Aurelia Butler

Structural coloration is promised to be the display technology of the future as there is no fading – it does not use dyes – and enables low-power displays without strong external light source. However, the disadvantage of this technique is that once a device is made, it is impossible to change its properties so the reproducible colors remain fixed. Recently, a research team has successfully obtained vivid colors by using semiconductor chips – not dyes – made by mimicking the human brain structure. More

A fifth of carbon dioxide emissions come from multinational companies’ global supply chains, according to a new study that shows the scope of multinationals’ influence on climate change. More

Quantum bits, or qubits, can hold quantum information much longer now thanks to efforts by an international research team. The researchers have increased the retention time, or coherence time, to 10 milliseconds — 10,000 times longer than the previous record — by combining the orbital motion and spinning inside an atom. Such a boost in information retention has major implications for information technology developments since the longer coherence time makes spin-orbit qubits the ideal candidate for building large quantum computers.
They published their results on July 20 in Nature Materials.
“We defined a spin-orbit qubit using a charged particle, which appears as a hole, trapped by an impurity atom in silicon crystal,” said lead author Dr. Takashi Kobayashi, research scientist at the University of New South Wales Sydney and assistant professor at Tohoku University. “Orbital motion and spinning of the hole are strongly coupled and locked together. This is reminiscent of a pair of meshing gears where circular motion and spinning are locked together.”
Qubits have been encoded with spin or orbital motion of a charged particle, producing different advantages that are highly demanded for building quantum computers. To utilize the advantages of qubits, Kobayashi and the team specifically used an exotic charged particle “hole” in silicon to define a qubit, since orbital motion and spin of holes in silicon are coupled together.
Spin-orbit qubits encoded by holes are particularly sensitive to electric fields, according to Kobayashi, which allows for more rapid control and benefits scaling up quantum computers. However, the qubits are affected by electrical noise, limiting their coherence time.
“In this work, we have engineered sensitivity to the electric field of our spin-orbit qubit by stretching the silicon crystal like a rubber band,” Kobayashi said. “This mechanical engineering of the spin-orbit qubit enables us to remarkably extend its coherence time, while still retaining moderate electrical sensitivity to control the spin-orbit qubit.”
Think of gears in a watch. Their individual spinning propels the entire mechanism to keep time. It is neither the spin nor orbital motion, but a combination of them that takes the information forward.
“These results open a pathway to develop new artificial quantum systems and to improve the functionality and scalability of spin-based quantum technologies,” Kobayashi said.
This work was supported in part by the ARC Centre of Excellence for Quantum Computation and Communication Technology, the U.S. Army Research Office and the Tohoku University Graduate Program in Spintronics.

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Materials provided by Tohoku University. Note: Content may be edited for style and length. More

