• Research at Sandia National Laboratories may help shape the future of quantum computing. Credit: TheDigitalArtist/Pixabay
     Research at Sandia National Laboratories may help shape the future of quantum computing. Credit: TheDigitalArtist/Pixabay

Shaping the Quantum Computing Future

January 7, 2019
Posted by George I. Seffers
E-mail About the Author

Sandia National Labs announces four quantum research projects.

Four newly announced projects led by Sandia National Laboratories aim to advance quantum computing technology, according to an announcement from the laboratories.

The efforts include: a quantum computing testbed with accessible components on which industrial, academic and government researchers can run their own algorithms; a suite of test programs to measure the performance of quantum hardware; classical software to ensure reliable operation of quantum computing testbeds and coax the most utility from them; and high-level quantum algorithms that explore connections with theoretical physics, classical optimization and machine learning.

These three- to five-year projects are funded at $42 million by the Department of Energy’s (DOE's) Office of Science’s Advanced Scientific Computing Research program, part of Sandia’s Advanced Science and Technology portfolio.

Design and construction of the quantum computer itself—formally known as the Quantum Scientific Computing Open User Testbed (QSCOUT)—under the direction of Sandia researcher Peter Maunz, is a $25.1 million, five-year project that will use trapped atomic ion technology. Researchers consider trapped ions promising because quantum bits are encoded in the electronic states of individual trapped atomic ions.

QSCOUT intends to make a trapped-ion quantum computer accessible to the DOE scientific community. Because today’s quantum computers only have access to a limited number of qubits and their operation is still subject to errors, these devices cannot yet solve scientific problems beyond the reach of classical computers. Nevertheless, access to prototype quantum processors such as QSCOUT should allow researchers to optimize existing quantum algorithms, invent new ones and assess the power of quantum computing to solve complex scientific problems.

Additionally, a Sandia team led by quantum researcher Robin Blume-Kohout is developing a toolbox of methods to measure the performance of quantum computers in real-world situations. The $3.7 million, five-year Quantum Performance Assessment project plans to develop a broad array of tiny quantum software programs. These range from simple routines to testbed-sized instances of real quantum algorithms for chemistry or machine learning that can be run on almost any quantum processor.

Once the computer is built by Maunz’s group and its reliability ascertained by Blume-Kohout’s team, the, $7.8 million, four-year Optimization, Verification and Engineered Reliability of Quantum Computers project aims to determine how the technology can best be used for computational tasks. Los Alamos National Laboratory (LANL) and Dartmouth College are partners on the project, which will develop classical middleware aimed at making computational use of the QSCOUT testbed and similar near-term quantum computers.

At the most theoretical level, the year-old, Sandia-led Quantum Optimization and Learning and Simulation (QOALAS) project’s team of theoretical physicists and computer scientists, headed by researcher Ojas Parekh, have produced a new quantum algorithm for solving linear systems of equations—one of the most fundamental and ubiquitous challenges facing science and engineering. The three-year, $4.5 million project, in addition to Sandia, includes LANL, the University of Maryland and Caltech.

“Our quantum linear systems algorithm, created at LANL, has the potential to provide an exponential speedup over classical algorithms in certain settings,” Parekh says in the written announcement. “Although similar quantum algorithms were already known for solving linear systems, ours is much simpler."

The team is working on other quantum algorithms that may offer an exponential speedup over the best-known classical algorithms.

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