Sandia Labs is revealing the inner workings of quantum computers
A precision diagnostic developed at the Department of Energy’s Sandia National Laboratories is emerging as a gold standard for detecting and describing problems inside quantum computing hardware.
Two papers published in the scientific journal Nature describe how separate research teams — one including Sandia researchers — used a Sandia technique called gate set tomography to develop and validate highly reliable quantum processors. Sandia has been developing gate set tomography since 2012, with funding from the DOE Office of Science through the Advanced Scientific Computing Research program.
Sandia scientists collaborated with Australian researchers at the University of New South Wales in Sydney, led by Professor Andrea Morello, to publish one of today’s papers. Together, they used GST to show that a sophisticated, three-qubit system comprising two atomic nuclei and one electron in a silicon chip could be manipulated reliably with 99%-plus accuracy.
In another Nature article appearing today, a group led by Professor Lieven Vandersypen at Delft University of Technology in the Netherlands used gate set tomography, implemented using Sandia software, to demonstrate the important milestone of 99%-plus accuracy but with a different approach, controlling electrons trapped within quantum dots instead of isolated atomic nuclei.
“We want researchers everywhere to know they have access to a powerful, cutting-edge tool that will help them make their breakthroughs,” said Sandia scientist Robin Blume-Kohout.
Gate set tomography is Sandia’s flagship technique for measuring the performance of qubits and quantum logic operations, also known as “gates.” It combines results from many kinds of measurements to generate a detailed report describing every error occurring in the qubits. Experimental scientists like Morello can use the diagnostic results to deduce what they need to fix.
The Sandia team maintains a free, open-source GST software called pyGSTi (pronounced “pigsty,” which stands for Python Gate Set Tomography Implementation). Publicly available at http://www.pygsti.info, it was used by both research groups publishing in Nature today.
While the Delft team used the pyGSTi software without assistance from the Sandia team, the UNSW-Sandia collaboration used a new, customized form of gate set tomography developed by the Sandia researchers. The new techniques enabled the team to rule out more potential error modes and focus on a few dominant error mechanisms