One company that actually has had the bravado to claim such a demonstration is Canadian firm D-Wave Systems, Inc. which is based out of Barnaby, British Columbia.  The company has developed what it claims to be working 16-qubit, 28-qubit, and 128-qubit quantum computer chips.  Each qubit is implemented with a magnetically coupling superconducting loop called rf-squid flux.  The company has fabricated some of these chips at NASA's Jet Propulsion Lab’s microdevices lab in Pasadena and NASA scientists who saw the work firsthand back its credibility, despite widespread doubts in the research community.

Now D-Wave has received an even bigger endorsement, from the world's largest internet firm: Google.  Google manager Hartmut Neven announced in a blog post last week that his company had been working with D-Wave to develop quantum computers to power a search of still images in a database of images, video, and PDFs.  The project has been ongoing for three years according to Mr. Neven.

He writes, elaborating, "Over the past three years a team at Google has studied how problems such as recognizing an object in an image or learning to make an optimal decision based on example data can be made amenable to solution by quantum algorithms. The algorithms we employ are the quantum adiabatic algorithms discovered by Edward Farhi and collaborators at MIT. These algorithms promise to find higher quality solutions for optimization problems than obtainable with classical solvers."

The announcement corresponded with the first demonstration of the fruits of the partnership.  At the Neural Information Processing Systems conference (NIPS 2009), Google showed off a working search that could locate images of cars in the database almost instantly after being first trained images of what a car looked like.  The search training was powered by D-Wave's new C4 Chimera chip and used the quantum adiabatic image algorithms.

In the search, Google first took 20,000 photographs -- half with cars in them and without.  In each picture they drew boxes around the cars (if there were any), identifying the "car" graphic element.  Next Google took a second set of 20,000 photos -- half with cars and half without.  They then put the second set to the quantum trained algorithm, which identified the cars faster than any traditional algorithm in Google's data farms.

Google was quite enthusiastic about the results.  Writes Mr. Neven, "There are still many open questions but in our experiments we observed that this detector performs better than those we had trained using classical solvers running on the computers we have in our data centers today. Besides progress in engineering synthetic intelligence we hope that improved mastery of quantum computing will also increase our appreciation for the structure of reality as described by the laws of quantum physics."

Could quantum computing be Google's trump card to keep down a resurgent Microsoft, which has been invigorated by its partnerships with Yahoo and Wolfram Alpha?  If the technology is as good as Google claims, the only real question seems to be how long it will take Google to make its deployment affordable.  D-Wave's past designs were complex beasts that needed to be chilled to almost 0 degrees Kelvin to operate properly. Still, Google and D-Wave have both come a long way in terms of quantum hardware and software, so we may not have to wait too long for quantum-computer-driven searches.

In next generation data centers, Google will likely use a mix of quantum computers alongside traditional von Neumann architecture servers.  This would allow the systems to serve diverse requests and use the best tool for the job for each search.

To get a taste of how Google's new search works and the mechanics that could drive the company's next-gen datacenters, read its conference paper, entitled, "NIPS 2009 Demonstration: Binary Classification using Hardware Implementation of Quantum Annealing" (PDF).