Aurelia Butler

In the quantum realm, electrons can group together to behave in interesting ways. Magnetism is one of these behaviors that we see in our day-to-day life, as is the rarer phenomena of superconductivity. Intriguingly, these two behaviors are often antagonists, meaning that the existence of one of them often destroys the other. However, if these two opposite quantum states are forced to coexist artificially, an elusive state called a topological superconductor appears, which is exciting for researchers trying to make topological qubits.
Topological qubits are exciting as one of the potential technologies for future quantum computers. In particular, topological qubits provide the basis for topological quantum computing, which is attractive because it is much less sensitive to interference from its surroundings from perturbing the measurements. However, designing and controlling topological qubits has remained a critically open problem, ultimately due to the difficulty of finding materials capable of hosting these states, such as topological superconductors.
To overcome the elusiveness of topological superconductors, which are remarkably hard to find in natural materials, physicists have developed methodologies to engineer these states by combining common materials. The basic ingredients to engineer topological superconductors — magnetism and superconductivity — often require combining dramatically different materials. What’s more, creating a topological superconducting material requires being able to finely tune the magnetism and superconductivity, so researchers have to prove that their material can be both magnetic and superconductive at the same time, and that they can control both properties. In their search for such a material, researchers have turned to graphene.
Graphene — a single layer of carbon atoms — represents a highly controllable and common material and has been raised as one of the critical materials for quantum technologies. However, the coexistence of magnetism and superconductivity has remained elusive in graphene, despite long-standing experimental efforts that demonstrated the existence of these two states independently. This fundamental limitation represents a critical obstacle towards the development of artificial topological superconductivity in graphene.
In a recent breakthrough experiment, researchers at the UAM in Spain, CNRS in France, and INL in Portugal, together with the theoretical support of Prof. Jose Lado at Aalto University, have demonstrated an initial step along a pathway towards topological qubits in graphene. The researchers demonstrated that single layers of graphene can host simultaneous magnetism and superconductivity, by measuring quantum excitations unique to this interplay. This breakthrough finding was accomplished by combining the magnetism of crystal domains in graphene, and the superconductivity of deposited metallic islands.
‘This experiment shows that two key paradigmatic quantum orders, superconductivity, and magnetism, can simultaneously coexist in graphene,’ said Professor Jose Lado, ‘Ultimately, this experiment demonstrates that graphene can simultaneously host the necessary ingredients for topological superconductivity. While in the current experiment we have not yet observed topological superconductivity, building on top of this experiment we can potentially open a new pathway towards carbon-based topological qubits.’
The researchers induced superconductivity in graphene by depositing an island of a conventional superconductor close to grain boundaries, naturally forming seams in the graphene which have a slightly different magnetic properties to the rest of the material. The superconductivity and grain boundary magnetism was demonstrated to give rise to Yu-Shiba-Rusinov states, which can only exists in a material when magnetism and superconductivity coexisting together. The phenomena the team observed in the experiment matched up with the theoretical model developed by Professor Lado, showing that the researchers can fully control the quantum phenomena in their designer hybrid system.
The demonstration of Yu-Shiba-Rusinov states in graphene is the first step towards the ultimate development of graphene-based topological qubits. In particular, by carefully controlling Yu-Shiba-Rusinov states, topological superconductivity and Majorana states can be created. Topological qubits based on Majorana states can potentially drastically overcome the limitations of current qubits, protecting quantum information by exploiting the nature of these unconventional states. The emergence of these states requires meticulous control of the system parameters. The current experiment establishes the critical starting point towards this goal, which can be built upon to hopefully open a disruptive road to carbon-based topological quantum computers.
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Researchers at the University of California, Riverside, have used a nanoscale synthetic antiferromagnet to control the interaction between magnons — research that could lead to faster and more energy-efficient computers.
In ferromagnets, electron spins point in the same direction. To make future computer technologies faster and more energy-efficient, spintronics research employs spin dynamics — fluctuations of the electron spins — to process information. Magnons, the quantum-mechanical units of spin fluctuations, interact with each other, leading to nonlinear features of the spin dynamics. Such nonlinearities play a central role in magnetic memory, spin torque oscillators, and many other spintronic applications.