Researchers from the Paul-Drude-Institut in Berlin, the Helmholtz-Zentrum in Dresden and the Ioffe Institute in St. Petersburg have demonstrated the use of elastic vibrations to manipulate the spin states of optically active color centers in SiC at room temperature. They show a non-trivial dependence of the acoustically induced spin transitions on the spin quantization direction, which can lead to chiral spin-acoustic resonances. These findings are important for applications in future quantum-electronic devices and have recently been published in Physical Review Letters.
Color centers in solids are optically active crystallographic defects containing one or more trapped electrons. Of special interest for applications in quantum technologies are optically addressable color centers, that is, lattice defects whose electronic spin states can be selectively initialized and read-out using light. In addition to initialization and read-out, it is also necessary to develop efficient methods to manipulate their spin states, and thus the information stored in them. While this is typically realized by applying microwave fields, an alternative and more efficient method could be the use of mechanical vibrations. Among the different materials for the implementation of such strain-based technologies, SiC is attracting growing attention as a robust material for nano-electromechanical systems with an ultrahigh sensitivity to vibrations that also hosts highly-coherent optically active color centers.
In a recent work published in Physical Review Letters, researches from the Paul-Drude-Institut fuer Festkoerperelektronik, the Helmholtz-Zentrum Dresden-Rossendorf and the Ioffe Institute have demonstrated the use of elastic vibrations to manipulate the spin states of optically active color centers in SiC at room temperature. In their study, the authors use the periodic modulation of the SiC crystal lattice to induce transitions between the spin levels of the silicon-vacancy center, an optically active color center with spin S=3/2. Of special importance for future applications is the fact that, in contrast to most atom-like light centers, where the observation of strain-induced effects requires cooling the system to very low temperatures, the effects reported here were observed at room temperature.
To couple the lattice vibrations to the silicon-vacancy centers, the authors first selectively created such centers by irradiating the SiC with protons. Then they fabricated an acoustic resonator for the excitation of standing surface acoustic waves (SAW) on the SiC. SAWs are elastic vibrations confined to the surface of a solid that resemble seismic waves created during an earthquake. When the frequency of the SAW matches the resonant frequencies of the color centers, the electrons trapped in them can use the energy of the SAW to jump between the different spin sublevels. Due to the special nature of the spin-strain coupling, the SAW can induce jumps between spin states with magnetic quantum number differences ?m=±1 and ?m=±2, while microwave-induced ones are restricted to ?m=±1. This allows to realize full control of the spin states using high-frequency vibrations without the aid of external microwave fields.
In addition, due to the intrinsic symmetry of the SAW strain fields combined with the peculiar properties of the half-integer spin system, the intensity of such spin transitions depends on the angle between SAW propagation and spin quantization directions, which can be controlled by an external magnetic field. Moreover, the authors predict a chiral spin-acoustic resonance under traveling SAWs. This means that, under certain experimental conditions, the spin transitions can be switched on or off by inverting the magnetic field or the SAW propagation direction.
These findings establish silicon carbide as a highly promising hybrid platform for on-chip spin-optomechanical quantum control enabling engineered interactions at room temperature.

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Materials provided by Forschungsverbund Berlin. Note: Content may be edited for style and length. More

Nature’s speed limits aren’t posted on road signs, but Rice University physicists have discovered a new way to deduce them that is better — infinitely better, in some cases — than previous methods.
“The big question is, ‘How fast can anything — information, mass, energy — move in nature?'” said Kaden Hazzard, a theoretical quantum physicist at Rice. “It turns out that if somebody hands you a material, it is incredibly difficult, in general, to answer the question.”
In a study published today in the American Physical Society journal PRX Quantum, Hazzard and Rice graduate student Zhiyuan Wang describe a new method for calculating the upper bound of speed limits in quantum matter.
“At a fundamental level, these bounds are much better than what was previously available,” said Hazzard, an assistant professor of physics and astronomy and member of the Rice Center for Quantum Materials. “This method frequently produces bounds that are 10 times more accurate, and it’s not unusual for them to be 100 times more accurate. In some cases, the improvement is so dramatic that we find finite speed limits where previous approaches predicted infinite ones.”
Nature’s ultimate speed limit is the speed of light, but in nearly all matter around us, the speed of energy and information is much slower. Frequently, it is impossible to describe this speed without accounting for the large role of quantum effects.
In the 1970s, physicists proved that information must move much slower than the speed of light in quantum materials, and though they could not compute an exact solution for the speeds, physicists Elliott Lieb and Derek Robinson pioneered mathematical methods for calculating the upper bounds of those speeds.

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“The idea is that even if I can’t tell you the exact top speed, can I tell you that the top speed must be less than a particular value,” Hazzard said. “If I can give a 100% guarantee that the real value is less than that upper bound, that can be extremely useful.”
Hazzard said physicists have long known that some of the bounds produced by the Lieb-Robinson method are “ridiculously imprecise.”
“It might say that information must move less than 100 miles per hour in a material when the real speed was measured at 0.01 miles per hour,” he said. “It’s not wrong, but it’s not very helpful.”
The more accurate bounds described in the PRX Quantum paper were calculated by a method Wang created.
“We invented a new graphical tool that lets us account for the microscopic interactions in the material instead of relying only on cruder properties such as its lattice structure,” Wang said.

