Google, NASA add D-Wave 2X to quantum computing arsenal

Some companies need a few computers to go about their business, others need very fast mainframes and servers to function. And then there are others who need exponentially faster computing to run at peak efficiency. Google and NASA are two examples of those companies, which is why they are heavily invested in the still nascent field of quantum computing. Luckily for them, D-Wave, a leading manufacturer of quantum computing equipment, has just revealed the D-Wave 2X to cater to their, as well as others', needs and experiments in the field.

In terms of performance, the D-Wave 2X tries to give justice to its moniker, meaning it is advertised to be twice as powerful as its predecessor. It doubles the number of qubits, quantum computing equivalent of bits, from 512 to 1,000. It also operates at an even lower 15 millikelvin, which is to say extremely and bitterly cold.

D-Wave has been supplying the quantum computers that Google's Quantum Artificial Intelligence Lab, a contract that last seven years wherein it will supply Google with the latest models. Google then uses the computers for its own research but also "leases" computing power to its other partners. Google's primary interest is, of course, in machine learning and problem optimization.

Quantum computing is a relatively recent development in the field of computing and is now being contested by some experts. Presumed to be faster than traditional computing, some research refutes this with evidence showing there is significant performance advantage to regular computers.

Google and proponents of quantum computing, however, believe that it just isn't about speed but also about "creative problem solving". Whereas a traditional computer would solve a given problem in a more conventional, linear, and mechanical manner, quantum computing would look for other solutions that could lead to more efficient use of resources. And when you're a company faced with millions if not billions of queries each day, that is definitely an optimization you'd want to have at your disposal.

SOURCE: Popular Science