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Army-Funded Academic Research Trials Machine Learning Approach to Correct Quantum Info Errors; Sara Gamble Quoted

1 min read
Domain-Aware
Domain-Aware Artificial Intelligence

A U.S. Army-supported research team from Louisiana State University has demonstrated the use of machine learning to correct errors found in quantum information systems.

The research team tested how a neural network's self-learning and self-evolving functions can correct information used in quantum sensing and communications, the Army said Monday.

This approach is designed to correct the distorted quantum information in photon-composed systems. The team said the technology has the potential to encode multiple information bits in a single photon during scenarios with atmospheric turbulence.

“The team’s result is an exciting step forward in developing this understanding, and it has the potential to ultimately enhance the Army’s sensing and communication capabilities on the battlefield," said Sara Gamble, program manager at the Army Research Office (ARO) within the Army Research Laboratory (ARL).

Join Potomac Officers Club for the 3rd Annual AI Summit on March 30th to learn about the progress, innovations and accomplishments of automation over the last year, what challenges remain, and how industry can help in 2021 and beyond.

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