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Machine learning in a quantum world

A rendering of a quantum agent exploring a maze, one of the problems addressed by a new framework for quantum machine learning. (Credit: E. Edwards/JQI)

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, in collaboration 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.

Many machine learning problems involve an agent—the program or device that is trying to learn—and an environment—the world that an agent explores as it learns. The machine learning community has paid particular attention to reinforcement learning, in which an agent interacts with its environment and learns how to behave through rewards and punishments. But such problems have not been widely studied when the agent and the environment obey the rules of quantum physics.

The new work provides a Rosetta Stone that translates the language of reinforcement learning to the quantum realm. It tackles sticky questions like what it means for a quantum agent to learn and how the history of a quantum agent’s interaction with its environment can be captured in a meaningful way. It also shows how a standard algorithm in the quantum toolkit can help agents learn faster in settings where an early stroke of luck can make a big difference—like when learning how to navigate a maze. 

Future research could investigate whether a quantum computer, with the added help of a quantum agent, could learn about its own noisy environment fast enough to change the way it reacts to errors. The work may also shed light on one of the deepest questions in physics: How does the everyday world arise from interactions that are, at the microscopic level, described by quantum mechanics? Studying how quantum agents interact with their quantum environments could add to the understanding of this lingering mystery.

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.

Recent Quantum Bits

October 17, 2016

Check out the second half of our feature story on Weyl semimetals and Weyl fermions, new materials and particles that have become a major focus for condensed matter researchers around the world. Part two looks at some of the theoretical work going on at JQI and CMTC. If you missed part one, it's not too late to catch up on the series. And if you missed our roundup of the research that led to last week's Nobel Prize in Physicsresearch that is closely related to Weyl materialswe encourage you to take a look.

JQI is also happy to congratulate Karina Jiménez-García on receiving a 2016 L'Oréal-UNESCO For Women in Science fellowship. "This is a recognition that I owe to all those that have guided and inspired me and those who have supported me throughout my professional career, especially my family," Jiménez-García said. We wrote a short story on how she plans to use the fellowship funds. It links to stories about the research she worked on while visiting JQI.

October 6, 2016

This year's Nobel Prize in Physics was awarded to three researchers who helped bring topology into physics. It's an innovation that has propelled condensed matter physics for the past three decades, leading recently to the discovery of several exotic materials.

We put together a roundup (http://jqi.umd.edu/physics-nobel-topological-exotic-matter) of the research that led to the prize and offered our take on topology. (Yes, we resorted to pastries.)

This year's prize is timely, too, as today we published part one (http://jqi.umd.edu/news/warm-welcome-weyl-physics) of a two-part series on Weyl semimetals, topological materials with a long history. That history is due, in part, to this year's laureates: David Thouless, Duncan Haldane and Michael Kosterlitz.

Part one focuses on the history and basic physics of Weyl materials. Part two, which will appear next week, focuses on some of the research being explored by physicists at JQI and the Condensed Matter Theory Center at the University of Maryland.

September 6, 2016

Optical systems, like your eye, sometimes need help to produce a crystal clear image. And it’s not just a problem for eyes. Research labs, too, worry about aberrations and distortions that lead to image inaccuracies. JQI physicists have implemented a novel imaging technique that adapts to these destructive errors and corrects them. They combine high performance lenses, akin to an artificial eye, with computer processing to capture an image of a single atomic ion and its motion with unprecedented nanoscale sensitivity. The research is featured on the cover of the September issue of Nature Photonics.

High-resolution adaptive imaging of a single atom J. D. Wong-Campos, K. G. Johnson, B. Neyenhuis, J. Mizrahi & C. Monroe, Nature Photonics doi:10.1038/nphoton.2016.136

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