quantum

From University of Chicago, Oct. 24, 2018: National labs and the University of Chicago are to create one of world’s longest fiber-optic links to “teleport” information.

From Daily Herald, Oct. 9, 2018: The U.S. Department of Energy has announced that it has awarded scientists at its Fermi National Accelerator Laboratory more than $10 million to spur research that could revolutionize not only our understanding of nature, but also the very way we investigate it.

Their efforts apply research from multiple disciplines to hunt for dark matter – in particular, the much sought-after axion.

The federal grants will support University of Chicago, Argonne and Fermilab in their explorations of quantum computing, dark matter, and imaging.

Quantum science is expected to lay the foundation for the next generation of computing and information processing as well as an array of other innovative technologies.

Through its Quantum Information Science-Enabled Discovery program, DOE is awarding Fermilab researchers funding for five quantum-science initiatives. It also funds Fermilab’s participation in three further initiatives led by other institutions.

From Crain’s Chicago Business, Sept. 20, 2018: University of Chicago Professor David Awschalom says that accelerating quantum research is critical for Chicago, which is uniquely positioned to help lead the nation, thanks to its University of Chicago, Argonne National Laboratory and Fermilab. The decisions the U.S. makes now will determine the role it plays in a technology that could deliver a generational leap forward if we are bold enough to seize the opportunity.

High-energy physicists and members of industry will meet face-to-face to brainstorm ideas and participate in the field’s first hands-on quantum computing workshop.

Researchers from Caltech and the University of Southern California report the first application of quantum computing to a physics problem. By employing quantum-compatible machine learning techniques, they developed a method of extracting a rare Higgs boson signal from copious noise data.