Tag Archives: 1097-qubit D-Wave 2X™ quantum computer

Google announces research results after testing 1,097-qubit D-Wave 2X™ quantum computers

If you’ve been reading this blog over the last few months, you’ll know that I’ve mentioned D-Wave Systems, a Vancouver (Canada)-based quantum computing company, frequently. The company seems to be signing all kinds of deals lately including one with Google (my Oct. 5, 2015 posting). Well, a Dec. 9, 2015 news item on Nanotechnology Now sheds more light on how Google is using D-Wave’s quantum computers,

Harris & Harris Group, Inc. (NASDAQ: TINY), an investor in transformative companies enabled by disruptive science, notes that yesterday [Dec. 8, 2015] NASA, Google and the Universities Space Research Association (USRA) hosted a tour of the jointly run Quantum Artificial Intelligence Laboratory located at the NASA’s Ames Research Center which houses one of D-Wave’s 1,097-qubit D-Wave 2X™ quantum computers. At this event, Google announced that D-Wave’s quantum computer was able to find solutions to complicated problems of nearly 1,000 variables up to 108 (100,000,000) times faster than classical computers.

A Dec. 8, 2015 posting by Hartmut Neven for the Google Research blog describes the research and the results (Note: Links have been removed),

During the last two years, the Google Quantum AI [artificial intelligence] team has made progress in understanding the physics governing quantum annealers. We recently applied these new insights to construct proof-of-principle optimization problems and programmed these into the D-Wave 2X quantum annealer that Google operates jointly with NASA. The problems were designed to demonstrate that quantum annealing can offer runtime advantages for hard optimization problems characterized by rugged energy landscapes. We found that for problem instances involving nearly 1000 binary variables, quantum annealing significantly outperforms its classical counterpart, simulated annealing. It is more than 108 times faster than simulated annealing running on a single core. We also compared the quantum hardware to another algorithm called 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 108.

For anyone (like me) who needs an explanation of quantum annealing, there’s this from its Wikipedia entry (Note: Links have been removed),

Quantum annealing (QA) is a metaheuristic for finding the global minimum of a given objective function over a given set of candidate solutions (candidate states), by a process using quantum fluctuations. Quantum annealing is used mainly for problems where the search space is discrete (combinatorial optimization problems) with many local minima; such as finding the ground state of a spin glass.[1] It was formulated in its present form by T. Kadowaki and H. Nishimori in “Quantum annealing in the transverse Ising model”[2] though a proposal in a different form had been proposed by A. B. Finilla, M. A. Gomez, C. Sebenik and J. D. Doll, in “Quantum annealing: A new method for minimizing multidimensional functions”.[3]

Not as helpful as I’d hoped but sometimes its necessary to learn a new vocabulary and a new set of basic principles, which takes time and requires the ability to ‘not know’ and/or ‘not understand’ until one day, you do.

In the meantime, here’s more possibly befuddling information from the researchers in the form of a paper on arXiv.org,

What is the Computational Value of Finite Range Tunneling? by Vasil S. Denchev, Sergio Boixo, Sergei V. Isakov, Nan Ding, Ryan Babbush, Vadim Smelyanskiy, John Martinis, Hartmut Neven. http://arxiv.org/abs/1512.02206

This paper is open access.