Quantum-Tunnelling Oscillators for Cognitive Modelling and Neural Computation: Foundations, Machine-Vision Realisation and Applications

Overview

1. Seymour Research Laboratories, Seymour, VIC 3660, Australia

Abstract

I present a quantum-tunnelling oscillator model as a universal dynamical engine for two paradigmatic problems in quantum cognition theory—optical illusion perception and group decision making—where individuals are treated as quantum-mechanical agents whose choices shift through context-dependent transitions rather than simple probabilities. I show that, when networked together, these units form a quantum-cognitive neural system that reproduces familiar collective and perceptual phenomena while naturally accommodating counterintuitive processes that challenge classical models. Bridging ideas from quantum cognition theory and neural networks, this approach offers a compact, physically grounded way to describe how real individuals and groups think, perceive and decide.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Data

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Data Availability Statement

The source codes that implement the neural network models discussed in this paper are available in the GitHub repository, https://github.com/IvanMaksymov/Quantum-Tunnelling-Neural-Networks-Tutorial and https://github.com/IvanMaksymov/Cognition-in-Superposition/tree/main/Military-Trucks. The FDTD and finite-difference solvers can be found at https://github.com/IvanMaksymov/Cognition-in-Superposition and https://github.com/IvanMaksymov/OpinionPolarisation, respectively.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 1 author, 9 keywords, 127 references.

Cite

This paper

Maksymov, I. S. (2026). Quantum-Tunnelling Oscillators for Cognitive Modelling and Neural Computation: Foundations, Machine-Vision Realisation and Applications. Entropy (Basel, Switzerland), 28(6), 697.

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