Laura Condon, a University of Arizona assistant professor of hydrology and atmospheric sciences, and Zheshen Zhang, assistant professor of materials science and engineering and optical sciences, have each received $1 million in funding from the National Science Foundation's Convergence Accelerator program.

"Tackling grand challenges like water management or quantum entanglement effectively requires bringing together expertise from many fields, each with its own piece of the needed solution, and teams like this need support to jump-start their ideas," said University of Arizona President Robert C. Robbins. "This funding allows our researchers to develop innovative ideas, with potentially transformative results. I am looking forward to Dr. Condon's and Dr. Zhang's progress."

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C-Accel is a new program that brings research teams together to address national-scale societal challenges, funding multidisciplinary teams with a quicker turnaround than traditional funding cycles. Condon and Zhang lead two of the 29 projects in the program's second cohort. Condon's project will advance cyberinfrastructure that can better model complex groundwater systems to fight climate change. Zhang is creating a prototype of a quantum sensor system that could improve areas including vehicle navigation, space communications and health care imaging.

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"We are excited for these teams to use convergence research and innovation-centric fundamentals to accelerate solutions that have a positive societal impact," said Douglas Maughan, head of C-Accel.

In June 2021, at the end of phase one of the funding process, the teams will participate in a pitch competition and a proposal evaluation. Selected teams will proceed to phase two, with potential funding of up to $5 million for 24 months.

Managing Water Resources With Machine Learning

Decisions about water management and use must be made every day, especially with costly events such as droughts and floods becoming increasingly common. To improve this decision-making process, Condon is partnering with CyVerse, a UArizona-led NSF-funded organization dedicated to providing life scientists with computational infrastructure to handle and analyze huge datasets. She'll be harnessing machine learning and data science to give U.S. water managers better information about how groundwater systems behave, so that they can make better decisions about how we use water.

"Water is one of the most pressing issues for climate change," Condon said, "and we need big physical hydrology models in order to make predictions about how environmental changes will impact our water resources. Our goal with this project is to make groundwater data and simulations more useful for decision making using machine learning."

Condon's team is partnering with the Bureau of Reclamation, the largest wholesale water provider in the country, which provides water to more than 31 million people.

"CyVerse cyberinfrastructure provides the tools and training that allow communities to derive more value from their data using advanced machine learning methods – we are making machine learning and artificial intelligence methods more accessible to communities and decision-makers," said Nirav Merchant, co-principal investigator of CyVerse and also a co-principal investigator of the new grant. "Condon's project will provide the unique opportunity to extend our capabilities by collaborating with some of the leading experts in the field."

Quantum Driving, Imaging and Communicating

Zhang will create a prototype of a network of sensors connected via quantum entanglement, which could improve sensor-based systems ranging from vehicle navigation to communications and medical imaging.

Quantum entanglement involves linking two particles, often atoms, in such a way that whatever happens to one particle influences the other. Zhang's method involves entangling photons, or light particles, which offers a stronger and more stable form of entanglement than can be achieved with atoms. It's a bit like rewriting the information from a more obscure language, like Latin, into a widely used language, like English. It's the same information but converting it to a different language means more people can understand and make changes to it, so the information has more impact.

"Atoms interact with the environment all the time – like, a pen is made of atoms, so if I pick that up, I'm interacting with it physically," Zhang said. "In comparison, entanglement carried by photons is quite robust, because photons typically do not interact with the environment in the same way; I can't pick up a ray of light."

Because sensing systems are ubiquitous, applications for this technology are broad. For instance, it could lead to the creation of an internally based navigation system for self-driving cars, thereby eliminating the risk of spoofing, which is when false GPS signals are used to guide a car to a location selected by hackers. It could also allow for faster and more precise medical imaging by entangling light sources in a method called atomic force microscopy.