Researchers have developed a new type of optical fiber by freezing a glass capillary filled with liquid. It guides light and sound waves simultaneously and enables highly efficient coupling between them. The high coupling strength lowers the energy consumption of photonic neuromorphic computing schemes and quantum signal processing applications by several orders of magnitude.
Artificial intelligence faces an energy crisis stemming from a physical traffic jam inside modern computer chips. Processors must continually shuffle data, such as the billions of parameters in complex models, between separate computing and memory nodes. This traffic jam, known as the "von Neumann bottleneck," hinders the speed and energy efficiency of advanced processors.
Researchers at the University of Michigan have created a device that enables them to control the flow of electrons through a semiconductor using only laser light—no electrical power source required. The device was built to explore fundamental physics and realize a previously unobserved behavior, but it could also open doors for new applications in areas that bridge optics and electronics, including sensing, imaging and telecommunications.
Quantum networks, systems consisting of multiple connected nodes or devices that can transmit quantum information to one another, have the potential to advance future communications. These networks typically leverage entanglement, a quantum phenomenon that prompts two or more distant particles to become highly correlated, so that measuring one instantly affects the state of the other.
Many modern technologies, from optical communications and artificial intelligence (AI) hardware to advanced sensors and medical imaging, depend on photonic and semiconductor devices that precisely control the interaction between light and electrons. Designing these devices, however, remains a major challenge because existing simulation tools often require researchers to choose between modeling an entire device or capturing the detailed behavior of electrons. Few can do both within the same model.
Logically, you would think a sleek surface has optimal aerodynamics—but recent research at Tohoku University turns this fundamental principle on its head. Applying an irregular microscale surface texture reduced the aerodynamic drag of a test model. The innovation has potential applications in the design of fuel-efficient vehicles. The study is published in the Journal of Fluid Mechanics.
A research team led by UCLA and the University of Rochester has demonstrated a promising evolution of an imaging system designed to capture details within "complex media," which scatter light, from depicting structures inside body tissue to seeing obstacles through heavy fog. The system uses physics-based machine learning to improve an existing imaging technique.
Our bodies generate extremely weak magnetic fields as electric currents flow through the heart, brain and other tissues. These signals are used in magnetocardiography and magnetoencephalography to assess heart function and brain activity, respectively. These fields can be detected at room temperature using diamond sensors containing nitrogen-vacancy (NV) centers, in which a carbon atom is replaced by a nitrogen atom adjacent to an empty lattice site.
In recent years, research using artificial intelligence to predict material properties has advanced rapidly. Neural network quantum Monte Carlo methods have attracted attention as highly accurate simulation techniques. However, their extremely high computational cost has limited their application to small molecular systems. This study introduces a new computational method that overcomes this limitation.
Researchers at Skoltech, together with a colleague from the Shanghai Institute of Optics and Fine Mechanics of the Chinese Academy of Sciences, working within the joint SIOM–Skoltech laboratory, have determined how to select the thickness and density of a plasma target so that a pulse passing through it retains its attosecond duration and high intensity. The results will help improve the design of plasma-based sources of ultraviolet and X-ray radiation used to study ultrafast processes in matter.
Reliably generating controlled miniature rotations has long been a challenge: Chemical propulsion systems wear out, and methods that use electric or magnetic fields require complex setups. A team from KIT's Institute of Microstructure Technology (IMT) and the Suzhou Institute of Nano-tech and Nano-bionics (SINANO) at the Chinese Academy of Sciences has now demonstrated that flow at a water surface alone is sufficient to rotate a floating object in a fixed direction. Their research is published in the journal Science Advances.
Quantum information is notoriously fragile. Internet traffic is anything but. Yet Northwestern University scientists have demonstrated they can peacefully coexist inside the same fiber-optic cable.
In most everyday materials, such as copper, silver and silicon, the behavior of electrons is relatively predictable. In quantum materials, however, electrons can interact in complex ways, giving rise to collective electronic states with remarkable properties. Understanding how these states emerge—and, ultimately, how to control them—is one of the central challenges in quantum materials research.
Heavy polar molecules are some of the most sensitive tools physicists have for probing what lies beyond the Standard Model, the theory that describes the particles and forces we know about. But turning that sensitivity into precise, trustworthy measurements has long been held back by one stubborn problem: Stray electric and magnetic fields drown out the tiny signals researchers are actually looking for.
Physicists have discovered a surprisingly simple way to reproduce one of the most fascinating models in modern physics—linked to black holes, quantum chaos and exotic electronic materials—using ultracold atoms trapped in light.
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