Quantum-Inspired Neural Network Model of Optical Illusions
Ivan S. Maksymov
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.