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Photonic time crystals unlock ultrafast control of light in the terahertz range

An international team of researchers from École Polytechnique, Collège de France and Helmholtz-Zentrum Dresden-Rossendorf (HZDR) has achieved a world first: the experimental realization of an all-optical photonic time crystal (PTC), a material whose optical properties can be strongly and periodically modulated over ultrafast timescales.

Published in Nature, this breakthrough uses HZDR’s TELBE superradiant terahertz source to drive the system into a new regime of light-matter interaction in the terahertz range. This discovery paves the way for ultrafast optical computing, new telecommunications systems and eventually new types of terahertz lasers.

Shaping the properties of light as it interacts with materials is the foundation of many discoveries and technological advances, including optical fibers for telecommunications, lasers as light sources and sensors for chemistry and biology.

Scientists have found a new way molecules can cooperate at room temperature

What if glowing molecules could synchronize, much like fireflies flashing in unison? Researchers have discovered that molecules confined within tiny gold nanostructures can behave collectively, coordinating their interactions even under conditions where this was previously thought impossible. The finding challenges long standing assumptions about how optical coherence forms and opens new possibilities for highly sensitive sensors, molecular photonics, and future quantum technologies capable of operating at room temperature.

Optical coherence describes a state in which light—or the molecules producing it—behaves in a highly coordinated way. It is the principle behind technologies such as lasers, advanced imaging systems and quantum communication. Traditionally, scientists believed this kind of coordinated behavior required specially designed optical cavities that trap light for relatively long periods.

Spectroscopy system detects aerosols using light reflected from traffic signs and tree trunks

Researchers have developed and tested an infrared spectroscopy system that can rapidly detect chemical aerosols from a distance by using light reflected from common surfaces such as traffic signs, tree trunks or painted surfaces. The new method could make it possible to detect hazards without complicated instruments, helping improve safety and ease operations at industrial sites, public venues and other high-risk locations.

“Previously, many remote chemical detection systems have relied on placing a mirror or other highly reflective target in the field to bounce the laser signal back to the detector, which isn’t practical in many real-world situations,” said research team leader Tim Johnson from Pacific Northwest National Laboratory. “Our approach eliminates that requirement by using reflections from ordinary surfaces, allowing us to detect aerosolized chemicals from a distance without installing specialized equipment at the target location.”

In the journal Applied Optics, the researchers report results from laboratory tests using reflected infrared laser light from different surfaces. They showed that many nonmetallic surfaces could be used for aerosol and vapor detection at standoff distances of up to 11 meters (36 feet).

Learning may rely on stronger neural connections, not expanding networks

How does the brain learn? Does it acquire new knowledge by creating new neural pathways or by strengthening existing connections? A new study from Bar-Ilan University offers evidence in favor of the latter, suggesting that learning is driven primarily by changes in the strength of existing neural connections rather than by expanding the brain’s underlying architecture.

Published in Physica A: Statistical Mechanics and its Applications, the study by Prof. Ido Kanter of Bar-Ilan University’s Department of Physics and the Gonda (Goldschmied) Multidisciplinary Brain Research Center explored this longstanding question using artificial neural networks trained on language-learning tasks.

As the amount of training data increased, the models became significantly better at learning. Surprisingly, however, the researchers found that the networks could still lose roughly the same proportion of connections (synapses) without any meaningful decline in performance. In other words, improved learning did not depend on building more complex networks. Instead, it resulted from more effective cooperation among the components that were already there.

Cesium atoms and quantum dots generate indistinguishable photons for modular quantum networks

Large-scale quantum communication networks require both reliable quantum memories and coherent single-photon sources that can exchange quantum information efficiently. A coherent source of single photons with narrow linewidth, high brightness, spectral uniformity and compatibility with quantum memories is necessary. While a variety of single-photon sources, such as quantum dots (QDs) and atoms in warm vapor cells, have been developed in recent years, each has inherent limitations, making a scalable and functional quantum network challenging to achieve.

Hybrid quantum architectures that combine different quantum light sources can address these challenges. For example, QDs, which suffer from spectral randomness and are not well suited for photon storage, can be paired with atomic systems that provide reliable frequency standards and quantum memories. In such architectures, QDs can serve as bright, high-rate photon sources, while atomic systems handle photon storage and synchronization.

However, a key challenge in realizing such hybrid quantum architectures is interfacing different quantum light sources. Single photons emitted from different sources exhibit distinct spatial and temporal properties, necessitating modifications and synchronization that introduce losses and increase resource needs.

New microwave neural network method could compress and secure wireless communications

One year after unveiling a first-of-its-kind “microwave brain” microchip capable of computing on ultrafast data and wireless signals, researchers from the Cornell Duffield College of Engineering have shown how the chip can encode information into its own language.

The work builds on the world’s first integrated microwave neural network designed by Bal Govind, Ph.D., and experimentally demonstrated with Maxwell Anderson. Together, they showed that the low-power chip could harness the physics of microwaves to emulate the brain’s pattern-finding abilities and perform computations almost instantaneously.

In a new study published in Nature Communications, the researchers found that the device can now use what they describe as microwave token embeddings—similar to the tokens used in large language models—to encode messages into radio signals and compress data, capabilities that could enable faster, more secure communications for satellites, drones and other technologies.

Physicists create Bose–Einstein condensate from ultracold polar molecules

Bose–Einstein condensates are states of matter that form when particles called bosons are cooled to temperatures that are only a fraction of a degree above absolute zero (i.e., 0 Kelvin [-460°F]). In these states, particles occupy the same quantum state and exhibit interesting collective behaviors, essentially behaving as if they were a single “super-particle.”

So far, physicists have primarily created Bose–Einstein condensates using atoms. The first realization of these states with molecules was just over two decades ago, in 2003.

Producing Bose–Einstein condensates with ultracold polar molecules, cooled molecules in which positive and negative charges are separate, has proved particularly challenging. This is partly due to chemical reactions that can cause a loss of these molecules while they are being cooled.

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