U.S. National Science Foundation Funding University of Texas to Close the Gap Between Quantum Hardware and Simulations

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NSF-Funded Project Aims to Squeeze Larger Simulations onto Quantum Computers

Key Points…

+  With new funding from the National Science Foundation, an academic-industry collaboration led by The University of Texas at Austin aims to close the gap between current quantum hardware and these ambitious simulations. They are developing a new suite of quantum algorithm methods and software tools that more efficiently use qubits — the basic units of information in a quantum computer, analogous to bits in a conventional computer. They plan to test their approach on trapped ion quantum computing systems being developed at Honeywell Quantum Solutions.

“It’s getting around having a very limited quantum memory to work with,” Potter said. “It’s a way to simulate systems that are too big to fit directly on your quantum memory all at once.”


+  The NSF grant is approximately $1 Million and is made through the Convergence Accelerator program, a new initiative designed to accelerate the transition of basic research and discovery into practical applications that address wide-scale societal challenges. The NSF has selected 29 teams addressing two transformative research areas: artificial intelligence and quantum technology.

+  Each team begins a nine-month phase one project developing their concept, participating in innovation curriculum and developing an initial prototype. At the end of phase one, each team participates in a pitch competition and a proposal evaluation. Selected teams from phase one will proceed to phase two, with potential funding up to $5 Million for two years.

+  The researchers will also use Lonestar5 — a petascale, high performance computing system operated by the Texas Advanced Computing Centerfor use by academic researchers in Austin and across Texas — to model how the new algorithms will behave on Honeywell’s quantum computer.

+  Their approach, called mid-circuit measurement and reset (MCMR), measures qubits in the middle of a model run and then resets them and reuses them, effectively expanding the number of qubits available in the system.

Source:  University of Texas at Austin.  Marc G Airhart,  NSF-Funded Project Aims to Squeeze Larger Simulations onto Quantum Computers…

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