Simulated quantum-optical object recognition from high-resolution images
Document Type
Article
Publication Date
1-1-2005
Abstract
A holographic experimental procedure assuming use of quantum states of light is simulated. It uses merely interference-based image storage and nonunitary image retrieval realized by wave function collapse. Successful results of computational view-invariant recognition of object images are presented. As in neural net theory, recognition is selective reconstruction of an image from a database of many concrete images (simultaneously stored in an associative memory) after presentation of a different version of that image. That is, in the first step, we store many high-resolution images of objects into quantum memory (a hologram). In the second step, we present a �nonlearned� noisy image version. We thereby trigger memory-influenced reorganization of the state of the system so that it finally encodes those corrected object images that correspond to the newly presented version. The holographic procedure seems to be implementable with present-day quantum optics.
Keywords
Quantum state of light, artificial intelligence, computer science, wave function collapse, computational view-invariant
Divisions
ai
Publication Title
Optics and Spectroscopy
Volume
99
Issue
2
Publisher
Interperiodica