Tiny magnetic tremors unlock exotic superconductivity

tiny magneticArtistic rendering of exotic 2D superconductivity in a material made from nanolayers of nickel (bottom layer) and bismuth (top layer). Magnetic fluctuations from the nickel layer allow electrons to pair up on the surface of bismuth. These pairs move losslessly in a phenomenon called superconductivity. (Credit E. Edwards; transmission electron microscopy images from the real materials are projected onto the cube layers. These images were re-used for this graphic with author permission)

Deep within solids, individual electrons zip around on a nanoscale highway paved with atoms. For the most part, these electrons avoid one another, kept in separate lanes by their mutual repulsion. But vibrations in the atomic road can blur their lanes and sometimes allow the tiny particles to pair up. The result is smooth and lossless travel, and it’s one way to create superconductivity.

But there are other, less common ways to achieve this effect. Scientists from the University of Maryland (UMD), the University of California, Irvine (UCI) and Fudan University have now shown that tiny magnetic tremors lead to superconductivity in a material made from metallic nano-layers. And, beyond that, the resulting electron pairs shatter a fundamental symmetry between past and future. Although the material is a known superconductor, these researchers provide a theoretical model and measurement, which, for the first time, unambiguously reveals the material’s exotic nature.

In quantum materials, breaking the symmetry between the past and the future often signifies unconventional phases of matter. The nickel-bismuth (Ni-Bi) sample studied here is the first example of a 2D material where this type of superconductivity is intrinsic, meaning that it happens without the help of external agents, such as a nearby superconductor. These findings, recently published in Science Advances, make Ni-Bi an appealing choice for use in future quantum computers. This research may also assist scientists in their search for other similarly strange superconductors.

Mehdi Kargarian*, a postdoctoral researcher at UMD and a co-author of the paper, explains that even after a century of study, superconductivity remains a vibrant area of research. “It is a rather old problem, so it is surprising that people are still discovering types of superconductivity in the lab that are unprecedented,” Kargarian says, adding that there are typically two questions scientists ask of a new superconductor. “First, we want to understand the underlying electron pairing—what is causing the superconductivity,” he says. “The second thing, related to applications, is to see if superconductivity is possible at higher temperatures.”

Superconductors, particularly the exotic types, largely remain shackled to unwieldy cryogenic equipment. Scientists are searching for ways to push superconducting temperatures higher, thus making these materials easier to use for things like improved electricity distribution and building quantum devices. In this new research, the team tackles Kargarian’s first question and the material hints at a positive outlook for the second question. Its exotic superconductivity, although still cryogenic, occurs at a higher temperature compared to other similar systems.

Ni-Bi superconductivity was first observed in the early 1990s. But later, when Fudan University scientists published studies of an ultrapure, ultra-thin sample, they noticed something unusual happening.

The strangeness starts with the superconductivity itself. Bismuth alone is not a superconductor, except under extraordinarily low temperatures and high pressure—conditions that are not easy to achieve. Nickel is magnetic and not a superconductor. In fact, strong magnets are known to suppress the effect. This means that too much nickel destroys the superconductivity, but a small amount induces it.

UMD theorists* proposed that fluctuations in nickel’s magnetism are at the heart of this peculiar effect. These tiny magnetic tremors help electrons to form pairs, thus doing the work performed by vibrations in conventional superconductors. If there is too much nickel, magnetism dominates and the effect of the fluctuations diminishes. If there is too much bismuth, then the top surface, where superconductivity takes place, is too far away from the source of magnetic fluctuations.

The goldilocks zone occurs when a twenty-nanometer-thick bismuth layer is grown on top of two nanometers of nickel. For this layer combination, superconductivity happens at around 4 degrees above absolute zero. While this is about as cold as deep space, it is actually quite lab-friendly and reachable using standard cryogenic equipment.

The idea that magnetic fluctuations can promote superconductivity is not new and dates back to the end of the 20th century. However, most earlier examples of such behavior require strict operating conditions, such as high pressure. The researchers explain that Ni-Bi is different because straightforward cooling is enough to achieve this type of exotic superconductivity, which breaks time symmetry.

The researchers employed a highly customized apparatus to search for signs of the broken symmetry. Light should rotate when reflected from samples that have this property. For Ni-Bi, the expected amount of light rotation is tens of nanoradians, which is about 100 billionths of a tick on a watch face. Jing Xia*, a co-author of the paper and a professor at UCI, has one of the only devices in the world capable of measuring such an imperceptible light rotation.

In order to measure this rotation for Ni-Bi, light waves are first injected into one end of a single special-purpose optical fiber. The two waves travel through the fiber, as if on independent paths. They hit the sample and then retrace their paths. Upon return, the waves are combined and form a pattern. Rotations of the light waves—from, say, symmetry breaking—will show up in the analyzed pattern as small translations. Xia and his colleagues at UCI measured around 100 nanoradians of rotation, confirming the broken symmetry. Importantly, the effect appeared just as the Ni-Bi sample became a superconductor, suggesting that the broken time symmetry and the appearance of superconductivity are strongly linked.

This form of superconductivity is rare and researchers say that there is still no recipe for making it happen. But, as Xia points out, there is guidance in the math behind the electron behavior. “We know mathematically how to make electron pairs break time-reversal symmetry,” Xia says. Practically, how do you achieve this formulaically? That is the million-dollar question. But my instinct is that when you do get magnetic fluctuation-mediated superconductivity, like in this material, then it is highly likely you get break that symmetry.”

* M. Kargarian is a postdoctoral researcher at the Condensed Matter Theory Center (CMTC), University of Maryland (UMD) and is affiliated with the Joint Quantum Institute. V. Yakovenko and V. Galitski are members of CMTC, fellows of the Joint Quantum Institute, and UMD professors. J. Xia is a professor at the University of California, Irvine.

