A Dough-Like Model for Understanding Double-Slit Phenomena
Ping-Rui Tsai, Tzay-Ming Hong

TL;DR
This paper introduces a deep learning-based dough-like model to interpret double-slit interference, offering a physically intuitive perspective on quantum superposition and measurement collapse that aligns with observed probability patterns.
Contribution
The study presents a novel surrogate model using deep learning that captures quantum interference phenomena with a dough analogy, providing an interpretable framework for quantum behavior.
Findings
Reproduces interference and diffraction patterns accurately
Provides a physical analogy linking superposition and measurement
Suggests a unified view of quantum phenomena through dough analogy
Abstract
The probabilistic interference fringes observed in the double slit experiment vividly demonstrate the quantum superposition principle, yet they also highlight a fundamental conceptual challenge: the relationship between a system before and after the measurement. According to Copenhagen interpretation, an unobserved quantum system evolves continuously based on the Schrodinger equation, whereas observation induces an instantaneous collapse of the wave function to an eigenstate. This contrast between continuous evolution and sudden collapse renders the single particle behavior particularly enigmatic, especially given that quantum mechanics itself is constructed upon the statistical behavior of ensembles rather than individual entities. In this study, we introduce a Double Slit Diffraction Surrogate Model DSM based on deep learning, designed to capture the mapping between wave functions and…
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Taxonomy
TopicsQuantum Mechanics and Applications · Quantum many-body systems · Quantum Information and Cryptography
