Single-photon sources are key components of quantum communication technologies. However, conventional designs use decoupled single-photon emitters and photon transmission methods, resulting in high transmission loss, limiting practical applicability. Now, researchers from Japan have developed a new method, where a single rare-earth ion is used to generate and guide single photons directly within an optical fiber at room temperature. It is low cost and can become a key component of upcoming quantum communication technologies.

Conventionally, deep neural networks (DNNs), including convolutional neural networks (CNNs), are trained using backpropagation—a standard algorithm in AI learning. However, backpropagation suffers from several limitations, such as high computational cost and overfitting. Researchers have now developed a new training approach called the Visual Forward–Forward Network (VFF-Net), which overcomes these challenges. By eliminating the need for backpropagation, VFF-Net enables more efficient, less resource-intensive training while maintaining high accuracy and robustness.

A time crystal, a long-life quantum system approaching perpetual motion, has been hooked up to its environment for the first time, unlocking an intriguing way to increase quantum computational and sensing power.

Researchers have forged a new path to vibration isolation that could help absorb shocks in a variety of applications, including vehicles, buildings and more.

Quantum computing is one of the frontiers of research and innovation due to its potential to outperform classical computers in specific tasks. The EQUALITY (Efficient QUantum ALgorithms for IndusTrY) project, funded by the European Union under the Horizon Europe programme, had the mission of developing innovative quantum computing algorithms to solve strategic industrial problems while dealing with the limitations of this nascent technology.

This paper proposes GAN-Solar, a novel quality optimization model for short-term solar radiation forecasting. Based on Generative Adversarial Networks (GANs), the model addresses spatial texture degradation and intensity distortion in predictions, significantly improving forecast quality and reliability for high-precision applications.