QuAIL project announces advances in quantum tunneling achieved on D-Wave 2X

NASA welcomed members of the news media on December 8 to tour its Quantum Artificial Intelligence Laboratory (QuAIL) at Ames Research Center, providing attendees a rare glimpse inside the facility that houses a 1,097-qubit D-Wave 2X quantum computer. Researchers on NASA’s QuAIL team are using the system to investigate areas where quantum algorithms might someday dramatically improve the agency’s ability to solve difficult optimization problems in aeronautics, Earth and space sciences and space exploration.

The QuAIL project is a collaborative effort among NASA, Google and the Universities Space Research Association. Hartmut Neven, director of Engineering at Google was on-hand, and took advantage of the event to announce the group’s advances in quantum annealing. “We found that for problem instances involving nearly 1000 binary variables, quantum annealing significantly outperforms its classical counterpart, simulated annealing,” Neven said in a blog post related to the event. “It is more than 10^8 times faster than simulated annealing running on a single core.”

The Google Quantum AI team also tested against quantum Monte Carlo. “This is a method designed to emulate the behavior of quantum systems, but it runs on conventional processors. While the scaling with size between these two methods is comparable, they are again separated by a large factor sometimes as high as 10^8,” said Neven.

He pulled these results from a proof of concept paper published the day of the event. The document both trumpets the advances made by D-Wave and the QuAiL project and tempers immediate expectations. “More work is needed to turn quantum enhanced optimization into a practical technology,” the researchers conclude. In the next generation of quantum annealers the team would like to “to increase the density and control precision of the connections between the qubits” and to engineer support for “the representation not only of quadratic optimization but of higher order optimization as well.”