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

Robert S. Wezeman; Niel M.P. Neumann; Frank Phillipson; Robert 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, Niel M.P. Neumann, Frank Phillipson, Robert E. Kooij^*

^*Corresponding author for this work

Quantum & Computer Engineering

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

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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 - Proceedings of the 2023 Computing Conference
Editors	Kohei Arai
Place of Publication	Cham
Publisher	Springer
Pages	517-531
Number of pages	15
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
Event	Proceedings of the Computing Conference 2023 - London, United Kingdom Duration: 22 Jun 2023 → 23 Jun 2023

Publication series

Name	Lecture Notes in Networks and Systems
Volume	711
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	Proceedings of the Computing Conference 2023
Country/Territory	United Kingdom
City	London
Period	22/06/23 → 23/06/23

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

Agent based Modeling
Population Density Estimation
Quantum Random Walk

Access to Document

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

978-3-031-37717-4_32Final published version, 3.28 MB

Cite this

Wezeman, R. S., Neumann, N. M. P., Phillipson, F., & Kooij, R. E. (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 (pp. 517-531). (Lecture Notes in Networks and Systems; Vol. 711 ). Springer. https://doi.org/10.1007/978-3-031-37717-4_32

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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.",

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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. Lecture Notes in Networks and Systems, vol. 711 , Springer, Cham, pp. 517-531, Proceedings of the Computing Conference 2023, London, United Kingdom, 22/06/23. 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, Niel M.P.; Phillipson, Frank et al.
Intelligent Computing - Proceedings of the 2023 Computing Conference. ed. / Kohei Arai. Cham: Springer, 2023. p. 517-531 (Lecture Notes in Networks and Systems; Vol. 711 ).

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

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