In 2008 Scott Aaronson said that Dwave trying to line up customers for their quantum computer was comically premature

Scott Aaronson is a professor working on Quantum computer theory at MIT.

In 2008 (and before and after) he was an outspoken critic of Dwave Systems. D-Wave has built an adiabatic quantum computer and yesterday announced the sale of a 128 qubit quantum computer system to Lockheed. The deal with Lockheed Martin concluded in November of 2010. A bet that I made back in 2006 was accurate.

Even if D-Wave managed to build (say) a coherent 1,024-qubit machine satisfying all of its design specs, it’s not obvious it would outperform a classical computer on any problem of practical interest. This is true both because of the inherent limitations of the adiabatic algorithm, and because of specific concerns about the Ising spin graph problem. On the other hand, it’s also not obvious that such a machine wouldn’t outperform a classical computer on some practical problems. The experiment would be an interesting one! Of course, this uncertainty — combined with the more immediate uncertainties about whether D-Wave can build such a machine at all, and indeed, about whether they can even produce two-qubit entanglement — also means that any talk of “lining up customers” is comically premature.

Faster than classical computer for a practical problem

Dwave and Google proved superior results for recognize objects in images.

“D-Wave develops processors that realize the adiabatic quantum algorithm by magnetically coupling superconducting loops called rf-squid flux qubits,” wrote Neven. “This design realizes what is known as the Ising model which represents the simplest model for an interacting many-body system and it can be manufactured using proven chip fabrication methods.”

At the NIPS 2009 conference, Google demonstrated how its algorithm could recognize cars in images. First, the researchers trained the system by showing it 20,000 photographs, half of which contained cars that had boxes drawn around them, while the other half had no cars. After the training, the researchers presented the algorithm with 20,000 new photos, with half containing cars. The algorithm could recognize which images had cars significantly faster than the algorithms used by any of Google’s conventional computers.

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