Bayesian statistical methods help improve the predictability of complex computational models in experimentally unknown research.
Bayesian statistical methods help improve the predictability of complex computational models in experimentally unknown research.
Fluxonium qubits can build cutting-edge quantum devices that will harness the potential of quantum computing.
FAIR (findable, accessible, interoperable, reusable) principles facilitate the use of large data sets by human and machine researchers.
Forefront nuclear physics capabilities and machine-learning data analyses combine to generate new information on quantum energy levels in sulfur-38.
JLab adapts internet TV approach to filter, calibrate and analyze accelerator data in real time.
Scientists successfully measure high-dimensional qudits, cousins to quantum computing qubits.
Analytical techniques can compute the strength of the connection between information in articles and health conditions.
For the first time, the error correction process significantly enhances the lifetime of quantum information.
Snekmer allows scientists to use rapid prototyping to better understand the function of proteins in microbes.
Machine learning techniques track turbulent blobs in millions of frames of video from tokamak experiments.