Johns Hopkins Team Models Quantum Noise on Superconducting Processors
AI-summarised brief · reviewed before publication
Researchers at Johns Hopkins Applied Physics Laboratory and Johns Hopkins University have developed a unified noise-modeling framework for superconducting quantum processors, improving predictive accuracy sevenfold compared to existing approaches. The framework combines multiple sources of quantum noise into a single experimentally validated model, which could inform hardware design, algorithm development, and error-correction strategies. The team used cloud access to 39 qubits across seven superconducting devices, reflecting real-world user interactions with quantum computers.