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.
|Title of host publication
|Intelligent Computing - Proceedings of the 2023 Computing Conference
|Place of Publication
|Number of pages
|Published - 2023
|Proceedings of the Computing Conference 2023 - London, United Kingdom
Duration: 22 Jun 2023 → 23 Jun 2023
|Lecture Notes in Networks and Systems
|Proceedings of the Computing Conference 2023
|22/06/23 → 23/06/23
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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.
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