Top Technology News — ScienceDaily

Top Technology News — ScienceDaily

Top Technology News — ScienceDaily
Top stories featured on ScienceDaily’s Space & Time, Matter & Energy, and Computers & Math sections.
Top Technology News — ScienceDaily

Qubits comprised of holes could be the trick to build faster, larger quantum computers
<div><img src=”” class=”ff-og-image-inserted”></div><p id=”first”>A new study indicates holes the solution to operational speed/coherence trade-off, potential scaling up of qubits to a mini-quantum computer.</p><div id=”text” readability=”99″>
<p>Quantum computers are predicted to be much more powerful and functional than today’s ‘classical’ computers.</p>
<p>One way to make a quantum bit is to use the ‘spin’ of an electron, which can point either up or down. To make quantum computers as fast and power-efficient as possible we would like to operate them using only electric fields, which are applied using ordinary electrodes.</p>
<p>Although spin does not ordinarily ‘talk’ to electric fields, in some materials spins can interact with electric fields indirectly, and these are some of the hottest materials currently studied in quantum computing.</p>
<p>The interaction that enables spins to talk to electric fields is called the spin-orbit interaction, and is traced all the way back to Einstein’s theory of relativity.</p>
<p>The fear of quantum-computing researchers has been that when this interaction is strong, any gain in operation speed would be offset by a loss in coherence (essentially, how long we can preserve quantum information).</p>

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<p>”If electrons start to talk to the electric fields we apply in the lab, this means they are also exposed to unwanted, fluctuating electric fields that exist in any material (generically called `noise’) and those electrons’ fragile quantum information would be destroyed,” says A/Prof Dimi Culcer (UNSW/FLEET), who led the theoretical roadmap study.</p>
<p>”But our study has shown this fear is not justified.”</p>
<p>”Our theoretical studies show that a solution is reached by using holes, which can be thought of as the absence of an electron, behaving like positively-charged electrons.”</p>
<p>In this way, a quantum bit can be made robust against charge fluctuations stemming from the solid background.</p>
<p>Moreover, the ‘sweet spot’ at which the qubit is least sensitive to such noise is also the point at which it can be operated the fastest.</p>
<p>”Our study predicts such a point exists in every quantum bit made of holes and provides a set of guidelines for experimentalists to reach these points in their labs,” says Dimi.</p>
<p>Reaching these points will facilitate experimental efforts to preserve quantum information for as long as possible. This will also provide strategies for ‘scaling up’ quantum bits — ie, building an ‘array’ of bits that would work as a mini-quantum computer.</p>
<p>”This theoretical prediction is of key importance for scaling up quantum processors and first experiments have already been carried out,” says Prof Sven Rogge of the Centre for Quantum Computing and Communication Technology (CQC<sup>2</sup>T).”</p>
<p>”Our recent experiments on hole qubits using acceptors in silicon already demonstrated longer coherence times than we expected,” says A/Prof Joe Salfi of the University of British Columbia. “It is encouraging to see that these observations rest on a firm theoretical footing. The prospects for hole qubits are bright indeed.”</p>
</div><p><strong><a href=””></a></strong> <a href=””>(Why?)</a></p> Fri, 02 Apr 2021 13:59:46 +0000 en

Scientists turn to deep learning to improve air quality forecasts
<div><img src=”” class=”ff-og-image-inserted”></div><p id=”first”>Air pollution from the burning of fossil fuels impacts human health but predicting pollution levels at a given time and place remains challenging, according to a team of scientists who are turning to deep learning to improve air quality estimates. Results of the team’s study could be helpful for modelers examining how economic factors like industrial productivity and health factors like hospitalizations change with pollution levels.</p><div id=”text” readability=”96″>
<p>”Air quality is one of the major issues within an urban area that affects people’s lives,” said Manzhu Yu, assistant professor of geography at Penn State. “Yet existing observations are not adequate to provide comprehensive information that may help vulnerable populations to plan ahead.”</p>
<p>Satellite and ground-based observations each measure air pollution, but they are limited, the scientists said. Satellites, for instance, may pass a given location at the same time each day and miss how emissions vary at different hours. Ground-based weather stations continuously collect data but only in a limited number of locations.</p>
<p>To address this, the scientists used deep learning, a type of machine learning, to analyze the relationship between satellite and ground-based observations of nitrogen dioxide in the greater Los Angeles area. Nitrogen dioxide is largely associated with emissions from traffic and power plants, the scientists said.</p>
<p>”The problem right now is nitrogen dioxide varies a lot during the day,” Yu said. “But we haven’t had an hourly, sub-urban scale product available to track air pollution. By comparing surface level and satellite observations, we can actually produce estimates with higher spatial and temporal resolution.”</p>
<p>The learned relationship allowed the researchers to take daily satellite observations and create hourly estimates of atmospheric nitrogen dioxide in roughly 3-mile grids, the scientists said. They recently reported their findings in the journal <em>Science of the Total Environment</em>.</p>
<p>”The challenge here is whether we can find a linkage between measurements from earth’s surface and satellite observations of the troposphere, which are actually far away from each other. That’s where deep learning comes in.”</p>
<p>Deep learning algorithms operate much like the human brain and feature multiple layers of artificial neurons for processing data and creating patterns. The system learns and trains itself based on connections it finds within large amounts of data, the scientists said.</p>
<p>The scientists tested two deep-learning algorithms and found the one that compared the ground-based observations directly to the satellite observations more accurately predicted nitrogen dioxide levels. Adding information like meteorological data, elevation and the locations of the ground-based stations and major roads and power plants improved the prediction accuracy further.</p>
<p>Yu said the study could be repeated for other greenhouse gases and applied to different cities or on regional and continental scales, the scientists said. In addition, the model could be updated when new, higher-resolution satellites are launched.</p>
<p>”With a high spatiotemporal resolution, our results will facilitate the study between air quality and health issues and improve the understanding of the dynamic evolution of airborne pollutants,” Yu said.</p>
</div><div id=”story_source” readability=”28.320895522388″> <p><strong>Story Source:</strong></p>
<p><a href=”” rel=”nofollow” target=”_blank”>Materials</a> provided by <a href=”” rel=”nofollow” target=”_blank”><strong>Penn State</strong></a>. Original written by Matthew Carroll. <em>Note: Content may be edited for style and length.</em></p>
</div><p><strong><a href=””></a></strong> <a href=””>(Why?)</a></p>
Fri, 02 Apr 2021 13:59:34 +0000 en

