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
- Yong Chae Lim
- Hsuan-Hao Lu
- Joseph Lukens
- Muneer Alshowkan
- Rangasayee Kannan
- Adam Siekmann
- Adam Stevens
- Anees Alnajjar
- Brian Post
- Brian Williams
- Bryan Lim
- Claire Marvinney
- Harper Jordan
- Hong Wang
- Hyeonsup Lim
- Jiheon Jun
- Joel Asiamah
- Joel Dawson
- Mariam Kiran
- Nance Ericson
- Peeyush Nandwana
- Priyanshi Agrawal
- Roger G Miller
- Ryan Dehoff
- Sarah Graham
- Srikanth Yoginath
- Sudarsanam Babu
- Tomas Grejtak
- Varisara Tansakul
- Vivek Sujan
- William Peter
- Yiyu Wang
- Yukinori Yamamoto
- Zhili Feng

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 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.

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