Filter Results
Related Organization
- Biological and Environmental Systems Science Directorate (23)
- Computing and Computational Sciences Directorate (35)
- Energy Science and Technology Directorate (217)
- Fusion and Fission Energy and Science Directorate (21)
- Information Technology Services Directorate (2)
- Isotope Science and Enrichment Directorate (6)
- National Security Sciences Directorate
(17)
- Neutron Sciences Directorate (11)
- Physical Sciences Directorate (128)
- User Facilities (27)
Researcher
- Ali Passian
- Joseph Chapman
- Nicholas Peters
- Hsuan-Hao Lu
- Joseph Lukens
- Muneer Alshowkan
- Anees Alnajjar
- Brian Williams
- Bryan Lim
- Claire Marvinney
- Harper Jordan
- Jason Jarnagin
- Joel Asiamah
- Joel Dawson
- Mariam Kiran
- Mark Provo II
- Nance Ericson
- Peeyush Nandwana
- Rangasayee Kannan
- Rob Root
- Srikanth Yoginath
- Tomas Grejtak
- Varisara Tansakul
- Yiyu Wang

Here we present a solution for practically demonstrating path-aware routing and visualizing a self-driving network.

Technologies directed to polarization agnostic continuous variable quantum key distribution are described.
Contact:
To learn more about this technology, email partnerships@ornl.gov or call 865-574-1051.

The ever-changing cellular communication landscape makes it difficult to identify, map, and localize commercial and private cellular base stations (PCBS).

The development of quantum networking requires architectures capable of dynamically reconfigurable entanglement distribution to meet diverse user needs and ensure tolerance against transmission disruptions.

Polarization drift in quantum networks is a major issue. Fiber transforms a transmitted signal’s polarization differently depending on its environment.

This invention addresses a key challenge in quantum communication networks by developing a controlled-NOT (CNOT) gate that operates between two degrees of freedom (DoFs) within a single photon: polarization and frequency.

A new nanostructured bainitic steel with accelerated kinetics for bainite formation at 200 C was designed using a coupled CALPHAD, machine learning, and data mining approach.