3 mins. This is really fascinating. Several applications, including quantum computing. Need special diamonds that scientists now can produce.
Diamonds are dazzling physicists with their powerful quantum properties. A particular impurity — the nitrogen-vacancy (NV) centre — allows diamonds to be used for everything from geolocation to diagnosing disease. This animation takes a closer look at these NV centres, and the carefully crafted artificial diamonds that make them possible.
EPFL scientists have developed ultralow-loss silicon nitride integrated circuits that are central for many photonic devices, such as chip-scale frequency combs, narrow-linewidth lasers, coherent LiDAR, and neuromorphic computing.
A new electrode that could free up 20% more light from organic light-emitting diodes has been developed at the University of Michigan. It could help extend the battery life of smartphones and laptops, or make next-gen televisions and displays much more energy efficient.
The approach prevents light from being trapped in the light-emitting part of an OLED, enabling OLEDs to maintain brightness while using less power. In addition, the electrode is easy to fit into existing processes for making OLED displays and light fixtures.
“With our approach, you can do it all in the same vacuum chamber,” said L. Jay Guo, U-M professor of electrical and computer engineering and corresponding author of the study.
In fall of 2019, we demonstrated that the Sycamore quantum processor could outperform the most powerful classical computers when applied to a tailor-made problem. The next challenge is to extend this result to solve practical problems in materials science, chemistry and physics. But going beyond the capabilities of classical computers for these problems is challenging and will require new insights to achieve state-of-the-art accuracy. Generally, the difficulty in performing quantum simulations of such physical problems is rooted in the wave nature of quantum particles, where deviations in the initial setup, interference from the environment, or small errors in the calculations can lead to large deviations in the computational result.
In two upcoming publications, we outline a blueprint for achieving record levels of precision for the task of simulating quantum materials. In the first work, we consider one-dimensional systems, like thin wires, and demonstrate how to accurately compute electronic properties, such as current and conductance. In the second work, we show how to map the Fermi-Hubbard model, which describes interacting electrons, to a quantum processor in order to simulate important physical properties. These works take a significant step towards realizing our long-term goal of simulating more complex systems with practical applications, like batteries and pharmaceuticals.
Chinese taikonauts’ electronic devices that were brought to Tiangong space station drew Chinese IT fans’ attentions on social media Sina Weibo. Netizens have found out those devices are all produced by Chinese companies.
Netizens have found that the electronic devices brought to China’s Tiangong space station and Shenzhou-12 capsule are all produced by Chinese companies. The topic “daily life of Chinese astronauts” had 240 million views on Sina Weibo by Thursday.
Various devices including a Huawei P30 mobile phone, Lenovo ThinkPad laptop and Xiaomi electronic screwdriver can be seen clearly in the livestream, released on Wednesday.
Mobile phones, tablets are connected to Wi-Fi with a downlink rate of 1.2 gigabits per second, which is equivalent to 5G speeds on the ground.
MIT researchers demonstrate a way to sharply reduce errors in two-qubit gates, a significant advance toward fully realizing quantum computation.
MIT researchers have made a significant advance on the road toward the full realization of quantum computation, demonstrating a technique that eliminates common errors in the most essential operation of quantum algorithms, the two-qubit operation or “gate.”
“Despite tremendous progress toward being able to perform computations with low error rates with superconducting quantum bits (qubits), errors in two-qubit gates, one of the building blocks of quantum computation, persist,” says Youngkyu Sung, an MIT graduate student in electrical engineering and computer science who is the lead author of a paper on this topic published on June 16, 2021, in Physical Review X. “We have demonstrated a way to sharply reduce those errors.”
Move over, gene-editing proteins—there’s a smaller, cheaper, more specific genetic engineering tool on the block: DNAzymes—small DNA molecules that can function like protein enzymes.
Researchers at the University of Illinois Urbana-Champaign have developed a technique that, for the first time, allows DNAzymes to target and cut double-stranded DNA, overcoming a significant limitation of the technology. DNAzymes have been used in biosensing, DNA computing and many other applications. However, when it comes to genetic engineering applications such as gene editing or gene therapy, they have faced a challenge: DNAzymes have only been able to target sites on single-stranded DNA, while the DNA coding for genes in cells is double-stranded. The researchers published their new technique in the Journal of the American Chemical Society.