For example, in the emergent field of magnetic neuromorphic networks — a technology that mimics the brain — nonlinearities are essential for tuning the response of magnetic neurons. Also, in another frontier area of research, nonlinear spin dynamics may become instrumental.
“We anticipate the concepts of quantum information and spintronics to consolidate in hybrid quantum systems,” said Igor Barsukov, an assistant professor at the Department of Physics & Astronomy who led the study that appears in Applied Materials & Interfaces. “We will have to control nonlinear spin dynamics at the quantum level to achieve their functionality.”
Barsukov explained that in nanomagnets, which serve as building blocks for many spintronic technologies, magnons show quantized energy levels. Interaction between the magnons follows certain symmetry rules. The research team learned to engineer the magnon interaction and identified two approaches to achieve nonlinearity: breaking the symmetry of the nanomagnet’s spin configuration; and modifying the symmetry of the magnons. They chose the second approach.
“Modifying magnon symmetry is the more challenging but also more application-friendly approach,” said Arezoo Etesamirad, the first author of the research paper and a graduate student in Barsukov’s lab.
In their approach, the researchers subjected a nanomagnet to a magnetic field that showed nonuniformity at characteristic nanometer length scales. This nanoscale nonuniform magnetic field itself had to originate from another nanoscale object.
For a source of such a magnetic field, the researchers used a nanoscale synthetic antiferromagnet, or SAF, consisting of two ferromagnetic layers with antiparallel spin orientation. In its normal state, SAF generates nearly no stray field — the magnetic field surrounding the SAF, which is very small. Once it undergoes the so-called spin-flop transition, the spins become canted and the SAF generates a stray field with nonuniformity at nanoscale, as needed. The researchers switched the SAF between the normal state and the spin-flop state in a controlled manner to toggle the symmetry-breaking field on and off.
“We were able to manipulate the magnon interaction coefficient by at least one order of magnitude,” Etesamirad said. “This is a very promising result, which could be used to engineer coherent magnon coupling in quantum information systems, create distinct dissipative states in magnetic neuromorphic networks, and control large excitation regimes in spin-torque devices.”
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Materials provided by University of California – Riverside. Original written by Iqbal Pittalwala. Note: Content may be edited for style and length. More

Isaac Newton may have met his match.
For centuries, engineers have relied on physical laws — developed by Newton and others — to understand the stresses and strains on the materials they work with. But solving those equations can be a computational slog, especially for complex materials.
MIT researchers have developed a technique to quickly determine certain properties of a material, like stress and strain, based on an image of the material showing its internal structure. The approach could one day eliminate the need for arduous physics-based calculations, instead relying on computer vision and machine learning to generate estimates in real time.
The researchers say the advance could enable faster design prototyping and material inspections. “It’s a brand new approach,” says Zhenze Yang, adding that the algorithm “completes the whole process without any domain knowledge of physics.”
The research appears today in the journal Science Advances. Yang is the paper’s lead author and a PhD student in the Department of Materials Science and Engineering. Co-authors include former MIT postdoc Chi-Hua Yu and Markus Buehler, the McAfee Professor of Engineering and the director of the Laboratory for Atomistic and Molecular Mechanics.
Engineers spend lots of time solving equations. They help reveal a material’s internal forces, like stress and strain, which can cause that material to deform or break. Such calculations might suggest how a proposed bridge would hold up amid heavy traffic loads or high winds. Unlike Sir Isaac, engineers today don’t need pen and paper for the task. “Many generations of mathematicians and engineers have written down these equations and then figured out how to solve them on computers,” says Buehler. “But it’s still a tough problem. It’s very expensive — it can take days, weeks, or even months to run some simulations. So, we thought: Let’s teach an AI to do this problem for you.”
The researchers turned to a machine learning technique called a Generative Adversarial Neural Network. They trained the network with thousands of paired images — one depicting a material’s internal microstructure subject to mechanical forces, and the other depicting that same material’s color-coded stress and strain values. With these examples, the network uses principles of game theory to iteratively figure out the relationships between the geometry of a material and its resulting stresses. More

New research from the University of Surrey has shown that silicon could be one of the most powerful materials for photonic informational manipulation — opening up new possibilities for the production of lasers and displays.
