research-article

Quantum Best Arm Identification

Authors:

Xuchuang Wang,

Yu-Zhen Janice Chen,

Matheus Guedes de Andrade,

Mohammad Hajiesmaili,

John C.S. Lui,

Don TowsleyAuthors Info & Claims

ACM SIGMETRICS Performance Evaluation Review, Volume 51, Issue 2

Pages 72 - 74

https://doi.org/10.1145/3626570.3626596

Published: 02 October 2023 Publication History

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Abstract

Recent progress on building quantum computers [1] envisages wide applications of quantum algorithms in the near future. With the advantage of quantum computer, one can speed up not only fundamental algorithms, e.g., unstructured search [6] and factoring [11], but recent machine learning algorithms [3] as well. In this paper, we study the quantum speedup on a canonical task of reinforcement learning-best arm identification in multi-armed bandits.

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Pokhrel SYash NKua JLi GPan L(2024)A Data-Encoding Approach to Quantum Federated Learning: Experimenting with Cloud ChallengesProceedings of the 8th Asia-Pacific Workshop on Networking10.1145/3663408.3665808(179-180)Online publication date: 3-Aug-2024
https://dl.acm.org/doi/10.1145/3663408.3665808

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cover image ACM SIGMETRICS Performance Evaluation Review

ACM SIGMETRICS Performance Evaluation Review Volume 51, Issue 2

September 2023

110 pages

ISSN:0163-5999

DOI:10.1145/3626570

Editor:
Bo Ji
Virginia Tech

Issue’s Table of Contents

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Published: 02 October 2023

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Pokhrel SYash NKua JLi GPan L(2024)A Data-Encoding Approach to Quantum Federated Learning: Experimenting with Cloud ChallengesProceedings of the 8th Asia-Pacific Workshop on Networking10.1145/3663408.3665808(179-180)Online publication date: 3-Aug-2024
https://dl.acm.org/doi/10.1145/3663408.3665808

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