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This NSF-funded project marks a significant step toward Quantum Intelligent Sensor Networks (QISNs), moving beyond isolated sensors to engineer distributed, entangled nodes that function as coordinated systems for environmental monitoring and disaster preparedness. By leveraging continuous-variable squeezed light and quantum error correction, the research aims to extract weak spatial signals that exceed classical sensing limits while addressing real-world challenges like optical loss and phase noise. The initiative also highlights the emerging intersection of physical quantum intelligence and neural networks within Stony Brook's AI Innovation Institute.

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NSF Awards $600,000 to Stony Brook and University of Hawaiʻi to Advance Distributed Quantum Intelligent Sensor Networks

Quantum Computing ReportSeptember 30, 20262 min read

Researchers at Stony Brook University and the University of Hawaiʻi at Mānoa have been awarded a $600,000 NSF grant to develop Quantum Intelligent Sensor Networks (QISNs). This project aims to advance quantum metrology by engineering distributed, entangled sensor nodes that work together as a coordinated system, rather than optimizing single isolated sensors. The research will focus on using distributed quantum resources to extract weak, spatially varying signals beyond classical sensing limits, with applications in environmental monitoring, disaster preparedness, magnetic field imaging, and resilient communications. The post NSF Awards $600,000 to Stony Brook and University of Hawaiʻi to Advance Distributed Quantum Intelligent Sensor Networks appeared first on Quantum Computing Report.

Researchers from Stony Brook University and the University of Hawaiʻi at Mānoa have been awarded a three-year, $600,000 grant from the National Science Foundation (NSF) under its Foundations of Emerging Technologies program to…

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