## 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 |
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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 | |

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 |
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Volume | 711 |

ISSN (Print) | 2367-3370 |

ISSN (Electronic) | 2367-3389 |

### Conference

Conference | Proceedings of the Computing Conference 2023 |
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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-careOtherwise 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