A hidden variables model for interference phenomena based on $p$-adic random dynamical systems
Daniel Dubischar, Volker Matthias Gundlach, Oliver Steinkamp, Andrei, Khrennikov

TL;DR
This paper introduces a $p$-adic random dynamical systems model for interference phenomena, supporting non-ergodic quantum interpretations and showing that certain interference patterns are noise-resistant, blending corpuscular and wave perspectives.
Contribution
It presents a novel $p$-adic RDS framework for quantum interference, demonstrating noise-resistant patterns and bridging corpuscular and wave models of quantum particles.
Findings
Interference patterns correspond to attractors in $p$-adic RDS.
Certain $p$-adic RDS produce noise-insensitive interference patterns.
The model supports nonlocal interactions in quantum phenomena.
Abstract
We propose a model based on random dynamical systems (RDS) in information spaces (realized as rings of -adic integers) which supports Buonomano's non-ergodic interpretation of quantum mechanics. In this model the memory system of an equipment works as a dynamical system perturbed by noise. Interference patterns correspond to attractors of RDS. There exists a large class of -adic RDS for which interference patterns cannot be disturbed by noise. Therefore, if the equipment is described by such a RDS then the result of statistical experiment does not depend on noise in the equipment. On the one hand, we support the corpuscular model, because a quantum particle can be described as a corpuscular object. On the other hand, our model does not differ strongly from the wave model, because a quantum particle interacts with the whole equipment. Hence the interaction has nonlocal character.…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
Topicsadvanced mathematical theories · Chaos-based Image/Signal Encryption · Topological and Geometric Data Analysis
