Quantum-implemented selective reconstruction of high-resolution images
classification
🪐 quant-ph
keywords
imagereconstructionhopfieldimplementationalmostarbitrarilyassociativebases
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This paper proposes quantum image reconstruction. Input-triggered selection of an image among many stored ones, and its reconstruction if the input is occluded or noisy, has been simulated by a computer program implementable in a real quantum-physical system. It is based on the Hopfield associative net; the quantum-wave implementation bases on holography. The main limitations of the classical Hopfield net are much reduced with the new, original -- quantum-optical -- implementation. Image resolution can be almost arbitrarily increased.
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