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Hazzard said Wang, a third-year graduate student, has an incredible talent for synthesizing mathematical relationships and recasting them in new terms.
“When I check his calculations, I can go step by step, churn through the calculations and see that they’re valid,” Hazzard said. “But to actually figure out how to get from point A to point B, what set of steps to take when there’s an infinite variety of things you could try at each step, the creativity is just amazing to me.”
The Wang-Hazzard method can be applied to any material made of particles moving in a discrete lattice. That includes oft-studied quantum materials like high-temperature superconductors, topological materials, heavy fermions and others. In each of these, the behavior of the materials arises from interactions of billions upon billions of particles, whose complexity is beyond direct calculation.
Hazzard said he expects the new method to be used in several ways.
“Besides the fundamental nature of this, it could be useful for understanding the performance of quantum computers, in particular in understanding how long they take to solve important problems in materials and chemistry,” he said.
Hazzard said he is certain the method will also be used to develop numerical algorithms because Wang has shown it can put rigorous bounds on the errors produced by oft-used numerical techniques that approximate the behavior of large systems.
A popular technique physicists have used for more than 60 years is to approximate a large system by a small one that can be simulated by a computer.
“We draw a small box around a finite chunk, simulate that and hope that’s enough to approximate the gigantic system,” Hazzard said. “But there has not been a rigorous way of bounding the errors in these approximations.”
The Wang-Hazzard method of calculating bounds could lead to just that.
“There is an intrinsic relationship between the error of a numerical algorithm and the speed of information propagation,” Wang explained, using the sound of his voice and the walls in his room to illustrate the link.
“The finite chunk has edges, just as my room has walls. When I speak, the sound will get reflected by the wall and echo back to me. In an infinite system, there is no edge, so there is no echo.”
In numerical algorithms, errors are the mathematical equivalent of echoes. They reverberate from the edges of the finite box, and the reflection undermines the algorithms’ ability to simulate the infinite case. The faster information moves through the finite system, the shorter the time the algorithm faithfully represents the infinite. Hazzard said he, Wang and others in his research group are using their method to craft numerical algorithms with guaranteed error bars.
“We don’t even have to change the existing algorithms to put strict, guaranteed error bars on the calculations,” he said. “But you can also flip it around and use this to make better numerical algorithms. We’re exploring that, and other people are interested in using these as well.” More

