New mathematical tools shed light on the fluctuations of living matter

Fluctuations in such energy-consuming systems cannot be assessed by traditional physics due to the influence of the arrow of time on their behavior

Quantitative predictions on the behavior of active matter can facilitate the experimental design of such systems

A research team at Kumamoto University (Japan) has unveiled a new mathematical framework that makes it possible to accurately model systems using multiple sensors that operate at different sensing rates. This breakthrough could pave the way for safer autonomous vehicles, smarter robots, and more reliable sensor networks.

Researchers at Heriot-Watt University have unveiled a prototype quantum network that links two smaller networks into one reconfigurable, eight-user system capable of routing and even teleporting entanglement on demand. 

Researchers have demonstrated a new approach to building quantum convolutional neural networks (QCNNs) using photonic circuits, paving the way for more efficient quantum machine learning. The method, reported in Advanced Photonics, introduces an adaptive step called “state injection,” allowing the circuit to adjust its behavior based on real-time measurements. Using single photons and integrated quantum photonic processors, the team achieved over 92 percent classification accuracy on simple image patterns, closely matching theoretical predictions. This proof-of-concept shows that QCNNs can be implemented with existing photonic technology and highlights a path toward scalable quantum processors for future applications in AI and data processing.