While computer chips’ extraordinary success has confirmed silicon as the prime material for electronic information control, silicon has a reputation as a poor choice for photonics; there are no commercially available silicon light-emitting diodes, lasers or displays.
Now, in a paper published by Light: Science and Applications journal, a Surrey-led international team of scientists has shown that silicon is an outstanding candidate for creating a device that can control multiple light beams.
The discovery means that it is now possible to produce silicon processors with built-in abilities for light beams to control other beams — boosting the speed and efficiency of electronic communications.
This is possible thanks to the wavelength band called the far-infrared or terahertz region of the electromagnetic spectrum. The effect works with a property called a nonlinearity, which is used to manipulate laser beams — for example, changing their colour. Green laser pointers work this way: they take the output from a very cheap and efficient but invisible infrared laser diode and change the colour to green with a nonlinear crystal that halves the wavelength.
Other kinds of nonlinearity can produce an output beam with a third of the wavelength or be used to redirect a laser beam to control the direction of the beam’s information. The stronger the nonlinearity, the easier it is to control with weaker input beams.
The researchers found that silicon possesses the strongest nonlinearity of this type ever discovered. Although the study was carried out with the crystal being cooled to very low cryogenic temperatures, such strong nonlinearities mean that extremely weak beams can be used.
Ben Murdin, co-author of the study and Professor of Physics at the University of Surrey, said: “Our finding was lucky because we weren’t looking for it. We were trying to understand how a very small number of phosphorus atoms in a silicon crystal could be used for making a quantum computer and how to use light beams to control quantum information stored in the phosphorus atoms.
“We were astonished to find that the phosphorus atoms were re-emitting light beams that were almost as bright as the very intense laser we were shining on them. We shelved the data for a couple of years while we thought about proving where the beams were coming from. It’s a great example of the way science proceeds by accident, and also how pan-European teams can still work together very effectively.”
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If a base station in a local area network tries to use a directional beam to transmit a signal to a user trying to connect to the network — instead of using a wide area network broadcast, as base stations commonly do — how does it know which direction to send the beam?
Researchers from Rice University and Brown University developed a link discovery method in 2020 using terahertz radiation, with high-frequency waves above 100 gigahertz. For this work, they deferred the question of what would happen if a wall or other reflector nearby creates a non-line-of-sight (NLOS) path from the base station to the receiver and focused on the simpler situation where the only existing path was along the line-of-sight (LOS).
In APL Photonics, from AIP Publishing, those same researchers address this question by considering two different generic types of transmitters and exploring how their characteristics can be used to determine whether an NLOS path contributes to the signal received by the receiver.
“One type of transmitter sends all frequencies more or less in the same direction,” said Daniel Mittleman, co-author and an engineering professor at Brown, “while the other type sends different frequencies in different directions, exhibiting strong angular dispersion. The situation is quite different in these two different cases.”
The researchers’ work shows that the transmitter sending different frequencies in different directions has distinct advantages in its ability to detect the NLOS path and distinguish them from the LOS path.
“A well-designed receiver would be able to detect both frequencies and use their properties to recognize the two paths and tell them apart,” Mittleman said.
Many recent reports within academic literature have focused on various challenges involved in using terahertz signals for wireless communications. Indeed, the term 6G has become a buzzword to encompass future generations of wireless systems that use these ultrahigh-frequency signals.
“For terahertz signals to be used for wireless communications, many challenges must be overcome, and one of the biggest is how to detect and exploit NLOS paths,” said Mittleman.
This work is among the first to provide a quantitative consideration of how to detect and exploit NLOS paths, as well as a comparison of the behavior of different transmitters within this context.
“For most realistic indoor scenarios we can envision for an above-100 gigahertz wireless network, the issue of NLOS path is definitely going to require careful consideration,” said Mittleman. “We need to know how to exploit these link opportunities to maintain connectivity.”
If, for example, the LOS path is blocked by something, an NLOS path can be used to maintain the link between the base station and receiver.
“Interestingly, with a transmitter creating strong angular dispersion, sometimes an NLOS link can provide even faster connectivity than the LOS link,” said Yasaman Ghasempour, co-author and assistant professor at Rice University. “But you can’t take advantage of such opportunities if you don’t know the NLOS path exists or how to find it.”
