US Patent Issued to Ernst & Young on Dec. 2 for "Methods and apparatus for time-series forecasting using deep learning models of a deep belief network with quantum computing" (Canadian, American Inventors)

ALEXANDRIA, Va., Dec. 2 -- United States Patent no. 12,488,263, issued on Dec. 2, was assigned to Ernst & Young LLP (Toronto).

"Methods and apparatus for time-series forecasting using deep learning models of a deep belief network with quantum computing" was invented by Sherif Barrad (Ile-Bizard, Canada), Ricardo A. Collado (Melrose, Mass.), Biren Agnihotri (Ontario, Canada) and Olumide Akinola (North York, Canada).

According to the abstract* released by the U.S. Patent & Trademark Office: "An apparatus including a Deep Belief Network is configured to receive, via a processor, input data. The processor is caused to initialize, based on the input data, weights for a learning model of the DBN. The processor is further caused to generate, via...