Quantum-Inspired Neural Network Model of Optical Illusions

• URL: http://arxiv.org/abs/2312.03447v1
• Date: Wed, 6 Dec 2023 12:10:56 GMT
• Title: Quantum-Inspired Neural Network Model of Optical Illusions
• Authors: Ivan S. Maksymov
• Abstract summary: We train a deep neural network model to simulate the human's perception of the Necker cube. Our results will find applications in video games and virtual reality systems employed for training of astronauts and operators of unmanned aerial vehicles.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Ambiguous optical illusions have been a paradigmatic object of fascination, research and inspiration in arts, psychology and video games. However, accurate computational models of perception of ambiguous figures have been elusive. In this paper, we design and train a deep neural network model to simulate the human's perception of the Necker cube, an ambiguous drawing with several alternating possible interpretations. Defining the weights of the neural network connection using a quantum generator of truly random numbers, in agreement with the emerging concepts of quantum artificial intelligence and quantum cognition we reveal that the actual perceptual state of the Necker cube is a qubit-like superposition of the two fundamental perceptual states predicted by classical theories. Our results will find applications in video games and virtual reality systems employed for training of astronauts and operators of unmanned aerial vehicles. They will also be useful for researchers working in the fields of machine learning and vision, psychology of perception and quantum-mechanical models of human mind and decision-making.

Related papers

• Deep Quantum Graph Dreaming: Deciphering Neural Network Insights into Quantum Experiments [0.5242869847419834]
  We use a technique called $inception$ or $deep$ $dreaming$ to explore what neural networks learn about quantum optics experiments. Our story begins by training deep neural networks on the properties of quantum systems. We find that the network can shift the initial distribution of properties of the quantum system, and we can conceptualize the learned strategies of the neural network.
  arXiv  Detail & Related papers  (2023-09-13T16:13:54Z)
• ShadowNet for Data-Centric Quantum System Learning [188.683909185536]
  We propose a data-centric learning paradigm combining the strength of neural-network protocols and classical shadows. Capitalizing on the generalization power of neural networks, this paradigm can be trained offline and excel at predicting previously unseen systems. We present the instantiation of our paradigm in quantum state tomography and direct fidelity estimation tasks and conduct numerical analysis up to 60 qubits.
  arXiv  Detail & Related papers  (2023-08-22T09:11:53Z)
• Linking Physics and Psychology of Bistable Perception Using an Eye Blink Inspired Quantum Harmonic Oscillator Model [0.0]
  This paper introduces a novel quantum-mechanical model that describes psychological phenomena. Study1 demonstrates the application of the proposed model to bistable perception of ambiguous figures. Study2 we demonstrate a viable physiological connection between physics and bistable perception.
  arXiv  Detail & Related papers  (2023-06-23T06:10:00Z)
• Adapting Brain-Like Neural Networks for Modeling Cortical Visual Prostheses [68.96380145211093]
  Cortical prostheses are devices implanted in the visual cortex that attempt to restore lost vision by electrically stimulating neurons. Currently, the vision provided by these devices is limited, and accurately predicting the visual percepts resulting from stimulation is an open challenge. We propose to address this challenge by utilizing 'brain-like' convolutional neural networks (CNNs), which have emerged as promising models of the visual system.
  arXiv  Detail & Related papers  (2022-09-27T17:33:19Z)
• Formal Conceptual Views in Neural Networks [0.0]
  We introduce two notions for conceptual views of a neural network, specifically a many-valued and a symbolic view. We test the conceptual expressivity of our novel views through different experiments on the ImageNet and Fruit-360 data sets. We demonstrate how conceptual views can be applied for abductive learning of human comprehensible rules from neurons.
  arXiv  Detail & Related papers  (2022-09-27T16:38:24Z)
• Quantum Structure in Human Perception [0.0]
  We investigate the ways in which the quantum structures of superposition, contextuality, and entanglement have their origins in human perception itself. Our analysis takes us from a simple quantum measurement model, along how human perception incorporates the warping mechanism of categorical perception.
  arXiv  Detail & Related papers  (2022-08-07T13:59:23Z)
• Standard Model Physics and the Digital Quantum Revolution: Thoughts about the Interface [68.8204255655161]
  Advances in isolating, controlling and entangling quantum systems are transforming what was once a curious feature of quantum mechanics into a vehicle for disruptive scientific and technological progress. From the perspective of three domain science theorists, this article compiles thoughts about the interface on entanglement, complexity, and quantum simulation.
  arXiv  Detail & Related papers  (2021-07-10T06:12:06Z)
• On quantum neural networks [91.3755431537592]
  We argue that the concept of a quantum neural network should be defined in terms of its most general function. Our reasoning is based on the use of the Feynman path integral formulation in quantum mechanics.
  arXiv  Detail & Related papers  (2021-04-12T18:30:30Z)
• The Hintons in your Neural Network: a Quantum Field Theory View of Deep Learning [84.33745072274942]
  We show how to represent linear and non-linear layers as unitary quantum gates, and interpret the fundamental excitations of the quantum model as particles. On top of opening a new perspective and techniques for studying neural networks, the quantum formulation is well suited for optical quantum computing.
  arXiv  Detail & Related papers  (2021-03-08T17:24:29Z)
• The power of quantum neural networks [3.327474729829121]
  In the near-term, however, the benefits of quantum machine learning are not so clear. We use tools from information geometry to define a notion of expressibility for quantum and classical models. We show that quantum neural networks are able to achieve a significantly better effective dimension than comparable classical neural networks.
  arXiv  Detail & Related papers  (2020-10-30T18:13:32Z)
• Quantum Deformed Neural Networks [83.71196337378022]
  We develop a new quantum neural network layer designed to run efficiently on a quantum computer. It can be simulated on a classical computer when restricted in the way it entangles input states.
  arXiv  Detail & Related papers  (2020-10-21T09:46:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.