Google Reports Progress In Quantum Computing
Earlier this week, Google showed off its work on D-Wave?s 2X quantum annealer computer at NASA?s Ames Research Center, claiming that it's millions of times faster than a traditional computer.
The computer Google is working on is made by Canadian D-Wave Systems Inc. It is actually a primitive type of "quantum" computer that uses different technology than classical computers. Existing machines rely on bits representing ones and zeros to complete tasks. In contrast, a quantum bit, or "qubit," pushes the boundaries of physics by representing a one and a zero at the same time. Many qubits working together at the same time would in theory create a computer that performs some calculations exponentially faster.
Google says that the quantum annealer computer solved a problem 100 million times faster than a traditional computer.
"It is more than 108 times faster than simulated annealing running on a single core," Google said.
The search giant also compared the quantum hardware to an 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 were again separated by a large factor sometimes as high as 108, according to Google.
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, Google's researchers would like to increase the density and control precision of the connections between the qubits as well as their coherence.
Another enhancement would be 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.
For higher-order optimization problems, rugged energy landscapes will become typical. Problems with such landscapes stand to benefit from quantum optimization because quantum tunneling makes it easier to traverse tall and narrow energy barriers.
Also, in Google's experience, the researchers found that lean stochastic local search techniques such as simulated annealing were often the most competitive for hard problems with little structure to exploit. Therefore, simulated annealing is regarded 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," Google added.
Google thinks this type of quantum computing will first be useful in machine learning, a field of artificial intelligence that uses data to teach computers to be smarter.
Google says that the quantum annealer computer solved a problem 100 million times faster than a traditional computer.
"It is more than 108 times faster than simulated annealing running on a single core," Google said.
The search giant also compared the quantum hardware to an 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 were again separated by a large factor sometimes as high as 108, according to Google.
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, Google's researchers would like to increase the density and control precision of the connections between the qubits as well as their coherence.
Another enhancement would be 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.
For higher-order optimization problems, rugged energy landscapes will become typical. Problems with such landscapes stand to benefit from quantum optimization because quantum tunneling makes it easier to traverse tall and narrow energy barriers.
Also, in Google's experience, the researchers found that lean stochastic local search techniques such as simulated annealing were often the most competitive for hard problems with little structure to exploit. Therefore, simulated annealing is regarded 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," Google added.
Google thinks this type of quantum computing will first be useful in machine learning, a field of artificial intelligence that uses data to teach computers to be smarter.
