Lincoln Lab unveils the most powerful AI supercomputer at any US university
Optimized for generative AI, TX-GAIN is driving innovation in biodefense, materials discovery, cybersecurity, and other areas of research and development.
Optimized for generative AI, TX-GAIN is driving innovation in biodefense, materials discovery, cybersecurity, and other areas of research and development.
Explosive growth of AI data centers is expected to increase greenhouse gas emissions. Researchers are now seeking solutions to reduce these environmental harms.
The research center, sponsored by the DOE’s National Nuclear Security Administration, will advance the simulation of extreme environments, such as those in hypersonic flight and atmospheric reentry.
Researchers achieved a type of coupling between artificial atoms and photons that could enable readout and processing of quantum information in a few nanoseconds.
MIT researchers developed a photon-shuttling “interconnect” that can facilitate remote entanglement, a key step toward a practical quantum computer.
The Exo 2 programming language enables reusable scheduling libraries external to compilers.
The advance holds the promise to reduce error-correction resource overhead.
As the use of generative AI continues to grow, Lincoln Laboratory's Vijay Gadepally describes what researchers and consumers can do to help mitigate its environmental impact.
By emulating a magnetic field on a superconducting quantum computer, researchers can probe complex properties of materials.
The advance offers a way to characterize a fundamental resource needed for quantum computing.
The advance brings quantum error correction a step closer to reality.
The Advanced Computing Users Survey, sampling sentiments from 120 top-tier universities, national labs, federal agencies, and private firms, finds the decline in America’s advanced computing lead spans many areas.
Researchers have demonstrated directional photon emission, the first step toward extensible quantum interconnects.
Lincoln Laboratory Supercomputing Center dataset aims to accelerate AI research into managing and optimizing high-performance computing systems.
With a tensor language prototype, “speed and correctness do not have to compete ... they can go together, hand-in-hand.”