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arXiv:0908.1034 · Physica E Low-dimensional Systems and Nanostructures

Event-by-event simulation of quantum phenomena

Hans A. De Raedt, Shuang Zhao, Shengjun Yuan, Fengping Jin, Kristel F. L. Michielsen, Seiji Miyashita

Published Jun 25, 2009• 4 citations
arXivHTMLPDF
DOI
10.1016/j.physe.2009.06.077

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We propose and analyse simple deterministic algorithms that can be used to construct machines that have primitive learning capabilities. We demonstrate that locally connected networks of these machines can be used to perform blind classification on an event-by-event basis, without storing the information of the individual events. We also demonstrate that properly designed networks of these machines exhibit behavior that is usually only attributed to quantum systems. We present networks that simulate quantum interference on an event-by-event basis. In particular we show that by using simple geometry and the learning capabilities of the machines it is possible to simulate single-photon interference in a Mach–Zehnder interferometer. The interference pattern generated by the network of deterministic learning machines is in perfect agreement with the quantum theoretical result for the single-photon Mach–Zehnder interferometer. To illustrate that networks of these machines are indeed capable of simulating quantum interference we simulate, event-by-event, a setup involving two chained Mach–Zehnder interferometers, and demonstrate that also in this case the simulation results agree with quantum theory.
This text explores the fundamental divide between empirical laboratory data and continuous theoretical physics models, framing the "quantum measurement paradox" as an ontological error, specifically E.T. Jaynes' ``mind projection fallacy'' where abstract mathematical tools like wavefunctions are mistaken for physical reality. While quantum theory successfully predicts ensemble averages, it remains inherently silent on individual, event-by-event detection statistics. To resolve these conceptual mysteries without resorting to ad-hoc explanations like wavefunction collapse, the text introduces Event-By-Event Simulation (EBES). As a modular, discrete-event simulation framework utilizing causally-driven Deterministic Learning Machines (DLMs), EBES bypasses continuous time and pre-specified probability distributions. Instead, it generates discrete data from the bottom up in a strictly localized, cause-and-effect manner. Ultimately, EBES provides a computationally efficient methodology that mirrors actual laboratory observations, modeling phenomena that lie entirely beyond the reach of standard quantum theory.