Researchers repeated a key measurement of the fundamental constant G, but the results remain inconsistent, highlighting the difficulty of putting gravity on the proverbial scale.
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Quantum Error Correction Faces Another Hurdle
Newly identified correlated errors in superconducting qubits could limit the performance of error-correction schemes needed for a practical quantum computer.
Building a working quantum computer is challenging because its basic components, qubits, are highly sensitive to environmental disturbances that compromise computation. Whereas classical bits can only undergo bit-flip errors that change 0 to 1 or vice versa, qubits also suffer from so-called phase errors that degrade the fundamental quantum interference effects essential for quantum computation. Joining several good, but not perfect, physical qubits into a logical qubit makes quantum error correction possible (see Research News: Cracking the Challenge of Quantum Error Correction). But that strategy can fail if too many qubits become faulty at the same time. In one leading hardware platform, superconducting circuits, such correlated qubit errors are typically triggered every few tens of seconds when ionizing radiation from the environment deposits energy into the chip hosting the circuits.
Optically dark gamma-ray burst reveals an unusually wide jet
Using various telescopes, an international team of astronomers has performed multi-wavelength observations of a recently identified gamma-ray burst source designated GRB 250416C. Results of the observational campaign, published April 23 on the v pre-print server, could help us better understand the nature of GRB 250416C and gamma-ray bursts in general.
Gamma-ray bursts (GRBs) are the most powerful electromagnetic explosions in the universe, usually caused by the destruction of massive stars. In general, they are observed as bursts of highly energetic gamma rays lasting from less than a second to several minutes.
Beyond borders: Metaverse manufacturing envisions AI-linked local production built on digital twins
Over the past decades, technological advances have fueled great innovation in a wide range of fields. Emerging and rapidly developing technologies, such as artificial intelligence (AI) systems, three-dimensional (3D) and four-dimensional (4D) printing, digital twins (i.e., virtual representations of physical objects, systems or processes) and advanced robots, are set to further transform many industries and sectors.
Researchers at London South Bank University explored the idea of metaverse manufacturing, an industrial ecosystem that would blend technology-enhanced physical production processes with immersive visual environments. In a paper published in Journal of the Royal Society Interface, they tried to envision how this ecosystem could work and what technologies it would rely on, while also considering its possible advantages in terms of sustainability and productivity.
The study was conducted within the Mechanical Intelligence (MI) Research Group at London South Bank University, which focuses on bioinspired design and adaptive engineering systems.
After a 40-year wait, technology finally enables three-sided zipper design
In 1985, the Innovative Design Fund placed an ad in Scientific American offering up to $10,000 to support clever prototypes for clothing, home decor, and textiles. William Freeman Ph.D., then an electrical engineer at Polaroid and now an MIT professor, saw it and submitted a novel idea: a three-sided zipper. Instead of fastening pants, it’d be like a switch that seamlessly flipped chairs, tents, and purses between soft and rigid states, making them easier to pack and put together.
Freeman’s blueprint was much like a regular zipper, except triangular. On each side, he nailed a belt to connect narrow wooden “teeth” together. A slider wrapping around the device could be moved up to fasten the three strips into place, straightening them into a triangular tube. His proposal was rejected, but Freeman patented his prototype and stored it in his garage in the hopes it might come in handy one day.
Nearly 40 years later, MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) researchers wanted to revive the project to create items with “tunable stiffness.” Prior attempts to adjust that weren’t easily reversible or required manual assembly, so CSAIL built an automated design tool and adaptable fastener called the “Y-zipper.” The scientists’ software program helps users customize three-sided zippers, which it then builds on its own in a 3D printer using plastics. These devices can be attached or embedded into camping equipment, medical gear, robots, and art installations for more convenient assembly.
DNA-reading AI reconstructs ancestry in minutes, matching top statistical methods
Researchers at the University of Oregon have developed an artificial intelligence tool that can read genetic code the way large language models like ChatGPT read text. Scanning the genome for biological mutation patterns, the computer model traces pairs of genes back in time to their last common ancestor.
