Title:
QUANTUM COMPUTING BASED DEEP LEARNING FOR DETECTION, DIAGNOSIS AND OTHER APPLICATIONS
Document Type and Number:
WIPO Patent Application WO/2021/257128
Kind Code:
A3
Abstract:
A method in an illustrative embodiment comprises configuring a machine learning system with a multi-layer network architecture comprising at least one neural network and one or more additional network layers, training the neural network at least in part utilizing quantum sampling performed by a quantum computing device, obtaining data characterizing a monitored system, processing at least a portion of the obtained data through at least a portion of the multi-layer network architecture of the machine learning system to generate a prediction of at least one characteristic of the monitored system from the obtained data, and executing at least one automated action relating to the monitored system based at least in part on the generated prediction. The neural network may comprise, for example, a deep belief network (DBN) that includes at least first and second restricted Boltzmann machines (RBMs) of respective first and second different types, or at least one conditional restricted Boltzmann machine (CRBM).
Inventors:
YOU FENGQI (US)
AJAGEKAR AKSHAY (US)
AJAGEKAR AKSHAY (US)
Application Number:
PCT/US2021/017801
Publication Date:
March 10, 2022
Filing Date:
February 12, 2021
Export Citation:
Assignee:
UNIV CORNELL (US)
International Classes:
G06N20/00
Foreign References:
| US20190121566A1 | 2019-04-25 | |||
| US20190220733A1 | 2019-07-18 | |||
| US20130132001A1 | 2013-05-23 |
Other References:
NAWAZ SYED JUNAID; SHARMA SHREE KRISHNA; WYNE SHURJEEL; PATWARY MOHAMMAD N.; ASADUZZAMAN MD.: "Quantum Machine Learning for 6G Communication Networks: State-of-the-Art and Vision for the Future", IEEE ACCESS, IEEE, USA, vol. 7, 1 January 1900 (1900-01-01), USA , pages 46317 - 46350, XP011719906, DOI: 10.1109/ACCESS.2019.2909490
Attorney, Agent or Firm:
RYAN, Joseph, B. (US)
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