Dwave systems has produced a 128 qubit quantum computer called Dwave One that uses quantum annealing to solve problems. They sold a system to Lockheed Martin for $10 million including support services.
Applications for Dwave One –
D-Wave One is the first in a line of products that help people begin to incorporate quantum computing into the way that they solve problems. D-Wave’s systems are best at solving the types of problems found in the fields of optimization and machine learning. These problems can be broadly described as data analysis and pattern recognition problems.
The D-Wave One allows users to experiment with algorithms and APIs we have developed at D-Wave or to implement their own learning algorithms to explore ways to attack these tasks.Being quantum mechanical in nature, The D-Wave One machine is also excellent at simulating quantum mechanical systems of interest to materials scientists, physicists and chemists. As such they may also prove extremely useful as academic research tools.
What is the benefit of using quantum annealing as a computation method over quantum search or quantum factoring?
Quantum annealing can be applied to a much broader range of problems than the more specialized algorithms such as factoring or unstructured search. Quantum annealing is a method of solving optimization problems, and once you start looking, you find these problems in almost every discipline and walk of life – genetics, finance, machine translation, bioinformatics, medical diagnosis, to name just a few.
Quantum annealing is also a much more natural way of running a quantum algorithm. In quantum annealing, the qubits always remain in what is known as the ‘ground state’. This is the configuration that the system naturally wants to be in, (in the same way that water will run downstream and find its own level). Many other algorithms – such as factoring – require the qubits to be maintained in highly unstable excited states, which make it extremely difficult to control them precisely enough to perform even a small factoring computation.
* The qubits in Rainier are among the “quietest” superconducting qubits ever built.
* D-Wave’s systems are getting bigger and more powerful all the time
* The hardware is a natural fit to optimization problems that can be found at the heart of many machine learning areas, and so D-Wave is taking on the grand challenge of writing prototype applications to demonstrate the power of quantum computing in these areas. Examples of such challenges might be using machine learning to discover correlations between genetic data and a person’s traits or health in order to help improve medical diagnosis, or detecting subtle meaning in text so that a computer program could truly ‘understand’ articles that it read online.