Dartmouth Engineering to Lead $2.7M DOE Grant to Revolutionize Quantum Computing
Jul 27, 2021 | by Julie Bonette
Funded by a new $2.7 million grant from the US Department of Energy (DOE), Dartmouth Engineering Professor Geoffroy Hautier will lead a three-year, multi-institutional effort to identify qbits, a basic unit of quantum information, in order to transform and advance quantum computing. The team aims to build a database of viable qbits, which can store information in their spin, by analyzing defects in solids.
"This is an exciting time for quantum information science research. There has been compelling work in the last decade showing that defects in solids are viable qbits and could be the basic units for future quantum computers, but there is still not a perfect 'quantum defect. We are convinced that our approach will lead to important findings."
Geoffroy Hautier, Principal Investigator and the Hodgson Family Associate Professor of Engineering at Dartmouth
It has been shown that quantum computers will be significantly faster than current computers at solving certain complex problems. However, even though quantum computers have been built and are currently in operation, major breakthroughs are needed to scale up and truly revolutionize the field. While certain defects in solids have shown promising properties and provide proof of concept, the field of quantum computing is still looking for a quantum defect with several desired attributes, such as the ability to retain a quantum state for a long period of time and being easily controllable.
Depiction of a carbon defect complex in silicon. The silicon atoms are in blue, carbon is in brown, and the yellow surface indicates the computed localization of electrons for a certain excited state of the defect. (Image by Geoffroy Hautier)
Previously, quantum defects have been identified on a case-by-case basis, but with the DOE funding, the researchers will use high-throughput computing to accelerate the search for these defects, build a database, and then experiment with and further test the most promising materials. As the database grows, the researchers intend to use machine learning to additionally quicken the screening process.
"I am grateful to the Department of Energy for the financial support. Tightly combining theoretical and experimental work is key for this project, and I am extremely excited to start to work soon with such a strong and complementary team," said Hautier.
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