Pfizer’s COVID-19 vaccine uses a gene-based platform that hasn’t been used for approved human vaccines. The reported high efficacy is a good sign for other vaccines using that approach.
A major technical challenge for any practical, real-world quantum computer comes from the need for a large number of physical qubits to deal with errors that accumulate during computation. Such quantum error correction is resource-intensive and computationally time-consuming. But researchers have found an effective software method that enables significant compression of quantum circuits, relaxing the demands placed on hardware development.
Quantum computers may still be far from a commercial reality, but what is termed ‘quantum advantage’—the ability of a quantum computer to compute hundreds or thousands of times faster than a classical computer-has indeed been achieved on what are called Noisy Intermediate-Scale Quantum (NISQ) devices in early proof-of-principle experiments.
Unfortunately, NISQ devices are still prone to lots of errors that accumulate during their operation. For there to be any real-world application of quantum advantage, the design of a fully operational large-scale quantum computer with high error tolerance is required. Currently, NISQ devices can be engineered with approximately 100 qubits, but fault-tolerant computers would need millions of physical qubits at the very least to encode the logical information with sufficiently low error rates. A fault-tolerant implementation of quantum computational circuits not only makes the quantum computer larger, but also the runtime longer by orders of magnitude. An extended runtime itself in turn means the computation is even more susceptible to errors.
Solar power stations in space that beam ‘emission-free electricity’ down to Earth could soon be a reality thanks to a UK government funded project.
Above the Earth there are no clouds and no day or night that could obstruct the sun’s ray – making a space solar station a constant zero carbon power source.
Continue reading “Space solar power station a step closer thanks to government project” »
A new study outlines ways colleges and universities can update their curricula to prepare the workforce for a new wave of quantum technology jobs. Three researchers, including Rochester Institute of Technology Associate Professor Ben Zwickl, suggested steps that need to be taken in a new paper in Physical Review Physics Education Research after interviewing managers at more than 20 quantum technology companies across the U.S.
The study’s authors from University of Colorado Boulder and RIT set out to better understand the types of entry-level positions that exist in these companies and the educational pathways that might lead into those jobs. They found that while the companies still seek employees with traditional STEM degrees, they want the candidates to have a grasp of fundamental concepts in quantum information science and technology.
“For a lot of those roles, there’s this idea of being ‘quantum aware’ that’s highly desirable,” said Zwickl, a member of RIT’s Future Photon Initiative and Center for Advancing STEM Teaching, Learning and Evaluation. “The companies told us that many positions don’t need to have deep expertise, but students could really benefit from a one- or two-semester introductory sequence that teaches the foundational concepts, some of the hardware implementations, how the algorithms work, what a qubit is, and things like that. Then a graduate can bring in all the strength of a traditional STEM degree but can speak the language that the company is talking about.”
Sophomore math major Xzavier Herbert was never much into science fiction or the space program, but his skills in pure mathematics seem to keep drawing him into NASA’s orbit.
With an interest in representation theory, Herbert spent the summer virtually at NASA, studying connections between classical information theory and quantum information theory, each of which corresponds to a different set of laws: classical physics and quantum mechanics.
“What I’m doing involves how representation theory allows us to draw a direct analog from classical information theory to quantum information theory,” Herbert says. “It turns out that there is a mathematical way of justifying how these are related.”
A team of researchers from Delft University of Technology (TU Delft), Leiden University, Tohoku University and the Max Planck Institute for the Structure and Dynamics of Matter has developed a new type of MRI scanner that can image waves in ultrathin magnets. Unlike electrical currents, these so-called spin waves produce little heat, making them promising signal carriers for future green ICT applications.
MRI scanners can look into the human body in a non-invasive manner. The scanner detects the magnetic fields radiated by the atoms inside, which makes it possible to study the health of organs even though they are hidden underneath thick layers of tissue.
The non-invasive, see-through power of MRI is desirable for many research fields and industries. It could be particularly useful as an imaging tool in nanotechnology and the chip industry. Being able to detect signals in computer chips and other nanodevices would facilitate optimizing their performance and reducing their heat production. However, the millimeter resolution of conventional MRI is insufficient to study chip-scale devices. A team of researchers led by TU Delft have now developed a new method for sensing magnetic waves at the sub-micrometer scale.
There are no guarantees all those rockets will get off the ground on time, however.
The planned launch of a U.S. spy satellite this afternoon (Nov. 13) could kick off a binge of four liftoffs in four days, if we’re lucky.
The National Reconnaissance Office’s classified NROL-101 spacecraft is scheduled to launch atop a United Launch Alliance (ULA) Atlas V rocket at 5:13 p.m. EST (2213 GMT) today from Cape Canaveral Air Force Station in Florida. You can watch that mission live here at Space.com, courtesy of ULA, or directly via the company.
https://www.youtube.com/watch?v=zupk6Oegbb0&feature=youtu.be
Han from WrySci HX goes through the results of the 2020 Human Augmentation Survey, and explains why these types of surveys are important for the future. More below ↓↓↓
Follow me on twitter: https://twitter.com/han_xavier_
Continue reading “Would You Upgrade? Results from the 2020 Human Augmentation Survey!” »
Learned optimizers are algorithms that can be trained to solve optimization problems. Although learned optimizers can outperform baseline optimizers in restricted settings, the ML research community understands remarkably little about their inner workings or why they work as well as they do. In a paper currently under review for ICLR 2021, a Google Brain research team attempts to shed some light on the matter.
The researchers explain that optimization algorithms can be considered the basis of modern machine learning. A popular research area in recent years has focused on learning optimization algorithms by directly parameterizing and training an optimizer on a distribution of tasks.
Research on learned optimizers aims to replace the baseline “hand-designed” optimizers with a parametric optimizer trained on a set of tasks, which can then be applied more generally. In contrast to baseline optimizers that use simple update rules derived from theoretical principles, learned optimizers use flexible, high-dimensional, nonlinear parameterizations.