A hand-held video game console allowing indefinite gameplay might be a parent’s worst nightmare.
But this Game Boy is not just a toy. It’s a powerful proof-of-concept, developed by researchers at Northwestern University and the Delft University of Technology (TU Delft) in the Netherlands, that pushes the boundaries of battery-free intermittent computing into the realm of fun and interaction.
Instead of batteries, which are costly, environmentally hazardous and ultimately end up in landfills, this device harvests energy from the sun — and the user. These advances enable gaming to last forever without having to stop and recharge the battery.
“It’s the first battery-free interactive device that harvests energy from user actions,” said Northwestern’s Josiah Hester, who co-led the research. “When you press a button, the device converts that energy into something that powers your gaming.”
“Sustainable gaming will become a reality, and we made a major step in that direction — by getting rid of the battery completely,” said TU Delft’s Przemyslaw Pawelczak, who co-led the research with Hester. “With our platform, we want to make a statement that it is possible to make a sustainable gaming system that brings fun and joy to the user.”
The teams will present the research virtually at UbiComp 2020, a major conference within the field of interactive systems, on Sept. 15.
Hester is an assistant professor of electrical and computer engineering and computer science in Northwestern’s McCormick School of Engineering. Pawelczak is an assistant professor in the Embedded Software Lab at TU Delft. Their team includes Jasper de Winkel and Vito Kortbeek, both Ph.D. candidates at TU Delft.
The researchers’ energy aware gaming platform (ENGAGE) has the size and form factor of the original Game Boy, while being equipped with a set of solar panels around the screen. Button presses by the user are a second source of energy. Most importantly, it impersonates the Game Boy processor. Although this solution requires a lot of computational power, and therefore energy, it allows any popular retro game to be played straight from its original cartridge.
As the device switches between power sources, it does experience short losses in power. To ensure an acceptable duration of gameplay between power failures, the researchers designed the system hardware and software from the ground up to be energy aware as well as very energy efficient. They also developed a new technique for storing the system state in non-volatile memory, minimizing overhead and allowing quick restoration when power returns. This eliminates the need to press “save” as seen in traditional platforms, as the player can now continue gameplay from the exact point of the device fully losing power — even if Mario is in mid-jump.
On a not-too-cloudy day, and for games that require at least moderate amounts of clicking, gameplay interruptions typically last less than one second for every 10 seconds of gameplay. The researchers find this to be a playable scenario for some games — including Chess, Solitaire and Tetris — but certainly not yet for all (action) games.
Although there is still a long way to go before state-of-the-art 21st century hand-held game consoles become fully battery-free, the researchers hope their devices raise awareness of the environmental impact of the small devices that make up the Internet of Things. Batteries are costly, environmentally hazardous and they must eventually be replaced to avoid that the entire device ends up at the landfill.
“Our work is the antithesis of the Internet of Things, which has many devices with batteries in them,” Hester said. “Those batteries eventually end up in the garbage. If they aren’t fully discharged, they can become hazardous. They are hard to recycle. We want to build devices that are more sustainable and can last for decades.” More

A Rochester Institute of Technology researcher developed a mathematical method that shows climate change likely caused the rise and fall of an ancient civilization. In an article recently featured in the journal Chaos: An Interdisciplinary Journal of Nonlinear Science, Nishant Malik, assistant professor in RIT’s School of Mathematical Sciences, outlined the new technique he developed and showed how shifting monsoon patterns led to the demise of the Indus Valley Civilization, a Bronze Age civilization contemporary to Mesopotamia and ancient Egypt.
Malik developed a method to study paleoclimate time series, sets of data that tell us about past climates using indirect observations. For example, by measuring the presence of a particular isotope in stalagmites from a cave in South Asia, scientists were able to develop a record of monsoon rainfall in the region for the past 5,700 years. But as Malik notes, studying paleoclimate time series poses several problems that make it challenging to analyze them with mathematical tools typically used to understand climate.
“Usually the data we get when analyzing paleoclimate is a short time series with noise and uncertainty in it,” said Malik. “As far as mathematics and climate is concerned, the tool we use very often in understanding climate and weather is dynamical systems. But dynamical systems theory is harder to apply to paleoclimate data. This new method can find transitions in the most challenging time series, including paleoclimate, which are short, have some amount of uncertainty and have noise in them.”
There are several theories about why the Indus Valley Civilization declined — including invasion by nomadic Indo-Aryans and earthquakes — but climate change appears to be the most likely scenario. But until Malik applied his hybrid approach — rooted in dynamical systems but also draws on methods from the fields of machine learning and information theory — there was no mathematical proof. His analysis showed there was a major shift in monsoon patterns just before the dawn of this civilization and that the pattern reversed course right before it declined, indicating it was in fact climate change that caused the fall.
Malik said he hopes the method will allow scientists to develop more automated methods of finding transitions in paleoclimate data and leads to additional important historical discoveries. The full text of the study is published in Chaos: An Interdisciplinary Journal of Nonlinear Science.

