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A new paper by a Los Alamos team has established a theoretical framework for predicting the critical number of parameters needed for quantum machine learning models to prevent stall-out. The technique, called overparametrization, enhances performance in quantum machine learning for applications that stymie classical computers. The research was conducted in the Laboratory’s Quantum Computing Summer School in 2021 and has implications for using machine learning to learn the properties of quantum data.