Items tagged with "machine learning"
Researchers at the University of Maryland (UMD) have trained a small hybrid quantum computer to reproduce the features in a particular set of images.
In many situations, chaos makes it nearly impossible to predict what will happen next. Nowhere is this more apparent than in weather forecasts, which are notorious for their unreliability. But the clever application of artificial intelligence can help reign in some chaotic systems, making them more predictable than ever before.
From self-driving cars and IBM’s Watson to chess engines and AlphaGo, there is no shortage of news about machine learning, the field of artificial intelligence that studies how to make computers that can learn. Recently, parallel to these advances, scientists have started to ask how quantum devices and techniques might aid machine learning in the future.
To date, much research in the emerging field of quantum machine learning has attacked choke points in ordinary machine learning tasks, focusing, for example, on how to use quantum computers to speed up image recognition. But Vedran Dunjko and Hans Briegel at the University of Innsbruck, along with JQI Fellow Jake Taylor, have taken a broader view. Rather than focusing on speeding up subroutines for specific tasks, the researchers have introduced an approach to quantum machine learning that unifies much of the prior work and extends it to problems that received little attention before. They also showed how to increase learning performance for a large group of problems. The research has been accepted for publication in Physical Review Letters.
Quantum-enhanced machine learning. V. Dunjko, J. M. Taylor and H. J. Briegel, Physical Review Letters, to appear. arXiv: http://arxiv.org/abs/1507.08482.