91°µÍø

Qubits must typically be kept isolated and very cold to minimize interactions with the external environment. These interactions lead to qubit decoherence - essentially loss of quantum information - and adversely affect the efficiency of quantum computing schemes. However, it may be possible to not only control these environmental interactions, but harness them in a constructive manner that results in entanglement, versus destroying it. The result is a scalable, more efficient, quantum computing platform that doesn't require cryogenics to operate.

Reducing the propagation loss, while increasing electric field confinement, is a major goal of nanophotonics for future high bandwidth, high processing speed computational requirements. However, in the current state-of-the-art metal waveguides, the propagating signal suffers restrictive limiting losses as the size of the components are reduced to the nano-scale regime. In this project we seek to exploit the propagation of surface plasmon nanojets on nanostructured thin films in order to reduce propagation losses while retaining field confinement. This improvement will allow advances into future nanophotonic-based computational platforms that will leapfrog Moore’s Law.

Neuromorphic computing is one proposed computing architecture for the beyond Moore's Law computing landscape. Neuromorphic computers are software/hardware systems that have implementations with features inspired by biological brains. Key research questions associated with neuromorphic computing are (1) how to program or train these architectures to perform tasks and (2) what supporting software is required in order to integrate these architectures into real systems and make them accessible to novice users. The goal of this project is to develop algorithms for training neuromorphic computers and supporting software for neuromorphic computers, including development environments, visualization tools, and hardware simulators.

There has been a recent surge of success in utilizing Deep Learning (DL) in imaging and speech applications for its relatively automatic feature generation and, in particular for convolutional neural networks (CNNs), high accuracy classification abilities. While these models learn their parameters through data-driven methods, model selection (as architecture construction) through hyper-parameter choices remains a tedious and highly intuition driven task. To address this, Multi-node Evolutionary Neural Networks for Deep Learning (MENNDL) is proposed as a method for automating network selection on computational clusters through hyper-parameter optimization performed via genetic algorithms.

EDEN is a visual analytics tool for exploratory analysis of multivariate data sets. Based on an interactive variant of parallel coordinates, EDEN includes statistical analytics that guide the user to significant associations in complex data sets without information loss.

While the term ‘innovation ecosystem’ is often utilized, the concept is rarely quantified. Oak Ridge National Lab conducted a ground-breaking application of natural language processing, link analysis and other computational techniques to transform text and numerical data into metrics on clean energy innovation activity and geography for the U.S. Department of Energy. The project demonstrates that a machine-assisted methodology gives the user a replicable method to rapidly identify, quantify and characterize clean energy innovation ecosystems.

The Beholder system is a software client / server system that detects intrusion by monitoring the real-world execution time of critical kernel-level operations. Beholder was designed for use with critical infrastructure systems, especially in the power grid.

Hyperion is a software system for static analysis of compiled software, enabling the detection of undesirable behavior or the demonstration of correct behavior.

ORNL is developing quantum information tools to help secure the electric grid. Researchers are working to extend the range and reduce the cost of quantum key distribution.