SoylentNews

takyon writes:

Google Research Blog has reported a 100 million times (10⁸) speedup when using D-Wave quantum annealing systems to solve certain optimization problems versus a single classical processor core. Note that D-Wave systems are said to cost $10 million, far more than a single core:

During the last two years, the Google Quantum AI 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.

[More after the break.]

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.

From the arXiv paper "What is the Computational Value of Finite Range Tunneling?":

To illustrate how dramatic this effect can be, when we ran smaller instances of the weak-strong cluster networks on the older D-Wave Vesuvius chips we predicted that at 1000 variables DWave would be 10⁴ times faster than [simulated annealing]. In fact, we observed a speedup of more than a factor of 10⁸.

Update: Experts, including longtime D-Wave critic Scott Aaronson, are not impressed with Google's claims. Perhaps Rose's Law will take care of that. Via NextBigFuture.

Original Submission