Encounter-Based Density Approximation Using Multi-step and Quantum-Inspired Random Walks

Robert S. Wezeman; N.M.P. Neumann; Frank Phillipson; R.E. Kooij

doi:10.1007/978-3-031-37717-4_32

Encounter-Based Density Approximation Using Multi-step and Quantum-Inspired Random Walks

Robert S. Wezeman^*, N.M.P. Neumann, Frank Phillipson, R.E. Kooij

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceeding › Academic › peer-review

Abstract

In this paper we study encounter-based density estimation using different random walks and analyse the effects of the step-size on the convergence of the density approximation. Furthermore, we analyse different types of random walks, namely, a uniform random walk, with every position equally likely to be visited next, a classical random walk and a quantum-inspired random walk, where the probability distribution for the next state is sampled from a quantum random walk. We find that walks with additional steps lead to faster convergence, but that the type of step, quantum-inspired or classical, has only a marginal effect.

Original language	English
Title of host publication	Intelligent Computing
Subtitle of host publication	Proceedings of the 2023 Computing Conference, Volume 1
Editors	Kohei Arai
Publisher	Springer, Cham
Pages	517-531
ISBN (Electronic)	978-3-031-37717-4
ISBN (Print)	978-3-031-37716-7
DOIs	https://doi.org/10.1007/978-3-031-37717-4_32
Publication status	Published - 2023

Publication series

Series	Lecture Notes in Networks and Systems (LNNS)
Volume	711
ISSN	2367-3370

Keywords

agent based modeling
population density estimation
quantum random walk

Access to Document

10.1007/978-3-031-37717-4_32

Cite this

@inproceedings{1934148462ee4e0dae949f7300b6bf0a,

title = "Encounter-Based Density Approximation Using Multi-step and Quantum-Inspired Random Walks",

abstract = "In this paper we study encounter-based density estimation using different random walks and analyse the effects of the step-size on the convergence of the density approximation. Furthermore, we analyse different types of random walks, namely, a uniform random walk, with every position equally likely to be visited next, a classical random walk and a quantum-inspired random walk, where the probability distribution for the next state is sampled from a quantum random walk. We find that walks with additional steps lead to faster convergence, but that the type of step, quantum-inspired or classical, has only a marginal effect.",

keywords = "agent based modeling, population density estimation, quantum random walk",

author = "Wezeman, {Robert S.} and N.M.P. Neumann and Frank Phillipson and R.E. Kooij",

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year = "2023",

doi = "10.1007/978-3-031-37717-4_32",

language = "English",

isbn = "978-3-031-37716-7",

series = "Lecture Notes in Networks and Systems (LNNS)",

publisher = "Springer, Cham",

pages = "517--531",

editor = "Kohei Arai",

booktitle = "Intelligent Computing",

address = "Switzerland",

}

Wezeman, RS, Neumann, NMP, Phillipson, F & Kooij, RE 2023, Encounter-Based Density Approximation Using Multi-step and Quantum-Inspired Random Walks. in K Arai (ed.), Intelligent Computing: Proceedings of the 2023 Computing Conference, Volume 1. Springer, Cham, Lecture Notes in Networks and Systems (LNNS), vol. 711, pp. 517-531. https://doi.org/10.1007/978-3-031-37717-4_32

Encounter-Based Density Approximation Using Multi-step and Quantum-Inspired Random Walks. / Wezeman, Robert S.; Neumann, N.M.P.; Phillipson, Frank et al.
Intelligent Computing: Proceedings of the 2023 Computing Conference, Volume 1. ed. / Kohei Arai. Springer, Cham, 2023. p. 517-531 (Lecture Notes in Networks and Systems (LNNS), Vol. 711).

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceeding › Academic › peer-review

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AB - In this paper we study encounter-based density estimation using different random walks and analyse the effects of the step-size on the convergence of the density approximation. Furthermore, we analyse different types of random walks, namely, a uniform random walk, with every position equally likely to be visited next, a classical random walk and a quantum-inspired random walk, where the probability distribution for the next state is sampled from a quantum random walk. We find that walks with additional steps lead to faster convergence, but that the type of step, quantum-inspired or classical, has only a marginal effect.

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KW - population density estimation

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