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Materials provided by Rochester Institute of Technology. Original written by Luke Auburn. Note: Content may be edited for style and length. More

Molecules are the building blocks of everyday life. Many materials are composed of them, a little like a LEGO model consists of a multitude of different bricks. But while individual LEGO bricks can be simply shifted or removed, this is not so easy in the nanoworld. Atoms and molecules behave in a completely different way to macroscopic objects and each brick requires its own “instruction manual.” Scientists from Jülich and Berlin have now developed an artificial intelligence system that autonomously learns how to grip and move individual molecules using a scanning tunnelling microscope. The method, which has been published in Science Advances, is not only relevant for research but also for novel production technologies such as molecular 3D printing.
Rapid prototyping, the fast and cost-effective production of prototypes or models — better known as 3D printing — has long since established itself as an important tool for industry. “If this concept could be transferred to the nanoscale to allow individual molecules to be specifically put together or separated again just like LEGO bricks, the possibilities would be almost endless, given that there are around 1060 conceivable types of molecule,” explains Dr. Christian Wagner, head of the ERC working group on molecular manipulation at Forschungszentrum Jülich.
There is one problem, however. Although the scanning tunnelling microscope is a useful tool for shifting individual molecules back and forth, a special custom “recipe” is always required in order to guide the tip of the microscope to arrange molecules spatially in a targeted manner. This recipe can neither be calculated, nor deduced by intuition — the mechanics on the nanoscale are simply too variable and complex. After all, the tip of the microscope is ultimately not a flexible gripper, but rather a rigid cone. The molecules merely adhere lightly to the microscope tip and can only be put in the right place through sophisticated movement patterns.
“To date, such targeted movement of molecules has only been possible by hand, through trial and error. But with the help of a self-learning, autonomous software control system, we have now succeeded for the first time in finding a solution for this diversity and variability on the nanoscale, and in automating this process,” says a delighted Prof. Dr. Stefan Tautz, head of Jülich’s Quantum Nanoscience institute.
The key to this development lies in so-called reinforcement learning, a special variant of machine learning. “We do not prescribe a solution pathway for the software agent, but rather reward success and penalize failure,” explains Prof. Dr. Klaus-Robert Müller, head of the Machine Learning department at TU Berlin. The algorithm repeatedly tries to solve the task at hand and learns from its experiences. The general public first became aware of reinforcement learning a few years ago through AlphaGo Zero. This artificial intelligence system autonomously developed strategies for winning the highly complex game of Go without studying human players — and after just a few days, it was able to beat professional Go players.
“In our case, the agent was given the task of removing individual molecules from a layer in which they are held by a complex network of chemical bonds. To be precise, these were perylene molecules, such as those used in dyes and organic light-emitting diodes,” explains Dr. Christian Wagner. The special challenge here is that the force required to move them must never exceed the strength of the bond with which the tip of the scanning tunnelling microscope attracts the molecule, since this bond would otherwise break. “The microscope tip therefore has to execute a special movement pattern, which we previously had to discover by hand, quite literally,” Wagner adds. While the software agent initially performs completely random movement actions that break the bond between the tip of the microscope and the molecule, over time it develops rules as to which movement is the most promising for success in which situation and therefore gets better with each cycle.
However, the use of reinforcement learning in the nanoscopic range brings with it additional challenges. The metal atoms that make up the tip of the scanning tunnelling microscope can end up shifting slightly, which alters the bond strength to the molecule each time. “Every new attempt makes the risk of a change and thus the breakage of the bond between tip and molecule greater. The software agent is therefore forced to learn particularly quickly, since its experiences can become obsolete at any time,” Prof. Dr. Stefan Tautz explains. “It’s a little as if the road network, traffic laws, bodywork, and rules for operating the vehicle are constantly changing while driving autonomously.” The researchers have overcome this challenge by making the software learn a simple model of the environment in which the manipulation takes place in parallel with the initial cycles. The agent then simultaneously trains both in reality and in its own model, which has the effect of significantly accelerating the learning process.
“This is the first time ever that we have succeeded in bringing together artificial intelligence and nanotechnology,” emphasizes Klaus-Robert Müller. “Up until now, this has only been a ‘proof of principle’,” Tautz adds. “However, we are confident that our work will pave the way for the robot-assisted automated construction of functional supramolecular structures, such as molecular transistors, memory cells, or qubits — with a speed, precision, and reliability far in excess of what is currently possible.” More