Passive Source Quantum Superresolution Assisted by Physics-Informed Robust Deep Learning

NSF Award Search · 01002526DB NSF RESEARCH & RELATED ACTIVIT · $424,999 · view on nsf.gov ↗

Abstract

Resolution is key to seeing fine details in imaging and sensing—whether it’s in biomedical imaging, observing distant stars, quantum measurements, or everyday optical systems. But all optical systems face a crucial barrier known as the diffraction limit, which sets a fundamental limit on how closely two points can be distinguished. Previous breakthroughs have achieved super-resolution by actively controlling or labeling the sample, which works well for certain biomedical systems. However, this approach isn’t possible for live biological samples that could be damaged by probes, for delicate quantum systems that can be disturbed by measurement, or for astronomical objects that we simply cannot manipulate. This project aims to overcome these challenges by developing new super-resolution methods that do not require controlling the source. The research team will combine advanced physical models of imaging with artificial intelligence (AI) to resolve details of passive, uncontrollable objects in real time. This research will promote the progress of imaging and sensing science by pushing the boundaries of what is possible in optical imaging, benefiting fields like medicine, astrophysics, and quantum sensing. In addition, by integrating artificial intelligence with optical engineering, the project will create unique educational opportunities in quantum and optical physics and AI for high school, undergraduate, and graduate students, helping to inspire and train the next genera

Key facts

NSF award ID
2514953
Awardee
Stevens Institute of Technology (NJ)
SAM.gov UEI
JJ6CN5Y5A2R5
PI
Xiaofeng Qian
Primary program
01002526DB NSF RESEARCH & RELATED ACTIVIT
All programs
Artificial Intelligence (AI), Machine Learning Theory, QUANTUM INFORMATION SCIENCE
Estimated total
$424,999
Funds obligated
$424,999
Transaction type
Standard Grant
Period
09/01/2025 → 08/31/2028