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Quantum Optimization Quantum Machine Learning

Quantum Algorithm for Online Exp-concave Optimization

arXiv
Authors: Jianhao He, Chengchang Liu, Xutong Liu, Lvzhou Li, John C. S. Lui

Year

2024

Paper ID

37633

Status

Preprint

Abstract Read

~2 min

Abstract Words

99

Citations

N/A

Abstract

We explore whether quantum advantages can be found for the zeroth-order feedback online exp-concave optimization problem, which is also known as bandit exp-concave optimization with multi-point feedback. We present quantum online quasi-Newton methods to tackle the problem and show that there exists quantum advantages for such problems. Our method approximates the Hessian by quantum estimated inexact gradient and can achieve O\(nlog T\) regret with O(1) queries at each round, where n is the dimension of the decision set and T is the total decision rounds. Such regret improves the optimal classical algorithm by a factor of T2/3.

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  • This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
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  • We explore whether quantum advantages can be found for the zeroth-order feedback online exp-concave optimization problem, which is also known as bandit exp-concave optimization...

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References & Citation Signals

[1] DOI https://doi.org/arXiv:2410.19688 [2] arXiv https://arxiv.org/abs/2410.19688 [3] Publisher https://arxiv.org/abs/2410.19688

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