Version 1
: Received: 27 June 2024 / Approved: 27 June 2024 / Online: 27 June 2024 (04:27:12 CEST)
How to cite:
Maksymov, I. Quantum-Tunnelling Deep Neural Networks for Sociophysical Neuromorphic AI. Preprints2024, 2024061912. https://doi.org/10.20944/preprints202406.1912.v1
Maksymov, I. Quantum-Tunnelling Deep Neural Networks for Sociophysical Neuromorphic AI. Preprints 2024, 2024061912. https://doi.org/10.20944/preprints202406.1912.v1
Maksymov, I. Quantum-Tunnelling Deep Neural Networks for Sociophysical Neuromorphic AI. Preprints2024, 2024061912. https://doi.org/10.20944/preprints202406.1912.v1
APA Style
Maksymov, I. (2024). Quantum-Tunnelling Deep Neural Networks for Sociophysical Neuromorphic AI. Preprints. https://doi.org/10.20944/preprints202406.1912.v1
Chicago/Turabian Style
Maksymov, I. 2024 "Quantum-Tunnelling Deep Neural Networks for Sociophysical Neuromorphic AI" Preprints. https://doi.org/10.20944/preprints202406.1912.v1
Abstract
The discovery of the quantum tunnelling effect---the transmission of particles through a high potential barrier---was one of the most impressive achievements of quantum mechanics made in the 1920s. Responding to the contemporary challenges, I introduce a novel deep neural network (DNN) architecture that processes information using the effect of quantum tunnelling. I demonstrate the ability of the quantum tunnelling DNN (QT-DNN) to recognise optical illusions like a human. Hardware implementation of QT-DNN is expected to result in an inexpensive and energy-efficient neuromorphic chip suitable for applications in autonomous vehicles. The optical illusions recognition tests developed in this paper should lay foundations for cognitive benchmarking tasks for AI systems of the future, benefiting the fields of sociophysics and behavioural science.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
