Moore’s Law just got torn up and jumped on by the future of quantum computing, as Google in partnership with NASA and the quantum computing firm D-Wave, announced that they’ve been clocking up like-for-like speeds on their quantum box 100 million-times faster than their best traditional supercomputer.

Or to put it in slightly more technical terms, Hartmut Neven, Google’s director of Engineering says, “We found that for problem instances involving nearly 1000 binary variables, quantum annealing significantly outperforms its classical counterpart, simulated annealing.”

When they ran a Quantum Monte Carlo simulation (a decent bench-test for super computer power-lifting), they found their quantum-based system ran 100 million times faster.

It’s early days, but very exciting. Think of the rendering power in games one of those bad boys would give you!

Hartmut went on to say, “While these results are intriguing and very encouraging, there is more work ahead to turn quantum enhanced optimization into a practical technology. The design of next generation annealers must facilitate the embedding of problems of practical relevance. For instance, we would like to increase the density and control precision of the connections between the qubits as well as their coherence. Another enhancement we wish to engineer is to support the representation not only of quadratic optimization, but of higher order optimization as well. This necessitates that not only pairs of qubits can interact directly but also larger sets of qubits. Our quantum hardware group is working on these improvements which will make it easier for users to input hard optimization problems. For higher-order optimization problems, rugged energy landscapes will become typical. Problems with such landscapes stand to benefit from quantum optimization because quantum tunnelling makes it easier to traverse tall and narrow energy barriers.”

“We should note that there are algorithms, such as techniques based on cluster finding, that can exploit the sparse qubit connectivity in the current generation of D-Wave processors and still solve our proof-of-principle problems faster than the current quantum hardware. But due to the denser connectivity of next generation annealers, we expect those methods will become ineffective. Also, in our experience we find that lean stochastic local search techniques such as simulated annealing are often the most competitive for hard problems with little structure to exploit. Therefore, we regard simulated annealing as a generic classical competition that quantum annealing needs to beat. We are optimistic that the significant runtime gains we have found will carry over to commercially relevant problems as they occur in tasks relevant to machine intelligence.”

The results are reported in a scientific paper over here.