Written by E. Edwards/JQI

"Time-reversal symmetry-breaking superconductivity in epitaxial bismuth/nickel bilayers," X. Gong, M. Kargarian, A. Stern, D. Yue, H. Zhou, X. Jin, V.M. Galitski, V.M. Yakovenko, J. Xia, Science Advances, 3, (2017)


Topological Superconductors

Tiny tug unleashes cryogenic currents

Quantum Thermometer or Optical Refrigerator?

In an arranged marriage of optics and mechanics, physicists have created microscopic structural beams that have a variety of powerful uses when light strikes them. Able to operate in ordinary, room-temperature environments, yet exploiting some of the deepest principles of quantum physics, these optomechanical systems can act as inherently accurate thermometers, or conversely, as a type of optical shield that diverts heat. The research was performed by a team led by the Joint Quantum Institute (JQI) (link is external), a research collaboration of the National Institute of Standards and Technology (NIST) and the University of Maryland.

Neural take on quantum entanglement

An artist's rendering of a neural network with two layers. At the top is a real quantum system, like atoms in an optical lattice. Below is a network of hidden neurons that capture their interactions. (Credit: E. Edwards/JQI)

Machine learning, the field that’s driving a revolution in artificial intelligence, has cemented its role in modern technology. Its tools and techniques have led to rapid improvements in everything from self-driving cars and speech recognition to the digital mastery of an ancient board game.

Now, physicists are beginning to use machine learning tools to tackle a different kind of problem, one at the heart of quantum physics. In a paper published recently in Physical Review X, researchers from JQI and the Condensed Matter Theory Center (CMTC) at the University of Maryland showed that certain neural networks—abstract webs that pass information from node to node like neurons in the brain—can succinctly describe wide swathes of quantum systems.

Dongling Deng, a JQI Postdoctoral Fellow who is a member of CMTC and the paper’s first author, says that researchers who use computers to study quantum systems might benefit from the simple descriptions that neural networks provide. “If we want to numerically tackle some quantum problem,” Deng says, “we first need to find an efficient representation.”

On paper and, more importantly, on computers, physicists have many ways of representing quantum systems. Typically these representations comprise lists of numbers describing the likelihood that a system will be found in different quantum states. But it becomes difficult to extract properties or predictions from a digital description as the number of quantum particles grows, and the prevailing wisdom has been that entanglement—an exotic quantum connection between particles—plays a key role in thwarting simple representations.

The neural networks used by Deng and his collaborators—CMTC Director and JQI Fellow Sankar Das Sarma and Fudan University physicist and former JQI Postdoctoral Fellow Xiaopeng Li—can efficiently represent quantum systems that harbor lots of entanglement, a surprising improvement over prior methods.

What’s more, the new results go beyond mere representation. “This research is unique in that it does not just provide an efficient representation of highly entangled quantum states,” Das Sarma says. “It is a new way of solving intractable, interacting quantum many-body problems that uses machine learning tools to find exact solutions.”

Neural networks and their accompanying learning techniques powered AlphaGo, the computer program that beat some of the world’s best Go players last year (and the top player this year. The news excited Deng, an avid fan of the board game. Last year, around the same time as AlphaGo’s triumphs, a paper appeared that introduced the idea of using neural networks to represent quantum states, although it gave no indication of exactly how wide the tool’s reach might be. “We immediately recognized that this should be a very important paper,” Deng says, “so we put all our energy and time into studying the problem more.”

The result was a more complete account of the capabilities of certain neural networks to represent quantum states. In particular, the team studied neural networks that use two distinct groups of neurons. The first group, called the visible neurons, represents real quantum particles, like atoms in an optical lattice or ions in a chain. To account for interactions between particles, the researchers employed a second group of neurons—the hidden neurons—which link up with visible neurons. These links capture the physical interactions between real particles, and as long as the number of connections stays relatively small, the neural network description remains simple.

Specifying a number for each connection and mathematically forgetting the hidden neurons can produce a compact representation of many interesting quantum states, including states with topological characteristics and some with surprising amounts of entanglement.

Beyond its potential as a tool in numerical simulations, the new framework allowed Deng and collaborators to prove some mathematical facts about the families of quantum states represented by neural networks. For instance, neural networks with only short-range interactions—those in which each hidden neuron is only connected to a small cluster of visible neurons—have a strict limit on their total entanglement. This technical result, known as an area law, is a research pursuit of many condensed matter physicists.

These neural networks can’t capture everything, though. “They are a very restricted regime,” Deng says, adding that they don’t offer an efficient universal representation. If they did, they could be used to simulate a quantum computer with an ordinary computer, something physicists and computer scientists think is very unlikely. Still, the collection of states that they do represent efficiently, and the overlap of that collection with other representation methods, is an open problem that Deng says is ripe for further exploration.

By Chris Cesare, This email address is being protected from spambots. You need JavaScript enabled to view it. 

"Quantum Entanglement in Neural Network States," D.L. Deng, X. Li, D. Sarma, PHYSICAL REVIEW X, 7, (2017)
Dongling Deng | This email address is being protected from spambots. You need JavaScript enabled to view it.

Gravitational Waves Again Detected

LigoGW170104Dancing Duo of Black Holes (Credit: LIGO)

Scientists in the Laser Interferometer Gravitational-Wave Observatory (LIGO) collaboration now report the detection of a third gravitational wave event, named GW170104 and described in the July 1, 2017 Physical Review Letters. UMD's Peter Shawhan and Alessandra Buonanno are principal investigators in the experiment.

For a detailed account, see the CMNS story here.