Quantum-tunnelling deep neural network for optical illusion recognition
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866912241025024000 |
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| author | Maksymov, Ivan S. |
| author_facet | Maksymov, Ivan S. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_11013 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Quantum-tunnelling deep neural network for optical illusion recognition Maksymov, Ivan S. Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition Human-Computer Interaction Neural and Evolutionary Computing Physics and Society Quantum Physics 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. |
| title | Quantum-tunnelling deep neural network for optical illusion recognition |
| topic | Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition Human-Computer Interaction Neural and Evolutionary Computing Physics and Society Quantum Physics |
| url | https://arxiv.org/abs/2407.11013 |