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Physicists create new electrically controlled silicon-based quantum device

A team of scientists at Simon Fraser University’s Quantum Technology Lab and leading Canada-based quantum company Photonic Inc. have created a new type of silicon-based quantum device controlled both optically and electrically, marking the latest breakthrough in the global quantum computing race.

The research, published in the journal Nature Photonics, reveals new diode nanocavity devices for electrical control over silicon color center qubits.

The devices have achieved the first-ever demonstration of an electrically-injected single-photon source in silicon. The breakthrough clears another hurdle toward building a quantum computer—which has enormous potential to provide computing power well beyond that of today’s supercomputers and advance fields like chemistry, materials science, medicine and cybersecurity.

How you make it matters: Spintronics device performance tied to atomic interface changes

Spintronics devices will be key to realizing faster and more energy-efficient computers. To give us a better understanding of how to make them, a Kobe University team now showed how different manufacturing techniques influence the material properties of a key component.

Electronic devices could be made more efficient and faster if electrons could carry more information at once. This is the basic idea behind spintronics, where researchers try to use the electrons’ spin in addition to charge in , processing and sensor devices to significantly improve our computers.

One component for such devices is the “,” which may be used, for example, for neuron-like behavior in information processing or in a new type of fast and non-volatile memory. They consist of two ferromagnets, usually a nickel-iron alloy, sandwiching a thin insulating layer such as graphene.

Engineers uncover why tiny particles form clusters in turbulent air

Tiny solid particles—like pollutants, cloud droplets and medicine powders—form highly concentrated clusters in turbulent environments like smokestacks, clouds and pharmaceutical mixers.

What causes these extreme clusters—which make it more difficult to predict everything from the spread of wildfire smoke to finding the right combination of ingredients for more effective drugs—has puzzled scientists.

A University at Buffalo study, published in Proceedings of the National Academy of Sciences, suggests the answer lies within the electric forces between particles.

Advanced AI links atomic structure to quantum tech

A research team led by Oak Ridge National Laboratory has developed a new method to uncover the atomic origins of unusual material behavior. This approach uses Bayesian deep learning, a form of artificial intelligence that combines probability theory and neural networks to analyze complex datasets with exceptional efficiency.

The technique reduces the amount of time needed for experiments. It helps researchers explore sample regions widely and rapidly converge on important features that exhibit interesting properties.

“This method makes it possible to study a material’s properties with much greater efficiency,” said ORNL’s Ganesh Narasimha. “Usually, we would need to scan a large region, and then several small regions, and perform spectroscopy, which is very time-consuming. Here, the AI algorithm takes control and does this process automatically and intelligently.”

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