprehensive treatment of quantum optimization, Conditional Gradient Methods: From Core Principles to AI Applications

provides a rigorous introduction to the computational model of quantum computers, contains detailed discussion of some of the most important developments in quantum optimization algorithms, and

summarizes the most important developments in the open literature.

By: Giacomo Nannicini
Imprint: Society for Industrial & Applied Mathematics,U.S.
Country of Publication: United States
ISBN: 9781611978759
ISBN 10: 1611978750
Series: MOS-SIAM Series on Optimization
Pages: 273
Publication Date: 31 December 2025
Audience: Professional and scholarly , Undergraduate
Format: Paperback
Publisher's Status: Active

Giacomo Nannicini is an associate professor in the Daniel J. Epstein Department of Industrial and Systems Engineering, with a courtesy appointment in the Ming Hsieh Department of Electrical and Computer Engineering in the USC School of Advanced Computing. He was a postdoctoral fellow at the CMU Tepper School of Business, a visiting scholar at the MIT Sloan School of Management, an assistant professor at the Singapore University of Technology and Design, and a research staff member at the IBM’s T.J. Watson Research Center. He received the 2021 Beale–Orchard-Hays Prize, the 2016 COIN-OR Cup, the 2015 Robert Faure Prize, and the 2012 Glover-Klingman Prize. His main research and teaching interest is optimization and its applications.