May 3 2021
Scientists from Louisiana, including a team from the Tulane University School of Science and Engineering, have designed a smart quantum technology that could find real-world applications to quantum networks and future quantum communications systems employed in the military.
Tulane scientists are part of a team of Louisiana researchers looking at how smart quantum technology can improve communications in the military. Image Credit: Tulane University.
Ryan Glasser, an associate professor of physics at Tulane, together with his group in the Department of Physics, collaborated on the study with scientists from Louisiana State University. The research featured on the cover of the March 2021 issue of the journal Advanced Quantum Technologies.
Recent developments in optical technologies have resulted in extremely high information transfer rates using the spatial properties of light—i.e. images (and more complex structured beams). However, a difficulty in such communications using light through free-space is that turbulence can severely distort the beams, resulting in errors in the communication.
Ryan Glasser, Associate Professor of Physics, Tulane University
To rectify the faults, scientists have developed an artificial intelligence (AI) scheme to help solve the negative impacts of turbulence on light that travels through the air. The system adjusts for laser light’s spatial distortions.
We showed the system’s efficacy first in the classical regime by using simulations.
Ryan Glasser, Associate Professor of Physics, Tulane University
The study was financially supported via the U.S. Office of Naval Research to design AI methods to develop strong communications networks, under program officer Santanu Das. Further, Tulane joined hands with the Louisiana State University (LSU) team, which helped implement an experiment to illustrate that the AI method could be adapted to function with the help of quanta of light, or single photons.
The experiment our LSU collaborators performed shows that we can overcome the destructive effects of turbulence on single photons, which will aid in the real-world implementation of free-space quantum communication links.
Ryan Glasser, Associate Professor of Physics, Tulane University
“Such technologies are crucial to future quantum technologies, including quantum networks and quantum imaging. We’re excited to be doing research that combines the flourishing fields of quantum technologies and artificial intelligence,” added Glasser.
According to Sara Gamble, who is the program manager at the U.S. Army Research Office, the study is still in the initial stages of comprehending the ability of machine learning methods to play a part in quantum information science.
But, Glasser stated, “The team’s result is an exciting step forward in developing this understanding, and it has the potential to ultimately enhance the Army’s sensing and communication capabilities on the battlefield.”
Other scientists from Tulane who were involved in this study are Sanjaya Lohani, a postdoctoral researcher, and Erin M. Knutson, at present a postdoctoral fellow at Santa Clara University.
The LSU group includes PhD candidate Narayan Bhusal, postdoctoral researcher Chenglong You, graduate student Mingyuan Hong, undergraduate student Joshua Fabre, and assistant professor of physics Omar S. Magana-Loaiza. Pengcheng Zhao from Qingdao University of Science and Technology was also a part of this study.
Scientists from Louisiana are part of the Louisiana Quantum Initiative, a statewide attempt to progress the research and technology of quantum systems as part of the second quantum revolution and design the plan and technological infrastructure of quantum-driven networks and devices. The initiative is an ecosystem of studies that depends on emergent and dynamic associations and measures among institutions as well as individual members.
Journal Reference:
Bhusal, N., et al. (2021) Spatial Mode Correction of Single Photons Using Machine Learning. Advanced Quantum Technologies. doi.org/10.1002/qute.202000103.
Source: https://tulane.edu/