Machine-learning algorithms have been run on a quantum computer by physicists at IBM. Although the proof-of-concept demonstration did not involve practical tasks, the team hopes that scaling-up the algorithms to run on larger quantum systems could give machine learning a boost.
Machine learning is a type of artificial intelligence that involves a computer working-out how to do a task by analysing large numbers of examples of the task being done. A typical task could be to tell the difference between photographs of cats and dogs. The machine learning system would be “trained” by inputting lots of images of cats and dogs and the system would create a mathematical model that has a clear boundary between cats and dogs.
Many machine learning algorithms are “kernel methods”, which determine similarities between patterns. The strategy is to transform the data – pixels in a digital image, for example – into a higher-dimensional representation that has clear boundaries between classification types. All images of cats, for example, would reside in one region of this higher-dimensional, space whereas all images of dogs reside in another.
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