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Quantum chip holds multiple photons at once, opening path to scalable memory

For highly fragile quantum information systems, the ability to store quantum information is vital—but also challenging. Quantum information is transported in particles of light called photons, which often must be temporarily paused (or “stored”) while other, slower quantum operations catch up. This storage must be performed on microchips as small as 1 centimeter (0.4 inches)—a distance covered by light in a few trillionths of a second. Storing photons for a microsecond would represent a massive leap forward for the capabilities of quantum chips.

New research from The Grainger College of Engineering at the University of Illinois Urbana-Champaign addresses this challenge by developing an integrated on-chip nanophotonic platform for longer-term storage of photons.

The research, led by physics professor Elizabeth Goldschmidt and published in Nano Letters, describes an integrated platform that leverages the versatility of spectral hole burning and the scalability of thin-film lithium niobate, giving it potential for scalable manufacturing, with implications for both classical and quantum photonics.

AI image watermarks can survive new model training, but durability varies by design

Watermarks are increasingly being used to make AI-generated images recognizable and to ensure their origin can be traced. Previous research has focused on whether watermarks can withstand image manipulation. CISPA researcher Michel Meintz from the SprintML Lab has investigated whether watermarks can withstand the training of a new generative model.

The result: Not all watermarks are equally robust. Their ability to persist across multiple model generations depends heavily on their design and the model that is used. The paper “Watermark Degradation Across Model Iterations” was presented at the ACM Workshop on Information Hiding and Multimedia Security (IH&MMSec 26) in Florence.

AI-generated images are ubiquitous today and are widely distributed, especially via the internet. This increases the risk that AI-generated images will be used to train new image-generation models. “When companies train on their own synthetic data, it can lead to model collapse,” explains Michel Meintz. “The more you train on your own synthetic data, the more likely the model’s quality is to deteriorate.”

Physics model reveals fundamental trade-off between prediction and energy efficiency in intelligent systems

Artificial intelligence is increasingly becoming part of our everyday lives, from digital assistants to robots that respond to their surroundings. Each of these capabilities is based on physical computational processes that consume energy. An international research team led by Hans Briegel from the Department of Theoretical Physics at the University of Innsbruck, Austria, has investigated the physical limits on the efficiency with which intelligent systems process information. Their findings are published in Physical Review X.

An agent is a system that gathers information from its environment and responds to that feedback, much like a robot moving through a room while constantly making new observations. “Through his actions, the agent changes the world he himself is trying to predict. We wanted to understand what that changes about the physics of information processing,” explains Lukas Fiderer, the study’s lead author.

In 1961, physicist Rolf Landauer identified a fundamental physical cost of erasing information. However, the relationship also works in reverse: Under suitable conditions, an agent can use information about a physical system to extract energy from it. The new study investigates the greatest average amount of useful energy an ideal agent can extract per interaction through repeated exchanges with its environment. It thus provides a precise thermodynamic benchmark for comparing different ways of organizing the agent’s memory and actions.

Light reads electron spins inside porous crystals, opening path to quantum chemical sensors

University of Glasgow researchers are part of an international collaboration that could lead to a new generation of quantum sensors. The team, which included researchers from the University of Tokyo, University of Glasgow, University of Sheffield and Kobe University, has for the first time used light to read out the magnetic spin of electrons trapped inside a porous crystalline material known as a metal-organic framework (MOF).

The team’s work builds on previous research into a method of detecting electron spins using light called optically detected magnetic resonance, or ODMR. The technique has attracted attention as a useful method for reading out spin qubits in quantum sensing.

The development marks an important step toward using MOFs, which have been regarded as promising materials for quantum sensing applications, to detect chemical substances with extraordinary sensitivity in future sensing devices.

Scientists Uncover Two Hidden Forms of Disorder in Next-Generation Semiconductors

A study of twisted two-dimensional semiconductors reveals how subtle material disorder can emerge at different spatial scales.

Two distinct forms of hidden disorder may influence how ultrathin semiconductors emit light. One extends across relatively large areas of the material, while the other is concentrated around tiny defects. A new theoretical approach offers a way to identify these patterns by analyzing changes in light emission rather than attempting to separate individual spectral signals.

Developed by Katsunori Wakabayashi at the Research Center for Materials Nanoarchitectonics (MANA), part of the National Institute for Materials Science (NIMS), the framework could provide a more reliable way to investigate imperfections in materials used for advanced optical and quantum devices.

Biofluorescence Discovered in Fire Salamanders for the First Time

Researchers have discovered that fire salamanders can produce a striking cyan-green fluorescence when exposed to ultraviolet light.

Fire salamanders are famous for their vivid yellow and black warning colors, but part of their appearance remains invisible to human eyes. Researchers have found that when ultraviolet light strikes the animals, their skin and defensive secretions emit a cyan-green fluorescence that may persist for more than 24 hours after the secretions are released.

The fluorescence is strongest in the salamanders’ yellow underside and along the sides of the body. It originates mainly from skin glands and the substances they produce, suggesting that the phenomenon is closely connected to the same tissues involved in the animal’s chemical defenses.

Credential-Stealing GitHub Actions Workflows Planted in Tens of Thousands of Repositories

Cybersecurity researchers have disclosed details of an ongoing credential-theft campaign that has compromised two high-profile open-source maintainer accounts to push a malicious workflow into over 340 repositories.

“Using the account of Takashi Kitao, author of the 18,400-star game engine pyxel, the attacker pushed a malicious workflow to 27 repositories starting at 13:20 UTC,” StepSecurity said. “Eight hours later, the account of Henry Wu (henrywoo), the original author of Uber’s athenadriver, was used to push the same workflow to 318 repositories in a 16-minute window, 21:10–21:26 UTC.”

As of October 9, 2026, Socket said it has identified more than 500 GitHub accounts that committed the malicious workflow to tens of thousands of repositories since October 7, 2026.

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