Lifeboat Foundation

This model of computing would use 1/1000th of the energy today’s computers do. So why aren’t we using it?

A new electrical method to conveniently change the direction of electron flow in some quantum materials could have implications for the development of next-generation electronic devices and quantum computers.

A team of researchers from Penn State developed and demonstrated the method in materials that exhibit the quantum anomalous Hall (QAH) effect—a phenomenon in which the flow of electrons along the edge of a material does not lose energy. The team described the work in a paper in the journal Nature Materials.

“As electronic devices get smaller and computational demands get larger, it is increasingly important to find ways to improve the efficiency of information transfer, which includes the control of electron flow,” said Cui-Zu Chang, Henry W. Knerr Early Career Professor and associate professor of physics at Penn State and co-corresponding author of the paper. “The QAH effect is promising because there is no energy loss as electrons flow along the edges of materials.”

The super-special material graphene continues to surprise and fascinate scientists, this time revealing a rare electronic state termed ‘ferro-valleytricity’, which occurs when graphene is stacked up in a particular five-layer combination.

When in this new state, the graphene stack exhibits weird and wonderful magnetic and electronic behavior, as reported by researchers from the Massachusetts Institute of Technology (MIT), Harvard University, and the National Institute for Materials Science in Japan.

Using graphene in this way could help in the development of both classical and quantum computers, according to the team, especially in terms of creating data storage solutions that offer large capacities but that also need relatively little energy to run.

What makes a quantum computer more powerful than a classical computer? It’s a surprisingly subtle question that physicists are still grappling with, decades into the quantum age.

As Shor looked for applications for his quantum period-finding algorithm, he rediscovered a previously known but obscure mathematical theorem: For every number, there exists a periodic function whose periods are related to the number’s prime factors. So if there’s a number you want to factor, you can compute the corresponding function and then solve the problem using period finding — “exactly what quantum computers are so good at,” Regev said.

On a classical computer, this would be an agonizingly slow way to factor a large number — slower even than trying every possible factor. But Shor’s method speeds up the process exponentially, making period finding an ideal way to construct a fast quantum factoring algorithm.

Shor’s algorithm was one of a few key early results that transformed quantum computing from an obscure subfield of theoretical computer science to the juggernaut it is today. But putting the algorithm into practice is a daunting task, because quantum computers are notoriously susceptible to errors: In addition to the qubits required to perform their computations, they need many others doing extra work to keep them from failing. A recent paper by Ekerå and the Google researcher Craig Gidney estimates that using Shor’s algorithm to factor a security-standard 2,048-bit number (about 600 digits long) would require a quantum computer with 20 million qubits. Today’s state-of-the-art machines have at most a few hundred.

From every study they could find, including research that was never published, research by the military and private businesses, and research that had sat dormant on hard drives for decades to find out how personality and intelligence relate to each another.⁠
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Fourteen years later, the massive data catalog has dropped. It contains 79 personality traits and 97 cognitive abilities from 1,300 studies from over 50 countries including over 2 million participants. And an early meta-analysis published in the Proceedings of the National Academy of Sciences shows that personality and intelligence relate in some surprising ways.⁠
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Personality describes how someone generally thinks, feels, and behaves. Intelligence (termed cognitive ability by the researchers) describes how well someone can understand and apply information.⁠
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Here are 3 of the 5 findings:⁠
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1. Extraversion, a measure of sociality and enthusiasm, was only negligibly related to intelligence overall. However, the activity facet more strongly correlated, and (surprisingly) sociability had a small negative relationship with some cognitive abilities. ⁠
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2. Neuroticism encompasses negative emotionality, which can inhibit advanced thinking. Despite the trope of the moody genius, perhaps it’s no surprise that higher levels of neuroticism predicted lower levels of intelligence, albeit weakly. The uneven temper and depression facets were particularly strong predictors of decreased intelligence. ⁠
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3. Conscientiousness, a measure of self-regulation and orderliness, correlated positively with intelligence overall. But some facets, including cautiousness and routine seeking, predicted lower cognitive abilities.⁠
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For the rest of the findings, along with something interesting they learned about extraversion, click here: https://www.freethink.com/society/study-personality-intellig…jjjrtebdkm.

Article by Elizabeth Gilbert.

There is an intense, worldwide search for novel materials to build computer microchips with that are not based on classic transistors but on much more energy-saving, brain-like components. However, whereas the theoretical basis for classic transistor-based digital computers is solid, there are no real theoretical guidelines for the creation of brain-like computers.

Such a theory would be absolutely necessary to put the efforts that go into engineering new kinds of microchips on solid ground, argues Herbert Jaeger, Professor of Computing in Cognitive Materials at the University of Groningen.

Computers have, so far, relied on stable switches that can be off or on, usually transistors. These digital computers are logical machines and their programming is also based on logical reasoning. For decades, computers have become more powerful by further miniaturization of the transistors, but this process is now approaching a physical limit. That is why scientists are working to find new materials to make more versatile switches, which could use more values than just the digitals 0 or 1.

Microsoft has decided its Project Silica storage would be an efficient and sustainable choice for its cloud data centers, with the 7 TB glass media touted to last 10,000 years.

We live in an era of data deluge. The data centers that are operated to store and process this flood of data use a lot of electricity, which has been called a major contributor to environmental pollution. To overcome this situation, polygonal computing systems with lower power consumption and higher computation speed are being researched, but they are not able to handle the huge demand for data processing because they operate with electrical signals, just like conventional binary computing systems.

Dr. Do Kyung Hwang of the Center for Opto-Electronic Materials & Devices of the Korea Institute of Science and Technology (KIST) and Professor Jong-Soo Lee of the Department of Energy Science & Engineering at Daegu Gyeongbuk Institute of Science and Technology (DGIST) have jointly developed a new zero-dimensional and two-dimensional (2D-0D) semiconductor artificial junction material and observed the effect of a next-generation memory powered by light.

Transmitting data between the computing and storage parts of a multi-level computer using light rather than electrical signals can dramatically increase processing speed.