Scientists in Australia have pioneered the use of AI-based algorithms to detect errors in quantum computers. This will make them faster and more accurate.
Researchers at the University of Sydney and quantum control company Q-CTRL have announced a way to identify sources of error in quantum computers using machine learning. This will enable hardware designers to pinpoint performance degradation and accelerate the path towards more efficient quantum computers.
In an effort to reduce errors caused by noise, the team developed a method to detect the smallest deviations from the exact conditions required to execute quantum algorithms using ion traps and superconducting quantum computing equipment. These technologies are used in leading industrial quantum devices from IBM, Google, Honeywell, IonQ, and other companies.
The scientists noted that the ability to identify and suppress sources of performance degradation in quantum equipment is critical for both basic research and industrial efforts to build quantum sensors and computers.
“Quantum control, augmented by machine learning, has shown us the way to make these systems practically useful and dramatically accelerate the development of quantum devices,” he said.
