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.
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Quantum computing promises a platform for efficiently solving certain types problems thought to be intractable for traditional computers. The number of qubits needed to be competitive with classical computers varies dramatically depending on the problem. This project seeks to determine the maximum quantum operation rate for a given cooling capacity.
Developing a ground-based, quantum-secured, authenticated time distribution system for the energy grid.
The Oak Ridge National Laboratory's Computational Data Analytics Group's has worked over 12 years in creating text analytics systems to quickly discover meaningful information from raw data. These capabilities focus on six key areas, emphasizing high performance over very large sets of raw documents.
Collecting and Extracting: Collecting millions of documents from databases, Internet, Social Media, and hard drives; extracting text from hundreds of file formats; and translating this information into multiple languages.
Storing and Indexing: Storing and indexing millions of documents in search servers, distributed file systems (MapReduce), relational databases, and file systems.
Recommending: Filtering the full content of millions of documents to recommend the most valuable and relevant information based on a user’s own information, or user selections, or a user’s interactions with information.
Categorize: Grouping items based on the full content of documents using supervised and semi-supervised machine learning methods and targeted search lists.
Clustering: Creating a hierarchical group of documents based on similarity using unsupervised learning methods on the full content of each document.
Visualizing: Showing hierarchies, groups, and relationships among documents that helps the user quickly understand their value, and to see new connections.
This work has resulted in eight issued ( 7,072,883 7,315,858 7,693,903 7,805,446 7,937,389 8,473,314 8,825,710 9,256,649) and one pending patents , several commercial licenses (including Pro2Serve and TextOre), a spin off company (Global Security Information Analysts LLC (GSIA)), an R&D 100 Awards, and scores of peer reviewed research publications.
Collecting and Extracting: Collecting millions of documents from databases, Internet, Social Media, and hard drives; extracting text from hundreds of file formats; and translating this information into multiple languages.
Storing and Indexing: Storing and indexing millions of documents in search servers, distributed file systems (MapReduce), relational databases, and file systems.
Recommending: Filtering the full content of millions of documents to recommend the most valuable and relevant information based on a user’s own information, or user selections, or a user’s interactions with information.
Categorize: Grouping items based on the full content of documents using supervised and semi-supervised machine learning methods and targeted search lists.
Clustering: Creating a hierarchical group of documents based on similarity using unsupervised learning methods on the full content of each document.
Visualizing: Showing hierarchies, groups, and relationships among documents that helps the user quickly understand their value, and to see new connections.
This work has resulted in eight issued ( 7,072,883 7,315,858 7,693,903 7,805,446 7,937,389 8,473,314 8,825,710 9,256,649) and one pending patents , several commercial licenses (including Pro2Serve and TextOre), a spin off company (Global Security Information Analysts LLC (GSIA)), an R&D 100 Awards, and scores of peer reviewed research publications.
Big data demands the need for intelligent, recommender agents that can enhance a person’s situational or domain awareness of their environment. The ability to have a keen awareness and availability of relevant information provides a critical competitive edge. Unfortunately, there is simply too much data streaming too quickly for a person to manually process, analyze, and take action within a reasonable amount of time. In an attempt to alleviate this challenge, many people subscribe to relevant Internet information. There may be forms of subscriptions with the most common being Really Simple Syndication (RSS), blogs, even Facebook and Twitter. The concept is simple, when new information is posted to the site; a subscriber sees a list of this new information. The subscriber then has the option of following a link to read more. This approach is a very useful and successful model for monitoring this data, but it does have some significant drawbacks. In practice, the feeds of new information become quite lengthy, and contain more information than can be practically read. Furthermore, there can be a significant number of items that have little interest to the subscriber. Thus, the ability to find new and relevant information proves critical. We have developed a content-based recommender system that addresses both of these problems. The flexibility of input allows the system to be adaptable to industry and government use cases and data sets such as news feeds, resumes, proposal requests, etc.
Nonlinear interferometers, which use parametric amplifiers in place of beam splitters, can improve the signal to noise ratio of interferometric sensors by a factor of twice the power gain. Recently ORNL has realized a novel, inherently stable, nonlinear interferometer using nonlinear rubidium (Rb) vapor. This approach reduces the complexity and the size, weight and power requirements (SWAP) of earlier demonstrations. However, it is still constructed using bulk, free-space optics on a lab table. This project seeks to realize a reduced SWAP further and perform measurements to quantify its performance relative to other approaches.
We propose an entirely new experimental photonic qubit interface which will enable quantum connections between common material qubits such as ions or atoms.
Localized electron emission from nanostructures can be achieved with the aid of excitation of plasmons with short optical pulses.
Keep Information Safe at Sea with Quantum Physics
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.