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Quantum memory devices can store data as quantum states instead of binary states, as classical computer memories do. While some existing quantum memory technologies have achieved highly promising results, several challenges will need to be overcome before they can be implemented on a large scale.

Researchers at the AWS Center for Quantum Networking and Harvard University have recently developed a promising capable of error detection and with a lifetime or coherence time (i.e., the time for which a quantum memory can hold a superposition without collapsing) exceeding 2 seconds. This memory, presented in a paper in Science, could pave the way towards the creation of scalable quantum networks.

Quantum networks are systems that can distribute entangled , or qubits, to users who are in different geographic locations. While passing through the networks, qubits are typically encoded as photons (i.e., single particles of light).

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UCLA researchers and their colleagues have discovered a new physics principle governing how heat transfers through materials, and the finding contradicts the conventional wisdom that heat always moves faster as pressure increases.

Up until now, the common belief has held true in recorded observations and involving different materials such as gases, liquids and solids.

The researchers detailed their discovery in a study published last week by Nature. They have found that boron arsenide, which has already been viewed as a highly promising material for heat management and advanced electronics, also has a unique property. After reaching an extremely high pressure that is hundreds of times greater than the pressure found at the bottom of the ocean, boron arsenide’s thermal conductivity actually begins to decrease.

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Cornell University researchers have created an interface that allows users to handwrite and sketch within computer code—a challenge to conventional coding, which typically relies on typing.

The pen-based , called Notate, lets users of computational, digital notebooks open drawing canvases and handwrite diagrams within lines of traditional, digitized .

Powered by a , the interface bridges handwritten and textual programming contexts: notation in the handwritten diagram can reference textual code and vice versa. For instance, Notate recognizes handwritten programming symbols, like “n”, and then links them up to their typewritten equivalents.

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Researchers at the UPC’s Department of Electronic Engineering have developed a new type of magnetometer that can be integrated into microelectronic chips and that is fully compatible with the current integrated circuits. Of great interest for the miniaturization of electronic systems and sensors, the study has been recently published in Microsystems & Nanoengineering.

Microelectromechanical systems (MEMS) are electromechanical systems miniaturized to the maximum, so much so that they can be integrated into a chip. They are found in most of our day-to-day devices, such as computers, car braking systems and mobile phones. Integrating them into has clear advantages in terms of size, cost, speed and energy efficiency. But developing them is expensive, and their performance is often compromised by incompatibilities with other electronic systems within a device.

MEMS can be used, among many others, to develop magnetometers—a device that measures to provide direction during navigation, much like a compass—for integration into smartphones and wearables or for use in the automotive industry. Therefore, one of the most promising lines of work are Lorentz force MEMS magnetometers.

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Researchers affiliated with the Q-NEXT quantum research center show how to create quantum-entangled networks of atomic clocks and accelerometers—and they demonstrate the setup’s superior, high-precision performance.

For the first time, scientists have entangled atoms for use as networked , specifically, atomic clocks and accelerometers.

The research team’s experimental setup yielded ultraprecise measurements of time and acceleration. Compared to a similar setup that does not draw on , their time measurements were 3.5 times more precise, and acceleration measurements exhibited 1.2 times greater precision.

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Benchmarks orient AI. They encapsulate ideals and priorities that describe how the AI community should progress. When properly developed and analyzed, they allow the larger community to understand better and influence the direction of AI technology. The AI technology that has evolved the most in recent years is foundation models, highlighted by the advent of language models. A language model is essentially a box that accepts text and generates text. Despite their simplicity, these models may be customized (e.g., prompted or fine-tuned) to a wide range of downstream scenarios when trained on vast amounts of comprehensive data. However, there still needs to be more knowledge on the enormous surface of model capabilities, limits, and threats. They must benchmark language models holistically due to their fast growth, growing importance, and limited comprehension. But what does it mean to evaluate language models from a global perspective?

Language models are general-purpose text interfaces that may be used in various circumstances. And for each scenario, they may have a long list of requirements: models should be accurate, resilient, fair, and efficient, for example. In truth, the relative relevance of various desires is frequently determined by one’s perspective and ideals and the circumstance itself (e.g., inference efficiency might be of greater importance in mobile applications). They think that holistic assessment includes three components:

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UC San Diego nanoengineering professor Shyue Ping Ong described M3GNet as “an AlphaFold for materials”, referring to the breakthrough AI algorithm built by Google’s DeepMind that can predict protein structures.

“Similar to proteins, we need to know the structure of a material to predict its properties,” said Professor Ong.

“We truly believe that the M3GNet architecture is a transformative tool that can greatly expand our ability to explore new material chemistries and structures.”

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