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Bandit Algorithm Driven by a Classical Random Walk and a Quantum Walk.

Tomoki Yamagami1, Etsuo Segawa2, Takatomo Mihana1

  • 1Department of Information Physics and Computing, Graduate School of Information Science and Technology, The University of Tokyo, 7-3-1 Hongo, Bunkyo, Tokyo 113-8656, Japan.

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Summary
This summary is machine-generated.

Quantum walks offer unique spreading and localization properties, outperforming classical random walks in multi-armed bandit problems by better balancing exploration and exploitation.

Keywords:
bandit algorithmdecision-makingexploration–exploitation trade-offquantum walkrandom walk

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Area of Science:

  • Quantum computing and algorithms
  • Theoretical computer science
  • Machine learning

Background:

  • Classical random walks (RWs) exhibit linear spreading but lack localization.
  • Quantum walks (QWs) uniquely combine linear spreading and localization.
  • Multi-armed bandit (MAB) problems involve balancing exploration and exploitation.

Purpose of the Study:

  • To develop novel algorithms for MAB problems using both RW and QW frameworks.
  • To investigate the performance advantages of QW-based approaches over RW-based ones for MAB.
  • To leverage the distinct properties of QWs for improved MAB strategy.

Main Methods:

  • Development of RW-based algorithms for MAB.
  • Development of QW-based algorithms for MAB.
  • Comparative analysis of algorithm performance under various settings.

Main Results:

  • QW-based algorithms demonstrate superior performance compared to RW-based algorithms in specific MAB scenarios.
  • The enhanced performance is attributed to the QW's ability to effectively manage exploration and exploitation.
  • Localization and spreading properties of QWs are key to their MAB advantage.

Conclusions:

  • Quantum walks provide a powerful framework for addressing complex MAB problems.
  • QW-based strategies offer a promising alternative for optimizing decision-making under uncertainty.
  • This research highlights the practical applicability of quantum walk properties in machine learning.