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Crews at the Sanford Underground Research Facility have moved about 39% of the I-beam steel needed to construct the cryotat for the Deep Underground Neutrino Experiment. That represents nearly 2 million pounds of steel that completely fills the south cavern that is 500 feet long, 65 feet wide, and about 7 stories tall.

Bosonic quantum error-correction devices at Fermilab’s SQMS Center use multiple quantum states within harmonic oscillators, a departure from traditional two-level qubit systems and a potential pathway to increased fault tolerance.

The Deep Underground Neutrino Experiment led by Fermilab is included in the line up of most ambitious international science projects that demonstrates how modern science transcends borders and inspires new technologies.

NVIDIA announced an expansion of the NVIDIA CUDA-Q open source platform with CUDA-Q Logical, a verifiable approach to developing useful applications for fault-tolerant quantum computers. Fermilab researchers used the platform accelerating fault-tolerant algorithm development from five months to three weeks, a 7x speedup.

Rice University’s DUNE-TECH program introduces undergraduates to the science behind DUNE, one of the nation’s most ambitious physics experiments.

A novel underwater detection experiment will continue Minnesota’s legacy of neutrino research. The Lake Vermillion neutrino detector experiment is being led by Fermilab.

Gustavo Cancelo, senior scientist at Fermilab near Chicago talks about QICK, the Quantum Instrumentation Control Kit, an open source platform developed at Fermilab that has become a widely adopted tool for quantum control and readout across four continents.

Fermilab has refined measurements of the muon’s anomalous magnetic moment by focusing on where these calculations begin at the detector. The team achieved results more consistent with existing theoretical predictions without confirming new physics.

Researchers at Fermilab, Stanford University and the University of Delaware recently showed that one of the oldest precision experiments performed by physicists could also be used to search for millicharged particles.

Alex Drlica-Wagner of Fermilab and other researched deployed an AI learning model where to point a telescope and predict what happened next, then compare its guess with what the humans actually did and made it correct itself.