Kirigami-style fabrication may enable new 3D nanostructures
<div><img src=”” class=”ff-og-image-inserted”></div><p id=”first”>A new technique that mimics the ancient Japanese art of kirigami may offer an easier way to fabricate complex 3D nanostructures for use in electronics, manufacturing and health care.</p><div id=”text” readability=”94″>
<p>Kirigami enhances the Japanese artform of origami, which involves folding paper to create 3D structural designs, by strategically incorporating cuts to the paper prior to folding. The method enables artists to create sophisticated three-dimensional structures more easily.</p>
<p>”We used kirigami at the nanoscale to create complex 3D nanostructures,” said Daniel Lopez, Penn State Liang Professor of Electrical Engineering and Computer Science, and leader of the team that published this research in <em>Advanced Materials</em>. “These 3D structures are difficult to fabricate because current nanofabrication processes are based on the technology used to fabricate microelectronics which only use planar, or flat, films. Without kirigami techniques, complex three-dimensional structures would be much more complicated to fabricate or simply impossible to make.”</p>
<p>Lopez said that if force is applied to a uniform structural film, nothing really happens other than stretching it a bit, like what happens when a piece of paper is stretched. But when cuts are introduced to the film, and forces are applied in a certain direction, a structure pops up, similar to when a kirigami artist applies force to a cut paper. The geometry of the planar pattern of cuts determines the shape of the 3D architecture.</p>
<p>”We demonstrated that it is possible to use conventional planar fabrication methods to create different 3D nanostructures from the same 2D cut geometry,” Lopez said. “By introducing minimum changes to the dimensions of the cuts in the film, we can drastically change the three-dimensional shape of the pop-up architectures. We demonstrated nanoscale devices that can tilt or change their curvature just by changing the width of the cuts a few nanometers.”</p>
<p>This new field of kirigami-style nanoengineering enables the development of machines and structures that can change from one shape to another, or morph, in response to changes in the environment. One example is an electronic component that changes shape in elevated temperatures to enable more air flow within a device to keep it from overheating.</p>
<p>”This kirigami technique will allow the development of adaptive flexible electronics that can be incorporated onto surfaces with complicated topography, such as a sensor resting on the human brain,” Lopez said. “We could use these concepts to design sensors and actuators that can change shape and configuration to perform a task more efficiently. Imagine the potential of structures that can change shape with minuscule changes in temperature, illumination or chemical conditions.”</p>
<p>Lopez will focus his future research on applying these kirigami techniques to materials that are one atom thick, and thin actuators made of piezoelectrics. These 2D materials open new possibilities for applications of kirigami-induced structures. Lopez said his goal is to work with other researchers at Penn State’s Materials Research Institute (MRI) to develop a new generation of miniature machines that are atomically flat and are more responsive to changes in the environment.</p>
<p>”MRI is a world leader in the synthesis and characterization of 2D materials, which are the ultimate thin-films that can be used for kirigami engineering,” Lopez said. “Moreover, by incorporating ultra-thin piezo and ferroelectric materials onto kirigami structures, we will develop agile and shape-morphing structures. These shape-morphing micro-machines would be very useful for applications in harsh environments and for drug delivery and health monitoring. I am working at making Penn State and MRI the place where we develop these super-small machines for a specific variety of applications.”</p>
</div><div id=”story_source” readability=”28.285714285714″> <p><strong>Story Source:</strong></p>
<p><a href=”” rel=”nofollow” target=”_blank”>Materials</a> provided by <a href=”” rel=”nofollow” target=”_blank”><strong>Penn State</strong></a>. Original written by Jamie Oberdick. <em>Note: Content may be edited for style and length.</em></p>
</div><p><strong><a href=””></a></strong> <a href=””>(Why?)</a></p>
Fri, 02 Apr 2021 13:59:32 +0000 en

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