AI Enables Efficiencies in Quantum
(Military.Spot) Researchers from the U.S. Army Combat Capabilities Development Command’s Army Research Laboratory and Tulane University combined machine learning with quantum information science, or QIS, using photon measurements to reconstruct the quantum state of an unknown system.
QIS is a rapidly advancing field that exploits the unique properties of microscopic quantum systems, such as single particles of light or individual atoms, to achieve powerful applications in communication, computing and sensing, which are either impossible or less efficient under conventional means.
“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,” said Dr. Brian Kirby, a scientist at the Army’s corporate research laboratory. “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.
The researchers recently implemented a system that reconstructed quantum states and standard, more computationally intensive methods–in several cases, outperforming those methods.
The team simulated familiar sources of error in measurement, such as misaligned optical elements, and used these to train the machine learning system. The researchers further tested their system when measurements were not just noisy but completely missing. Notably, the team outperformed conventional state reconstruction methods in each situation while requiring fewer computational resources,