Cloud-tested quantum noise model predicts superconducting qubit errors with sevenfold better accuracy

Researchers from the Johns Hopkins Applied Physics Laboratory (APL) in Laurel, Maryland, and Johns Hopkins University in Baltimore have developed a practical, comprehensive noise-modeling framework for a popular class of superconducting quantum processors. Their work, published in the journal PRX Quantum, offers a sevenfold improvement in predictive accuracy over existing approaches.