A team of Oak Ridge National Laboratory (ORNL) scientists involved in research topics of cybersecurity, statistical approaches, control systems, and dynamical models, reported a basic approach to security of physical systems that are interfaced with IT
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This work develops an approach for engineering non-Gaussian photonic states in discrete frequency bins.
ORNL researchers developed a novel nonlinear level set learning method to reduce dimensionality in high-dimensional function approximation.
Researchers from ORNL, Stanford University, and Purdue University developed and demonstrated a novel, fully functional quantum local area network (QLAN).
Quantum Monte Carlo (QMC) methods are used to find the structure and electronic band gap of 2D GeSe, determining that the gap and its nature are highly tunable by strain.
Researcher proved that quantum resources are capable of revealing the magnetic structure and properties of magnetic materials such as rare earth tetraborides.
ORNL researchers developed a stochastic approximate gradient ascent method to reduce posterior uncertainty in Bayesian experimental design involving implicit models.
To help expedite the use of quantum processing units, ORNL researchers developed an advanced software framework.
A team of ORNL researchers has used the DCA++ application, a popular code for predicting the performance of quantum materials, to verify two performance-enhancing strategies.