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A new multilaboratory effort funded by the Defense Threat Reduction Agency is underway, creating a machine-learning tool to revolutionize the future of vaccine development, rapidly choosing a suitable vaccine platform …
Training a quantum neural network requires only a small amount of data, according to a new proof that upends previous assumptions stemming from classical computing’s huge appetite for data in …
Decreasing greenhouse gases, helping oil and gas companies cut costs, and ensuring nuclear safety—it’s all in a day’s work for one team of scientists from Los Alamos National Laboratory. With …
The field of machine learning on quantum computers got a boost from new research removing a potential roadblock to the practical implementation of quantum neural networks. While theorists had previously …
A machine-learning approach developed for sparse data reliably predicts fault slip in laboratory earthquakes and could be key to predicting fault slip and potentially earthquakes in the field. The research …
Researchers at Los Alamos National Laboratory are applying machine learning algorithms to help interpret massive amounts of ground deformation data collected with Interferometric Synthetic Aperture Radar (InSAR) satellites; the new …
Los Alamos researchers are the first to successfully demonstrate a machine-learning-based seismic imaging technique applying to real data. Once this model is fully trained, it can significantly reduce the computation …
Convolutional neural networks running on quantum computers have generated significant buzz for their potential to analyze quantum data better than classical computers can. While a fundamental solvability problem known as …
A Lab research team’s project was selected in a highly competitive $26 million Department of Energy (DOE) grant program to advance chemical and materials sciences by using data science. The …
Think Alexa or Siri, but meet Titan on the Red. LANL Weapons scientists and engineers who long for an automated research assistant will soon have that support when it comes …
