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Category: computing – Page 317

Technophobia is an extreme fear of technology. People with technophobia may fear the power of artificial intelligence, robots or computers.

Technophobia is more than resistance to learning new technology. Rather, people with the condition may obsess over technology. Or, they may go to great lengths to avoid incorporating technology into their lives.

Technophobia is not a clinical diagnosis in the Diagnostic and Statistical Manual of Mental Disorders (DSM-5). Still, as technology has expanded rapidly in recent years, some clinicians treat technophobia like a specific phobia. Specific phobias are irrational fears of a particular situation, object, animal or interaction. The fear isn’t in proportion to the actual danger.

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UNSW Sydney researchers have developed a chip-scale method using OLEDs to image magnetic fields, potentially transforming smartphones into portable quantum sensors. The technique is more scalable and doesn’t require laser input, making the device smaller and mass-producible. The technology could be used in remote medical diagnostics and material defect identification.

Smartphones could one day become portable quantum sensors thanks to a new chip-scale approach that uses organic light-emitting diodes (OLEDs) to image magnetic fields.

Researchers from the ARC Centre of Excellence in Exciton Science at UNSW Sydney have demonstrated that OLEDs, a type of semiconductor material commonly found in flat-screen televisions, smartphone screens, and other digital displays, can be used to map magnetic fields using magnetic resonance.

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This is according to a press release by the institutions published on Thursday.

“We’ve come up with an unprecedented principle. Yes, the wood transistor is slow and bulky, but it does work, and has huge development potential,” said Isak Engquist, senior associate professor at the Laboratory for Organic Electronics at Linköping University.

This isn’t the first time scientists have attempted to produce wooden transistors but previous trials resulted in versions that could regulate ion transport only. Making matters worse was the fact that when the ions ran out, the transistor stopped functioning.

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Bright graphics, a touchscreen, a speech synthesizer, messaging apps, games, and educational software—no, it’s not your kid’s iPad. This is the mid-1970s, and you’re using PLATO.

Far from its comparatively primitive contemporaries of teletypes and punch cards, PLATO was something else entirely. If you were fortunate enough to be near the University of Illinois Urbana-Champaign (UIUC) around a half-century ago, you just might have gotten a chance to build the future. Many of the computing innovations we treat as commonplace started with this system, and even today, some of PLATO’s capabilities have never been precisely duplicated. Today, we’ll look back on this influential technological testbed and see how you can experience it now.

From space race to Spacewar.

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From early detection and internal treatment of diseases to futuristic applications like augmenting human memory, biological computing, or biocomputing, has the potential to revolutionize medicine and computers.

Traditional computer hardware is limited in its ability to interface with living organs, which has constrained the development of medical devices. Computerized implants require a constant supply of electricity, they can cause scarring in soft tissue that makes them unusable and they cannot heal themselves the way organisms can. Through the use of biological molecules such as DNA or proteins, biocomputing has the potential to overcome these limitations.

Biocomputing is typically done either with or with non-living, enzyme-free molecules. Live cells can feed themselves and can heal, but it can be difficult to redirect cells from their ordinary functions toward computation. Non-living molecules solve some of the problems of live cells, but have weak output signals and are difficult to fine-tune and regulate.

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A tiny vibrating crystal weighing little more than a grain of sand has become the heaviest object ever to be recorded in a superposition of locations.

Physicists at the Swiss Federal Institute of Technology (ETH) Zurich coupled a mechanical resonator to a type of superconducting circuit commonly used in quantum computing to effectively replicate Erwin Schrödinger’s famous thought experiment on an unprecedented scale.

Ironically, Schrödinger would be somewhat skeptical that anything so large – well, anything at all – could exist in a nebulous state of reality.

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Encoding breakthrough allows for solving wider set of applications using neutral-atom quantum computers. QuEra Computing and university researchers have developed a method to expand the optimization calculations possible with neutral-atom quantum computers. This breakthrough, published in PRX Quantum, overcomes hardware limitations, enabling solutions to more complex problems, thus broadening applications in industries like logistics and pharmaceuticals.

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Topological superconductors are superconducting materials with unique characteristics, including the appearance of so-called in-gap Majorana states. These bound states can serve as qubits, making topological superconductors particularly promising for the creation of quantum computing technologies.

Some physicists have recently been exploring the potential for creating that integrate superconductors with swirling configurations of atomic magnetic dipoles (spins), known as quantum crystals. Most of these efforts suggested sandwiching quantum skyrmion crystals between superconductors to achieve topological superconductivity.

Kristian Mæland and Asle Sudbø, two researchers at the Norwegian University of Science and Technology, have recently proposed an alternative model system of topological superconductivity, which does not contain superconducting materials. This theoretical model, introduced in Physical Review Letters, would instead use a sandwich structure of a heavy metal, a , and a normal metal, where the induces a quantum skyrmion crystal in the magnetic insulator.

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Advances in quantum computation for electronic structure, and particularly heuristic quantum algorithms, create an ongoing need to characterize the performance and limitations of these methods. Here we discuss some potential pitfalls connected with the use of hardware-efficient Ansätze in variational quantum simulations of electronic structure. We illustrate that hardware-efficient Ansätze may break Hamiltonian symmetries and yield nondifferentiable potential energy curves, in addition to the well-known difficulty of optimizing variational parameters. We discuss the interplay between these limitations by carrying out a comparative analysis of hardware-efficient Ansätze versus unitary coupled cluster and full configuration interaction, and of second-and first-quantization strategies to encode Fermionic degrees of freedom to qubits.

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