“DNAzymes have many advantages, including higher stability, smaller size and lower cost than protein enzymes. These advantages perfectly fit the requirement for genetic engineering tools,” said study leader Yi Lu, a professor of chemistry at Illinois. “No DNAzymes could alter double-stranded DNA until this work. By making that happen, we open the door for DNAzymes to enter the entire world of genetic engineering.”
Scientists develop an energy-efficient strategy to reversibly change ‘spin orientation’ or magnetization direction in magnetite at room temperature.
Over the last few decades, conventional electronics has been rapidly reaching its technical limits in computing and information technology, calling for innovative devices that go beyond the mere manipulation of electron current. In this regard, spintronics, the study of devices that exploit the “spin” of electrons to perform functions, is one of the hottest areas in applied physics. But, measuring, altering, and, in general, working with this fundamental quantum property is no mean feat.
Current spintronic devices — for example, magnetic tunnel junctions — suffer from limitations such as high-power consumption, low operating temperatures, and severe constraints in material selection. To this end, a team of scientists at Tokyo University of Science and the National Institute for Materials Science (NIMS), Japan, has published a study in ACS Nano, in which they present a surprisingly simple yet efficient strategy to manipulate the magnetization angle in magnetite (Fe3O4), a typical ferromagnetic material.
Advanced uses of time in image rendering and reconstruction have been the focus of much scientific research in recent years. The motivation comes from the equivalence between space and time given by the finite speed of light c. This equivalence leads to correlations between the time evolution of electromagnetic fields at different points in space. Applications exploiting such correlations, known as time-of-flight (ToF)1 and light-in-flight (LiF)2 cameras, operate at various regimes from radio3,4 to optical5 frequencies. Time-of-flight imaging focuses on reconstructing a scene by measuring delayed stimulus responses via continuous wave, impulses or pseudo-random binary sequence (PRBS) codes1. Light-in-flight imaging, also known as transient imaging6, explores light transport and detection2,7. The combination of ToF and LiF has recently yielded higher accuracy and detail to the reconstruction process, especially in non-line-of-sight images with the inclusion of higher-order scattering and physical processes such as Rayleigh–Sommerfeld diffraction8 in the modeling. However, these methods require experimental characterization of the scene followed by large computational overheads that produce images at low frame rates in the optical regime. In the radio-frequency (RF) regime, 3D images at frame rates of 30 Hz have been produced with an array of 256 wide-band transceivers3. Microwave imaging has the additional capability of sensing through optically opaque media such as walls. Nonetheless, synthetic aperture radar reconstruction algorithms such as the one proposed in ref. 3 required each transceiver in the array to operate individually thus leaving room for improvements in image frame rates from continuous transmit-receive captures. Constructions using beamforming have similar challenges9 where a narrow focused beam scans a scene using an array of antennas and frequency modulated continuous wave (FMCW) techniques.
In this article, we develop an inverse light transport model10 for microwave signals. The model uses a spatiotemporal mask generated by multiple sources, each emitting different PRBS codes, and a single detector, all operating in continuous synchronous transmit-receive mode. This model allows image reconstructions with capture times of the order of microseconds and no prior scene knowledge. For first-order reflections, the algorithm reduces to a single dot product between the reconstruction matrix and captured signal, and can be executed in a few milliseconds. We demonstrate this algorithm through simulations and measurements performed using realistic scenes in a laboratory setting. We then use the second-order terms of the light transport model to reconstruct scene details not captured by the first-order terms.
We start by estimating the information capacity of the scene and develop the light transport equation for the transient imaging model with arguments borrowed from basic information and electromagnetic field theory. Next, we describe the image reconstruction algorithm as a series of approximations corresponding to multiple scatterings of the spatiotemporal illumination matrix. Specifically, we show that in the first-order approximation, the value of each pixel is the dot product between the captured time series and a unique time signature generated by the spatiotemporal electromagnetic field mask. Next, we show how the second-order approximation generates hidden features not accessible in the first-order image. Finally, we apply the reconstruction algorithm to simulated and experimental data and discuss the performance, strengths, and limitations of this technique.
You’ll need a 64-bit CPU to test Microsoft’s latest OS.
To install Windows 11, you will need a 64-bit CPU, 4GB of RAM, and an internet connection.
