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A sharper eye on ions

Artist's conception of an the imaging system and ion. A version of this image is featured on the cover of the September issue of Nature Photonics. Credit S. Kelley, JQI 

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.

Image formation depends on the way light coming from an object is collected and processed. For instance, the cornea and lens focus light waves as they enter the eye, forming an image on the retina. The clarity of this snapshot depends on the quality of the light. Objects appear blurry if the lenses bend light waves too much or too little, something we correct for by using glasses or contacts. Even with these errors, the brain excels at using contextual cues to analyze an image and grasp its concept. The idea of adaptive optics is similar to corrective eyewear.

To see a single ion, researchers must collect light from a lone, point-like object hovering inside a vacuum chamber. A laser illuminates the ion, causing it to emit light, which is then collected on a CCD camera, analogous to the retina. But first, the light passes through a vacuum window, two stages of optical magnification, and lenses that correct for astigmatism, all of which introduce distortion. This set of optics, called a microscope objective, combine with a camera to form the effective eye of the system.  A computer acts as a brain, processing the camera signal.

The researchers characterized the way an ion emitted light by fitting combinations of mathematical curves to the collected data. From these fits, they could determine how to manually adjust the microscope objective’s position to obtain a cleaner, sharper image. Notably, this imaging system was able to detect ion movements of mere nanometers--changes more than 1000 times smaller than the size of a red blood cell.

Beyond optimized ion pictures, the team plans to use this sensitive imaging system to measure quantum superpositions of two different motional states of a single ion. And the method, although applied here to atomic physics, could be translated to biology and astronomy, where point-like light sources are also common.

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

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 ( 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 ( 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 15, 2016

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:

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