(Tulane.edu) Tulane University researchers are teaming up with the U.S. Army Research Office on a machine learning project that could pave the way for small, mobile quantum networks and possibly lead to unbreakable, secure communication systems, quantum computers and enhanced radar.
As part of the research, scientists combined machine learning with quantum information science, or QIS, using photon measurements to reconstruct the quantum state of an unknown system. An unknown system might be an image a facial recognition device does not recognize.
Tulane team members on the project include Ryan Glasser, associate professor of physics, and researchers Onur Danaci and Sanjaya Lohani. They conducted the research with Brian Kirby, a scientist at the U.S. Army Combat Capabilities Development Command’s Army Research Laboratory.
“We wanted to apply machine learning to problems in QIS, as machine learning systems are capable of making predictions based on example data sets without explicit programming for the given task,” Kirby said.
“Machine learning has excelled in recent years in fields such as computer vision, where a machine learning algorithm trained on large sets of pre-classified images can then correctly classify new images that it has never seen before.”
To characterize an unknown quantum system, researchers most often perform quantum state tomography, or QST. They prepare and measure identical unknown quantum systems, and use a complicated, time-consuming computational process to determine the quantum system most consistent with the measurement results.
However, researchers will need to develop alternative methods to process the classical information associated with quantum information protocols. “This field often overlooks the classical information processing needed to operate quantum information systems,” Glasser said. “However, as research and capabilities are now maturing to the point that real-world implementations are within sight, these are the sorts of engineering problems that we need to solve.”