It’s the first language model designed for population genetics, said Andrew Kern, a computational biologist in the UO College of Arts and Sciences. As described in a paper published April 10 in the Proceedings of the National Academy of Sciences, the AI tool offers scientists a fast and flexible alternative to classical methods for reconstructing evolutionary history.
In practice, it can help researchers like Kern understand when disease-resistance genes emerged in a population, for example, or when species evolved key traits.
Synchrotron X-rays uncover hidden protein binding sites, enabling two new functions
Using bright X-rays from the Department of Energy’s SLAC National Accelerator Laboratory and Lawrence Berkeley National Laboratory (Berkeley Lab), researchers pioneered an innovative approach to designing proteins with targeted functions. Their method generated new insights that allowed the team to turn a single designed protein into two new proteins with completely different functions—one of which is the most active designed enzyme to date.
In the study, published in Nature Chemistry, the University of California, San Francisco (UCSF), team for the first time combined X-ray studies of how small molecule fragments bind to designed proteins, known as crystallographic fragment screening, with a method used to design proteins, called directed evolution. This breakthrough approach could lead to simpler ways to improve enzymes and medications, among other uses.
“Our novel protein design strategy simultaneously explores the landscapes of chemical space and sequence space, which helps design functional proteins rapidly,” said Sagar Bhattacharya, postdoctoral researcher at UCSF and an author on the paper. “Instead of the typical 5–10 rounds or more of directed evolution, we achieved the 10-fold higher enzyme activity with just two rounds of directed evolution.”
AI fails to make inroads with cybercriminals, study finds
Cybercriminals have been struggling to adopt AI in their work, reports the first-of-its-kind study that analyzed a dataset of 100 million posts from underground cybercrime communities. The study is published on the arXiv preprint server.
In reality, most cybercriminals—often referred to as hackers—lack the skills or resources to support real innovation within their criminal activities, experts say.
No digital content is safe from generative AI, researchers say
A research team led by Virginia Tech cybersecurity expert Bimal Viswanath has found a critical blind spot in today’s image protection techniques designed to prevent bad actors from stealing online content for unauthorized artificial intelligence training, style mimicry, and deepfake manipulations. The study is published on the arXiv preprint server.
The research team found that attackers can defeat existing security using off-the-shelf artificial intelligence (AI) models and simple commands. Furthermore, “There is currently no foolproof, mathematically guaranteed way for users to protect publicly posted images against an adversary using off-the-shelf GenAI models,” Viswanath said.
The work was presented at the fourth IEEE Conference on Secure and Trustworthy Machine Learning, in Munich, Germany. The authors include Viswanath, doctoral students Xavier Pleimling and Sifat Muhammad Abdullah, Assistant Professor Peng Gao, Murtuza Jadliwala of the University of Texas at San Antonio, and Gunjan Balde and Mainack Mondal of the Indian Institute of Technology, Kharagpur.
Symmetry says these crystal vibrations can never mix, but an exotic quantum phase rewrites the rules
Symmetry is one of the most fundamental principles in nature. It describes the rules that make an object look unchanged after a rotation, reflection, or other transformations. In materials, symmetry governs how atoms and electrons are arranged, and how they move together. Crucially, symmetry can even prevent certain collective atomic motions (vibrations) from interacting at all: some are simply forbidden to talk to each other. But what if those symmetry restrictions are not as rigid as they seem?
A new study in Nature Physics shows that these constraints can be partially lifted. Researchers at the University of Texas at Austin and the Max Planck Institute for the Structure and Dynamics of Matter (MPSD) in Hamburg found that electronic fluctuations can dynamically bridge vibrations that symmetry would normally keep separate. Led by Edoardo Baldini’s group at UT Austin, the study reveals how light, vibrations, and electrons become intertwined in a special type of crystal known as ferroaxial, opening new opportunities for controlling quantum states with light.
The researchers focused on a layered material that at room temperature develops an exotic quantum state. Ions and electrons rearrange together into a static, wave-like pattern known as a charge-density wave (CDW), which manifests as a tiling of star-of-David clusters.