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Quantum Machine Learning

Quantum Request-Answer Game with Buffer Model for Online Algorithms

arXiv
Authors: Kamil Khadiev

Year

2020

Paper ID

18167

Status

Preprint

Abstract Read

~2 min

Abstract Words

123

Citations

N/A

Abstract

We consider online algorithms as a request-answer game. An adversary that generates input requests, and an online algorithm answers. We consider a generalized version of the game that has a buffer of limited size. The adversary loads data to the buffer, and the algorithm has random access to elements of the buffer. We consider quantum and classical (deterministic or randomized) algorithms for the model. In the paper, we provide a specific problem (The Most Frequent Keyword Problem) and a quantum algorithm that works better than any classical (deterministic or randomized) algorithm in terms of competitive ratio. At the same time, for the problem, classical online algorithms in the standard model are equivalent to the classical algorithms in the request-answer game with buffer model.

Why This Paper Matters

  • This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
  • It adds a 2020 reference point for readers tracking recent quantum research.
  • We consider online algorithms as a request-answer game.

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