Predictive surrogate models drastically cut quantum computing measurement costs

New predictive surrogate models can drastically cut quantum computing measurement costs. Researchers developed classical machine learning models that learn and predict quantum processor outputs, significantly reducing the need for expensive hardware measurements. This innovation promises greater efficiency and accessibility for quantum computing tasks. These "digital twins" of quantum processors offer provable guarantees and are insensitive to system size, potentially democratizing access to advanced quantum hardware for a wider scientific community.