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Category: computing – Page 114

CU Denver Develops Quantum Tool that May Lead to Gamma-Ray Lasers and Access the Multiverse

Sahai has found a way to create extreme electromagnetic fields never before possible in a laboratory. These electromagnetic fields—created when electrons in materials vibrate and bounce at incredibly high speeds—power everything from computer chips to super particle colliders that search for evidence of dark matter. Until now, creating fields strong enough for advanced experiments has required huge, expensive facilities.

For example, scientists chasing evidence of dark matter use machines like the Large Hadron Collider at CERN, the European Organization for Nuclear Research, in Switzerland. To accommodate the radiofrequency cavities and superconducting magnets needed for accelerating high energy beams, the collider is 16.7 miles long. Running experiments at that scale demands huge resources, is incredibly expensive, and can be highly volatile.

Sahai developed a silicon-based, chip-like material that can withstand high-energy particle beams, manage the energy flow, and allow scientists to access electromagnetic fields created by the oscillations, or vibrations, of the quantum electron gas—all in a space about the size of your thumb.

The rapid movement creates the electromagnetic fields. With Sahai’s technique, the material manages the heat flow generated by the oscillation and keeps the sample intact and stable. This gives scientists a way to see activity like never before and opens the possibility of shrinking miles-long colliders into a chip.

A University of Colorado Denver engineer is on the cusp of giving scientists a new tool that can help them turn sci-fi into reality.

Imagine a safe gamma ray laser that could eradicate cancer cells without damaging healthy tissue. Or a tool that could help determine if Stephen Hawking’s multiverse theory is real by revealing the fabric underlying the universe.

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Researchers visualize crystal phase changes particle by particle in new simulations

The secret to how steel hardens and shape-memory alloys snap into place lies in rapid, atomic-scale shifts that scientists have struggled to observe in materials. Now, Cornell researchers are revealing how these transformations unfold, particle by particle, through advanced modeling techniques.

Using custom-built computer simulations, Julia Dshemuchadse, assistant professor of and engineering at Cornell Engineering, and Hillary Pan, Ph.D., have visualized solid-solid phase transitions in unprecedented detail, capturing the motion of every particle in a theoretical material as its crystal structure morphs into another.

Their findings, published in the Proceedings of the National Academy of Sciences, reveal not only classical transformation mechanisms, but also entirely new ones, reshaping how scientists understand this fundamental process in materials science.

Researchers demonstrate modular approach for building scalable quantum computers

What do children’s building blocks and quantum computing have in common? The answer is modularity.

It is difficult for scientists to build quantum computers monolithically—that is, as a single large unit. Quantum computing relies on the manipulation of millions of information units called qubits, but these qubits are difficult to assemble. The solution? Finding modular ways to construct quantum computers. Like plastic children’s bricks that lock together to create larger, more intricate structures, scientists can build smaller, higher-quality modules and string them together to form a comprehensive system.

Recognizing the potential of these modular systems, researchers from The Grainger College of Engineering at the University of Illinois Urbana-Champaign have presented an enhanced approach to scalable quantum computing by demonstrating a viable and high-performance modular architecture for superconducting quantum processors.

New research fuels the future of data storage: Predicting spin accumulation for faster, greener memory

Researchers from SANKEN (The Institute of Scientific and Industrial Research) at The University of Osaka have developed a new program, “postw90-spin,” that enables high-precision calculations of a novel performance indicator for the spin Hall effect, a phenomenon crucial for developing energy-efficient and high-speed next-generation magnetic memory devices.

This breakthrough addresses a long-standing challenge in spintronics research by providing a definitive measure of the spin Hall effect, overcoming ambiguities associated with traditional metrics. The research is published in the journal npj Spintronics.

The spin Hall effect, where many researchers recognize an generates a perpendicular , is key to devices. Previously, the spin Hall conductivity was used as a performance indicator. However, this metric is affected by how the spin current is defined, leading to inconsistencies.

New reconfigurable memristor-based system enables in-memory data sorting

Organizing data in a specific order, also known as sorting, is a central computing operation performed by a wide range of systems. Conventional hardware systems rely on separate components to store and sort data, which limits their speed and energy efficiency.

Researchers at Peking University have recently developed a new reconfigurable sort-in-memory system that relies on memristors to in-situ sort stored data. Their proposed system, outlined in a paper published in Nature Electronics and led by Professor Yuchao Yang, was found to store and sort data both quickly and energy-efficiently.

“The original idea comes from the fact that although operations like matrix multiplication and convolution have been widely implemented in CIM (Computing-in-Memory) systems, sorting has long been regarded as a ‘hard nut to crack’ in computing-in-memory technology due to its unique computational characteristics,” Yaoyu Tao, corresponding author of the paper, told TechXplore.

Gold clusters mimic atomic spin properties for scalable quantum computing applications

The efficiency of quantum computers, sensors and other applications often relies on the properties of electrons, including how they are spinning. One of the most accurate systems for high-performance quantum applications relies on tapping into the spin properties of electrons of atoms trapped in a gas, but these systems are difficult to scale up for use in larger quantum devices like quantum computers.

Now, a team of researchers from Penn State and Colorado State has demonstrated how a gold cluster can mimic these gaseous, trapped atoms, allowing scientists to take advantage of these spin properties in a system that can be easily scaled up.

“For the first time, we show that have the same key spin properties as the current state-of-the-art methods for quantum information systems,” said Ken Knappenberger, department head and professor of chemistry in the Penn State Eberly College of Science and leader of the research team.

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