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Category: supercomputing

Quantum computers model nine fusion fuel material configurations for first time

A team of scientists from Oak Ridge National Laboratory, Cleveland Clinic and IBM has calculated nine molecular configurations of a promising material to produce fuel for fusion energy—the first known instance of such computations on quantum computers.

Such calculations, demonstrated in a new paper published on the arXiv preprint server, are computationally challenging for classical computers to scale when working alone. They are a fundamental step toward optimizing the production and extraction of tritium—an extremely rare material in nature that is necessary to produce fusion energy with most of the proposed machines. Ensuring adequate supplies of tritium has long been a barrier to realizing the promise of clean, abundant energy from fusion power plants, and solving this issue is a key objective of the U.S. Department of Energy’s Genesis Mission.

Quantum computers are well-suited to computing the atomic-level chemistry of a liquid salt that contains fluorine, lithium and beryllium (FLiBe), one of the leading candidate materials for extracting tritium fuel in fusion reactors. To compute different configurations of clusters of FLiBe, the team used the same quantum-centric supercomputing techniques now being applied to 12,635-atom protein simulations with Cleveland Clinic. These methods can calculate the quantum behavior of electrons in complex materials, complementing and enhancing the capabilities of classical supercomputers and algorithms.

Quantum computer simulates hadronization, reproducing string breaking with 104 qubits

By remotely accessing an IBM quantum computer, a research scientist at Lawrence Berkeley National Laboratory has successfully simulated a key process in particle physics: hadronization. Although based on a simplified model of quantum mechanics, the project lays the groundwork for how physicists can leverage the power of quantum computers to make large scientific calculations beyond the capabilities of classical supercomputers. The research is published in the journal Physical Review D.

Hadronization occurs when two or more quarks—the subatomic building blocks of matter—bind together through the strong nuclear force to form composite particles called hadrons. The most familiar examples of hadrons are protons and neutrons, which form the nuclei of atoms. So, having a better understanding of the hadronization process means having a better understanding of the structure of matter, and—in turn—the universe.

Physical experiments have not been able to reveal every step of the process, however. Researchers at the Large Hadron Collider (LHC) at CERN accelerate protons to near light speeds, guide them into collisions and study the resulting debris of quarks and antiquarks. But these particles can only be indirectly measured before they immediately undergo hadronization—hence the need for computer simulations to fill in the gaps of these scientific observations.

New Wright-Patt supercomputer calculates in a day what would take average laptop 500 years

Wright Patterson Air Force Base has a new advanced problem solver for future military systems and weapons. It’s called the Flyer, named in honor of Wilbur and Orville Wright and their research in aerodynamics.

The Flyer is the Pentagon’s latest supercomputer. It has more than 186,000 cores able to process millions of advanced calculations in a few seconds.

David Shahady, deputy director of the Air Force Research Laboratory’s Digital Capabilities Directorate, equated the abilities of this unit to a pop-culture sci-fi character.

Germany’s New Photonic NPU Just Made NVIDIA’s Billion Dollar GPUs Look Like TRASH!

Photonic chips are no longer just a lab experiment, and in this video, we break down why a new photonic NPU could become one of the biggest shifts in AI hardware, data centers, and supercomputing. Instead of using electricity and transistors like a traditional GPU, this new class of processor uses light to perform computation, opening the door to dramatically faster matrix math, far lower energy use, and almost no on-chip heat. From the growing power crisis in AI infrastructure to the limits of silicon, Moore’s Law, and the memory wall, this story explores why photonic computing is suddenly becoming one of the most important technologies to watch. If you’re interested in photonic chips, optical computing, AI chips, NPUs, GPUs, data center efficiency, and the future of semiconductor technology, this video gives you the full picture. We also explore what makes these chips different from conventional silicon. The video covers photons instead of electrons, wavelength-division multiplexing, optical interference, thin-film lithium niobate, and why companies like Q.ANT are now deploying photonic processors in real supercomputing environments instead of just talking about them on research slides. We look at Q.ANT’s Native Processing Unit at the Leibniz Supercomputing Centre in Germany, the jump from first-generation to second-generation performance, and why benchmarks showing huge gains in throughput, AI inference, and energy efficiency are making people take photonic hardware much more seriously. More importantly, this is not just another faster chip story. It is about whether the AI industry can keep scaling without running straight into an energy wall. With GPUs demanding more power, more cooling, and more data movement every year, photonic co-processors may be the first real alternative that changes the economics of compute itself. The technology still has serious challenges, especially memory and optical-electrical conversion, but this may be the moment when computing with light stopped sounding like science fiction and started becoming real infrastructure.

