The breakthrough which was made jointly by the researchers at University of Sydney and quantum control startup: Q-CTRL provide solution to problems of identifying errors in quantum computing through machine learning..
The breakthrough, in a statement published by the team involved in the research is capable of helping hardware developer to easily identified performance deterioration and quicken the path to the quantum computer recovery.
The research project, according to the team from the University of Sydney, who were drawn from Quantum Control Laboratory and was led by Professor Michael Biercuk, is aim at reducing the error cause by the environmental noise in quantum computing by developing a technique capable of discovering a very small deviation from the precise condition needed to execute a particular quantum algorithm using trapped ion and superconducting quantum computing hardware.
Computational error is a big challenges in quantum computer, even the most advance machines in use today don't run operation for a very long time before error creeps in and thereby causing programs fail.
Q-CTRL scientists have come out with a new technique design to help process the measurement results by using machine learning algorithms and thereby avoiding measurement deviation. By the deploying of the existing control measurement they were also able to minimise the unwanted interference in the process and thereby able to identify the real noise that could be fixed.
While commenting on the development, Q-CTRL CEO Professor 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".
“Quantum control, augmented by machine learning, has shown a pathway to make these systems practically useful and dramatically accelerate R&D timelines,” he said.
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