# Quantum-tunnelling deep neural network for optical illusion recognition

arXiv: https://arxiv.org/abs/2407.11013 · HTML: https://mlsanity.com/paper/2407.11013/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 108 references that are in the map.
- First posted on arXiv 26 Jun 2024.

## Builds on (most cited first, 14 of 14)

- [Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images](https://mlsanity.com/paper/1412.1897/) (2014)
- [IllusionVQA: A Challenging Optical Illusion Dataset for Vision Language Models](https://mlsanity.com/paper/2403.15952/) (2024)
- [Theory of neuromorphic computing by waves: machine learning by rogue waves, dispersive shocks, and solitons](https://mlsanity.com/paper/1912.07044/) (2019)
- [Analogue and Physical Reservoir Computing Using Water Waves](https://mlsanity.com/paper/2306.09095/) (2023)
- [Predictive coding feedback results in perceived illusory contours in a recurrent neural network](https://mlsanity.com/paper/2102.01955/) (2021)
- [Quantum-Inspired Neural Network Model of Optical Illusions](https://mlsanity.com/paper/2312.03447/) (2023)
- [Reservoir Computing Using Measurement-Controlled Quantum Dynamics](https://mlsanity.com/paper/2403.01024/) (2024)
- [The Physics of Preference: Unravelling Imprecision of Human Preferences through Magnetisation Dynamics](https://mlsanity.com/paper/2310.00267/) (2023)
- [Learning from Pseudo-Randomness With an Artificial Neural Network - Does God Play Pseudo-Dice?](https://mlsanity.com/paper/1801.01117/) (2018)
- [Deep learning improved by biological activation functions](https://mlsanity.com/paper/1804.11237/) (2018)
- [Quantum propensities in the brain cortex and free will](https://mlsanity.com/paper/2107.06572/) (2021)
- [Causal potency of consciousness in the physical world](https://mlsanity.com/paper/2306.14707/) (2023)
- [Fake news: No ban, No spread -- with Sequestration](https://mlsanity.com/paper/2401.02167/) (2024)
- [Bistable perception, precision and neuromodulation](https://mlsanity.com/paper/2212.09729/) (2022)

## Built on by (most cited first, 6 of 6)

- [Quantum-Cognitive Neural Networks: Assessing Confidence and Uncertainty with Human Decision-Making Simulations](https://mlsanity.com/paper/2412.08010/) (2024)
- [Full-Precision and Ternarised Neural Networks with Tunnel-Diode Activation Functions: Computing and Physics Perspectives](https://mlsanity.com/paper/2503.04978/) (2025)
- [TunnElQNN: A Hybrid Quantum-classical Neural Network for Efficient Learning](https://mlsanity.com/paper/2505.00933/) (2025)
- [Transforming Traditional Neural Networks into Neuromorphic Quantum-Cognitive Models: A Tutorial with Applications](https://mlsanity.com/paper/2503.07681/) (2025)
- [Quantum-Cognitive Tunnelling Neural Networks for Military-Civilian Vehicle Classification and Sentiment Analysis](https://mlsanity.com/paper/2507.18645/) (2025)
- [Quantum Tunneling-Aware Machine Learning: Physics-Derived Noise Models for Robust Deployment](https://mlsanity.com/paper/2606.00741/) (2026)

## Abstract (arXiv)

The discovery of the quantum tunnelling (QT) 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 deep neural network (DNN) architecture that processes information using the effect of QT. I demonstrate the ability of QT-DNN to recognise optical illusions like a human. Tasking QT-DNN to simulate human perception of the Necker cube and Rubin's vase, I provide arguments in favour of the superiority of QT-based activation functions over the activation functions optimised for modern applications in machine vision, also showing that, at the fundamental level, QT-DNN is closely related to biology-inspired DNNs and models based on the principles of quantum information processing.