Developers can reduce quantum computing errors using custom machine learning algorithms
At present, quantum computer systems make too many errors to ever be really helpful, however, a synthetic intelligence that may appropriate quantum errors might supply an answer. The duty is extra complicated in quantum computing as a result every qubit, or quantum bit, exists in a blended state of zero and 1, and any try to establish errors by instantly measuring qubits destroys the information. Researchers have developed a way to identify sources of error in quantum computers through Artificial intelligence and machine learning, providing hardware developers the ability to pinpoint performance degradation with unprecedented accuracy.
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, scientists developed a new way to process the measurement results using custom Artificial Intelligence algorithms.
The ability to identify and suppress sources of performance degradation in quantum hardware was critical to both basic research and industrial efforts in 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. The published results in a prestigious, peer-reviewed journal validate the benefit of ongoing cooperation between foundational scientific research in a university laboratory and deep-tech startups.
Future applications of the technology could include mapping all transport modes and crowd movements simultaneously in real-time and automatically updating the schedule to solve disruption issues. Australia is seen as one of the forerunners in quantum computing. In 2018, scientists from the University of Melbourne simulated the power of quantum computing on supercomputers to crack a mathematical problem that would have required the memory capacity of more than a billion laptops to solve.
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