What’s the result?
Q-CTRL and the University of Sydney researchers have devised an error identification method in quantum computers.
The quantum control start-up and university researchers can now identify errors via machine learning. This will enable hardware developers to spot performance degradation which will, in turn, enable them to hasten paths to effective quantum computers.
The University of Sydney researchers were fixated on developing a technique that detects the smallest deviations from the exact conditions that are required to execute quantum algorithms via superconducting and trapped-ion computing hardware. These technologies are widely used by global industrial quantum computing efforts at Google, IonQ, IBM and Honeywell.
Q-CTRL researchers coined a method that enables the processing of measurement results via machine learning algorithms. The scientists also minimized the impact of background intrusion. The result? Simple discrimination between phantom artifacts and real noise fixable sources.
The combination of machine learning with experimental techniques has advantaged the development of quantum computers. The Q-CTRL researchers developed machine learning solutions that ease data translations. This is critical in industrial efforts to build quantum computers and quantum sensors as well as basic research efforts.
Quantum control that is based on machine learning has created a pathway that accelerates R&D timelines and makes the systems useful. The peer-reviewed published results validate the valuable efforts and results from the collaboration between the start-up and the university researchers.
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