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Quantum Optimization Quantum Machine Learning

Quantum algorithms for optimizers

arXiv

Authors: Giacomo Nannicini

Year

2024

Paper ID

64453

Status

Preprint

Abstract Read

~2 min

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Abstract

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.

Why This Paper Matters

This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
It adds a 2024 reference point for readers tracking recent quantum research.
This is a set of lecture notes for a graduate-level course on quantum algorithms, with an emphasis on quantum optimization algorithms.

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References & Citation Signals

[1] DOI https://doi.org/arXiv:2408.07086 [2] arXiv https://arxiv.org/abs/2408.07086 [3] Publisher https://arxiv.org/abs/2408.07086

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