Chris Xu reappointed as director of Applied and Engineering Physics
Chris Xu, IBM Professor of Engineering, has been reappointed to a three-year term. Read more about Chris Xu reappointed as director of Applied and Engineering Physics
Quantum sensors have the ability to detect incredibly small changes in time, gravity, and magnetic fields, among other applications. Using photonic quantum entanglement, Benjamin Malia, a postdoctoral researcher at Cornell, will advance the precision of quantum sensors as one of the newest Intelligence Community Postdoctoral Research Fellows.
The 2022 fellowships, granted by the Office of the Director of National Intelligence, began Oct. 3 and support unclassified basic research in areas of interest to the intelligence community.
“It’s an honor to be selected for this fellowship,” said Malia, who is advised by Peter McMahon, assistant professor of applied and engineering physics. “I’m excited about this opportunity and to be working on the development of cutting-edge quantum technology.”
Malia’s research uses materials called nonlinear crystals to generate quantum entangled photons that can be used for a number of applications. One application is molecular sensing, in which one of the entangled photons is sent to a sample and the other to a detector. If the sample’s photon is absorbed due to the sample’s molecular composition, for instance, the entangled photon at the detector will mimic the other photon’s state, giving scientists a tool to analyze molecular composition from a distance.
But this quantum sensing is difficult to perform in a field setting because such systems are sensitive to vibrations and thermal changes, among other disturbances, and photonic loss remains a key obstacle.
“Our goal is to scale up such a system by generating as much entanglement as possible between many photons,” Malia said. “In addition to the hardware challenges associated with this experiment, we also plan to incorporate machine learning methods in processing the results. The photons we count may not directly, or not efficiently, give us the actual quantity we want to measure. Machine learning algorithms can help extract useful information from potentially messy data collection.”
Intelligence officials envision more robust quantum sensors being used for applications such as navigation and timing in GPS-denied environments, efficient and accurate calibration of antennas, communication and navigation with magnetic fields, and other geospatial-intelligence gathering.
In previous work, Malia used quantum entanglement to improve the precision of atomic sensors, which were applicable to atomic magnetometers, atom interferometer gravimeters and atomic clocks.