September 19, 2021

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ML Technique Used to Pinpoint Quantum Errors

HPCwire, 29 Jul 2021, via ACM TechNews, Friday, July 30, 2021

Researchers at Australia's University of Sydney (USYD) and quantum control
startup Q-CTRL have designed a method of pinpointing quantum computing
errors via machine learning (ML). The USYD team devised a means of
recognizing the smallest divergences from the conditions necessary for
executing quantum algorithms with trapped ion and superconducting quantum
computing equipment. Q-CTRL scientists assembled custom ML algorithms to
process the measurement results, and minimized the impact of background
interference using existing quantum controls. This yielded an easy
distinction between sources of correctable "real" noise and phantom
artifacts of the measurements themselves. USYD's Michael J. Biercuk said,
"The ability to identify and suppress sources of performance degradation in
quantum hardware is critical to both basic research and industrial efforts
building quantum sensors and quantum computers."

  [“Who needs error-correcting codes when we have machine learning?''  PGN]