Quantum algorithms for optimizers

Fuente: arXiv
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Auteur principal: Nannicini, Giacomo
Format: Preprint
Publié: 2024
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author Nannicini, Giacomo
author_facet Nannicini, Giacomo
contents This is a set of lecture notes for a graduate-level course on quantum algorithms, with an emphasis on quantum optimization algorithms. It is developed for applied mathematicians and engineers, and requires no previous background in quantum mechanics. The main topics of this course, in addition to a rigorous introduction to the computational model, are: input/output models, quantum search, the quantum gradient algorithm, matrix manipulation algorithms, the mirror descent framework for semidefinite optimization (including the matrix multiplicative weights update algorithm), adiabatic optimization. This is a preprint for personal use only. Please refer to the printed version of the material.
format Preprint
id arxiv_https___arxiv_org_abs_2408_07086
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Quantum algorithms for optimizers
Nannicini, Giacomo
Quantum Physics
Discrete Mathematics
Optimization and Control
This is a set of lecture notes for a graduate-level course on quantum algorithms, with an emphasis on quantum optimization algorithms. It is developed for applied mathematicians and engineers, and requires no previous background in quantum mechanics. The main topics of this course, in addition to a rigorous introduction to the computational model, are: input/output models, quantum search, the quantum gradient algorithm, matrix manipulation algorithms, the mirror descent framework for semidefinite optimization (including the matrix multiplicative weights update algorithm), adiabatic optimization. This is a preprint for personal use only. Please refer to the printed version of the material.
title Quantum algorithms for optimizers
topic Quantum Physics
Discrete Mathematics
Optimization and Control
url https://arxiv.org/abs/2408.07086