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Quantum Information Processing

Quantum greedy algorithms for multi-armed bandits

  • 01-02-2023
Published in:
Author
Hiroshi Ohno
Published in
Quantum Information Processing | Issue 2/2023

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Abstract

The article presents quantum greedy algorithms for multi-armed bandits, a crucial framework in machine learning. It introduces two quantum subroutines to replace the classical argmax operation, achieving theoretically quadratic speedups. The proposed algorithms are evaluated through simulations using Bernoulli and empirical reward distributions, including the MovieLens dataset. The results show that these quantum algorithms can match or even exceed the performance of classical algorithms in certain scenarios, highlighting the potential of quantum computing in enhancing classical machine learning techniques.
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previous article EAQEC codes from two distinct constacyclic codes
next article Extension of the unextendible product bases of 5-qubit under coarsening the system
Title
Quantum greedy algorithms for multi-armed bandits
Author
Hiroshi Ohno
Publication date
01-02-2023
Publisher
Springer US
Published in
Quantum Information Processing / Issue 2/2023
Print ISSN: 1570-0755
Electronic ISSN: 1573-1332
DOI
https://doi.org/10.1007/s11128-023-03844-2
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Other articles of this Issue 2/2023

A note on uncertainty relations of metric-adjusted skew information

Two symmetric measurements may cause an unforeseen effect

Extension of the unextendible product bases of 5-qubit under coarsening the system

On the distillability problem of two-copy Werner states: matrices A, B of at most four nonzero entries

Practical efficient 1-out-of-n quantum oblivious transfer protocol

A GNN-based predictor for quantum architecture search