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Category: computing – Page 16

A new ‘library’ for Feynman integrals

Theoretical physicists at Johannes Gutenberg University Mainz (JGU) have developed a new method of ordering Feynman integrals. This critical step in making theoretical predictions for high-energy precision measurements has posed a major computational bottleneck until now.

Scientists in the research group of Professor Stefan Weinzierl from the PRISMA⁺⁺ Cluster of Excellence propose a solution to this longstanding challenge in new articles published in Physical Review Letters and Physical Review D. By ordering the integrals according to their intrinsic geometric properties, they can speed computation times by a factor of about 1,000.

“Feynman integrals are mathematical expressions that researchers must evaluate to make precise predictions,” said Weinzierl. “These are the first pillars for precise predictions for measurements at facilities like the Large Hadron Collider in Switzerland.” The number of these integrals varies from process to process, with some processes needing up to one million.

Scientists achieve all-electrical control of single-molecule quantum states

Quantum technologies promise revolutionary advances in computing, sensing and information processing. However, controlling individual quantum bits (qubits) at the atomic scale remains a major challenge because conventional approaches rely on magnetic fields, which are difficult to confine to a single molecule.

A research team at the Center for Quantum Nanoscience (QNS), led by Director Andreas Heinrich at the Institute for Basic Science (IBS), together with collaborators at the Karlsruhe Institute of Technology (KIT), has demonstrated that the quantum state of an individual magnetic molecule can instead be controlled electrically using a newly identified exchange-mediated mechanism. The study published in Nature Physics provides a new strategy for electrically controlling molecular quantum systems and could help pave the way for more scalable quantum technologies.

Magnetic molecules are considered attractive building blocks for future quantum technologies because they are only a few nanometers in size, can self-assemble into ordered structures and can be chemically tailored to possess desired quantum properties. These characteristics make them promising candidates for molecular quantum computing, quantum sensing and spintronic applications.

New computational imaging method cuts X-ray dose while preserving high resolution

Researchers have shown that it’s possible to take clear, high-resolution X-ray images using very little radiation. With more development, the new approach could eventually make medical X-ray diagnostics less risky and more accessible.

“While traditional X-ray imaging relies on enough X-ray photons reaching a detector to form a clear image, our approach uses computational techniques to reconstruct an image from fewer photons,” said research team leader Tiqiao Xiao from the Shanghai Advanced Research Institute, Chinese Academy of Sciences. “We were able to show the low-dose potential of this approach by achieving megapixel radiology with ultra-low-light.”

In Optica, the researchers demonstrate X-ray ghost images with nearly 2-megapixel resolution using only 0.48% of the X-ray photons typically required for X-ray imaging. The proof-of-concept study suggests that comparable X-ray image quality may eventually be achievable with far lower radiation doses than are used today.

Braided, exotic particles could build reliable, universal quantum computers

A truly useful quantum computer must be able to run any algorithm, with the same versatility an ordinary laptop offers. Physicists have now shown a new way to give a quantum computer exactly that flexibility, harnessing the capabilities of exotic quantum particles called non-Abelian anyons.

A team of scientists from the University of Chicago Pritzker School of Molecular Engineering (UChicago PME), Harvard, Stony Brook University and Quantinuum built and tested a complete toolkit of operations using non-Abelian anyons, proving for the first time the broad utility of this approach.

“We demonstrated a so-called universal gate set—meaning that if you store information in these emergent versions of quarks, and you move them around, you can do any quantum computation you might want to do,” said Ruben Verresen, assistant professor of molecular engineering at UChicago PME and a co-author of the new study published in Nature.

Implant helps paralyzed man to feed himself and drink from a cup

A neuroprosthetic system has helped a man with paralysis move his hand and feel touch again following a spinal cord injury, reports research published in Nature Medicine. Some of the system’s benefits continued even when the device was turned off, suggesting that it may support longer-term recovery as well as help movement in real time.

Spinal cord injury is a leading cause of paralysis, and more than half of cases involve tetraplegia, in which movement of the arms and legs is affected. Complete spinal cord injuries, in which there is no voluntary movement or feeling below the level of the injury, are particularly difficult to treat. Previous brain–computer interface systems have helped restore some movement but have not yet restored a sense of touch or supported longer-term recovery.

Chad Bouton and colleagues developed a “double neural bypass” system that reads brain signals linked to a person’s intention to move. It then uses these signals to help control a person’s own hand by delivering targeted stimulation to the spinal cord and the part of the brain involved in touch, the primary somatosensory cortex.

They Killed the Transistor As We Know It

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Structural shifts and constraints in animalbased neuroscience

Animal models have long been central to neuroscience, providing direct experimental access to neural processes underlying perception, action, cognition, and disease. Over the past century, work in non-human primates (NHPs), rodents, and other species has established key principles of neural organization and behavior and has supported much of translational neuroscience. However, the institutional and material conditions that sustain animal-based research are now changing in fundamental ways. Ethical and regulatory requirements have intensified, costs and approval timelines have increased, and global supply chains, particularly for NHPs, have become fragile. In parallel, advances in human neuroscience, stem-cell-derived systems, and computational approaches have matured to the point that they challenge the historical reliance on animals for many classes of questions. These forces are not eliminating animal research, but they are reshaping the conditions under which it remains feasible, competitive, and scientifically justified. In this Perspective, we examine how these converging pressures are reconfiguring animal-based neuroscience. We review long-term trends in animal use and accessibility, highlighting species-specific constraints and emerging geopolitical asymmetries. We then analyze the growing role of alternative and complementary platforms, including human brain organoids, genetically engineered rodents, small primates, and ‘human-centric’ neurophysiological and imaging approaches, emphasizing both their strengths and limitations. Finally, we discuss the implications of this diversification for research planning, training, and scientific organization. We argue that the future of neuroscience will be defined not by the disappearance of animal models, but by their integration into hybrid experimental frameworks that preserve mechanistic rigor while adapting to evolving scientific and societal constraints.

Keywords: animal models; neuroscience methodology; alternative experimental platforms; translational validity; research ethics and regulation.

Sensitive measurements uncover dual superconducting states in atom-thin NbSe₂ and TaS₂

A new study reveals that two widely studied ultrathin superconducting materials are more sophisticated than they appear. Although they seem to behave like simple superconductors with a single energy gap, they actually contain two strongly interacting superconducting states that work together and disguise themselves as one. This finding resolves a long-standing mystery about how these materials behave, providing new insight into superconductivity that could help scientists design better superconducting materials for future technologies such as quantum computers, ultra-efficient electronics and advanced sensors.

Sometimes, the biggest scientific discoveries come from looking more closely at something we thought we already understood. For decades, physicists have studied a remarkable class of materials called superconductors—materials that can carry electricity with zero energy loss. These materials could one day help power ultra-efficient electronics, quantum computers and advanced medical technologies.

One of the most widely studied superconductors, niobium diselenide (NbSe₂), seemed straightforward when peeled down to just a few atomic layers. Experiments suggested it behaved like a superconductor with a single energy gap—a fundamental fingerprint that describes how electrons order in pairs to flow without resistance.

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