Novel quantum lidar achieves high-sensitivity wind detection
A research team has proposed a wind sensing lidar theory based on up-conversion quantum interference and successfully developed a prototype. Their work is published in ACS Photonics.
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A research team has proposed a wind sensing lidar theory based on up-conversion quantum interference and successfully developed a prototype. Their work is published in ACS Photonics.
Bell's theorem, the well-known theoretical framework introduced by John Bell decades ago, delineates the limits of classical physical processes arising from relativistic causality principles. These are principles rooted in ...
Scintillators are detectors that make high-energy X-rays or particles visible through flashes of light to form an image. Their many applications include particle physics, medical imaging, X-ray security and more.
Initially investigating out of pure curiosity, researchers have made a discovery that bridges the gap between Aristotle's observations two millennia ago and modern-day understanding, while opening the door to a whole host ...
While various studies have hinted at the existence of dark matter, its nature, composition and underlying physics remain poorly understood.
Scientists are finding ways to use quantum effects to create groundbreaking thermal devices that can help cool electronic systems. The quantum thermal transistor is one of the most exciting innovations in this field. While ...
University of Copenhagen mathematicians have developed a recipe for upgrading quantum computers to simulate complex quantum systems, such as molecules. Their discovery brings us closer to being able to predict how new drugs ...
As our digital world generates massive amounts of data—more than 2 quintillion bytes of new content each day—yesterday's storage technologies are quickly reaching their limits. Optical memory devices, which use light ...
Atom interferometers are quantum sensors that use the wave-like nature of atoms to measure gravity, acceleration and rotation with exceptional precision.
Deep-learning models are being used in many fields, from health care diagnostics to financial forecasting. However, these models are so computationally intensive that they require the use of powerful cloud-based servers.