This Lab Just Proved Quantum Beats Supercomputers — Permanently?

A new scientific breakthrough has reignited one of the biggest debates in modern computing after researchers announced results suggesting that a quantum computer outperformed a classical supercomputer on a highly specialized task. The findings have fueled discussions about whether the era of \.

China Takes Supercomputer Crown From U.S. For First Time Since 2017

China took back a coveted computing crown from the United States on Tuesday, ratcheting up a fierce technological competition that has implications for science, national security and geopolitics.

LineShine, a massive computing system in Shenzhen, China, was declared the world’s fastest by a group of researchers using a set of standard tests for supercomputers. Besides raw speed, the system stood out because it uses only standard microprocessors and not the special-purpose chips called graphics processing units, which most high-end supercomputers rely on for heavy number crunching.

That underlying design could point to a better way to blend artificial intelligence with traditional scientific tasks, said Jack Dongarra, an organizer of the so-called Top500 list of the world’s most powerful supercomputers.

Chinese supercomputer displaces US machines as world’s fastest for first time since 2017

A supercomputer in China now outranks its U.S. counterparts as the world’s most powerful, marking the first time since 2017 that a Chinese computer has topped a list sometimes viewed as a measure of a nation’s technological prowess.

The LineShine computer in Shenzhen, China, displaced top-ranked U.S. computer El Capitan in the latest version of the TOP500 ranking announced Tuesday. It was the Chinese computer’s debut on the list.

Scientists behind the TOP500 project said the LineShine computer at China’s National Supercomputing Center achieved 2.198 exaflops, meaning it can perform more than 2 quintillion calculations per second.

Supercomputer illuminates subatomic particle that helps hold matter together

A team of researchers has leveraged a supercomputer at the U.S. Department of Energy’s (DOE) Argonne National Laboratory to reveal the internal structure of a pion in unprecedented detail. The findings are published in the Journal of High Energy Physics.

Pions are subatomic particles that help bind matter at some of the smallest scales in nature. They are closely connected to the strong nuclear force, the fundamental force that holds protons and neutrons together inside atomic nuclei. Understanding how pions work can help scientists explain how matter forms at its most fundamental level.

“Pions mediate the strong force that binds nucleons—that is, the protons and neutrons that account for an atom’s mass,” said Yong Zhao, an Argonne physicist and principal investigator on the project.

AI helps reveal large-scale quantum effects hidden in stacked atomic sheets

Quantum materials are a class of exotic materials with special properties that are governed by quantum mechanics rather than classical physics. Those properties—like superconductivity, entanglement and unusual forms of magnetism—often originate in the tiny repeating patterns of atoms inside crystals, but through clever engineering, they can be observed and controlled at a more human scale. Quantum materials are helping to power the quickly growing field of quantum computing and could find their way into future generations of energy-efficient electronics.

Designing new materials from the atomic scale up, however, requires intense modeling and simulation. Some materials may appear ordinary when viewed as small clusters of atoms, yet reveal new and useful properties when their atomic building blocks repeat and interact over larger distances. Researchers must be able to accurately predict behaviors at large scales in order to find materials with practical applications—otherwise, designing new materials is a slow and costly trial-and-error process.

In the past 50 years, supercomputers have helped materials scientists solve some of those thorny prediction problems, but two recent studies from the University of Washington demonstrate how newer computing techniques can help researchers sniff out promising quantum materials to pursue.

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