New machine learning algorithm to detect quantum errors

Researchers at the University of Sydney and quantum control startup Q-CTRL have developed a way to identify sources of error in quantum computers through machine learning, providing hardware developers the ability to pinpoint performance degradation with unprecedented accuracy. A joint scientific paper detailing the research, Quantum oscillator noise spectroscopy via displaced Cat states, was published in the Physical Review Letters, a physical science research journal and flagship publication of the American Physical Society. Focused on reducing errors caused by environmental "noise" – the Achilles' heel of quantum computing – the University of Sydney team developed a technique to detect the tiniest deviations from the precise conditions needed to execute quantum algorithms using trapped ion and superconducting quantum computing hardware. These are the core technologies used by industrial quantum computing efforts at IBM, Google, Honeywell and others. To pinpoint the source of the measured deviations, Q-CTRL scientists developed a new way to process the measurement results using custom machine learning algorithms.