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An international team with researchers from the University of Bayreuth has succeeded for the first time in discovering a previously unknown two-dimensional material by using modern high-pressure technology. The new material, beryllonitrene, consists of regularly arranged nitrogen and beryllium atoms. It has an unusual electronic lattice structure that shows great potential for applications in quantum technology. Its synthesis required a compression pressure that is about one million times higher than the pressure of the Earth’s atmosphere. The scientists have presented their discovery in the journal Physical Review Letters.
Since the discovery of graphene, which is made of carbon atoms, interest in two-dimensional materials has grown steadily in research and industry. Under extremely high pressures of up to 100 gigapascals, researchers from the University of Bayreuth, together with international partners, have now produced novel compounds composed of nitrogen and beryllium atoms. These are beryllium polynitrides, some of which conform to the monoclinic, others to the triclinic crystal system. The triclinic beryllium polynitrides exhibit one unusual characteristic when the pressure drops. They take on a crystal structure made up of layers. Each layer contains zigzag nitrogen chains connected by beryllium atoms. It can therefore be described as a planar structure consisting of BeN? pentagons and Be?N? hexagons. Thus, each layer represents a two-dimensional material, beryllonitrene.
Qualitatively, beryllonitrene is a new 2D material. Unlike graphene, the two-dimensional crystal structure of beryllonitrene results in a slightly distorted electronic lattice. Because of its resulting electronic properties, beryllonitrene would be excellently suited for applications in quantum technology if it could one day be produced on an industrial scale. In this still young field of research and development, the aim is to use the quantum mechanical properties and structures of matter for technical innovations — for example, for the construction of high-performance computers or for novel encryption techniques with the goal of secure communication.
“For the first time, close international cooperation in high-pressure research has now succeeded in producing a chemical compound in that was previously completely unknown. This compound could serve as a precursor for a 2D material with unique electronic properties. The fascinating achievement was only possible with the help of a laboratory-generated compression pressure almost a million times greater than the pressure of the Earth’s atmosphere. Our study thus once again proves the extraordinary potential of high-pressure research in materials science,” says co-author Prof. Dr. Natalia Dubrovinskaia from the Laboratory for Crystallography at the University of Bayreuth. “However, there is no possibility of devising a process for the production of beryllonitrene on an industrial scale as long as extremely high pressures, such as can only be generated in the research laboratory, are required for this. Nevertheless, it is highly significant that the new compound was created during decompression and that it can exist under ambient conditions. In principle, we cannot rule out that one day it will be possible to reproduce beryllonitrene or a similar 2D material with technically less complex processes and use it industrially. With our study, we have opened up new prospects for high-pressure research in the development of technologically promising 2D materials that may surpass graphene,” says corresponding author Prof. Dr. Leonid Dubrovinsky from the Bavarian Research Institute of Experimental Geochemistry & Geophysics at the University of Bayreuth.
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University of South Australia researchers have drawn inspiration from a 300-million-year-old superior flying machine — the dragonfly — to show why future flapping wing drones will probably resemble the insect in shape, wings and gearing.
A team of PhD students led by UniSA Professor of Sensor Systems, Javaan Chahl, spent part of the 2020 COVID-19 lockdown designing and testing key parts of a dragonfly-inspired drone that might match the insect’s extraordinary skills in hovering, cruising and aerobatics.
The UniSA students worked remotely on the project, solving mathematical formulas at home on whiteboards, digitising stereo photographs of insect wings into 3D models, and using spare rooms as rapid prototyping workshops to test parts of the flapping wing drone.
Their findings have been published in the journal Drones.
Describing the dragonfly as the “apex insect flyer,” Prof Chahl says numerous engineering lessons can be learned from its mastery in the air.
“Dragonflies are supremely efficient in all areas of flying. They need to be. After emerging from under water until their death (up to six months), male dragonflies are involved in perpetual, dangerous combat against male rivals. Mating requires an aerial pursuit of females and they are constantly avoiding predators. Their flying abilities have evolved over millions of years to ensure they survive,” Prof Chahl says. More