The Quantum Enhanced Optimization program aims to develop technology that can speed up the training of machine learning algorithms; support circuit fault diagnostics on larger circuits; and accelerate optimal scheduling of multiple machines on multiple tasks, the Office of the Director of National Intelligence said Monday.
“The goal of the QEO program is a design for quantum annealers that provides a 10,000-fold increase in speed on hard optimization problems, which improves at larger and larger problem sizes when compared to conventional computing methods,” said Karl Roenigk, QEO program manager at IARPA.
IARPA has awarded a QEO research contract to a consortium led by the University of Southern California.
The team includes Lockheed Martin, Northrop Grumman, California Institute of Technology, Harvard University, Massachusetts Institute of Technology, University of California at Berkeley, University College London, Saarland University, University of Waterloo and Tokyo Institute of Technology.
NASA‘s Ames Research Center and Texas A&M University will also provide validation support for the program, ODNI noted.