A mathematics professor at The University of Manchester has developed a novel machine-learning method to detect sudden changes in fluid behaviour, improving speed and cost of identifying these instabilities and overcoming one of the major obstacles faced when using machine learning to simulate physical systems.

Quantum exchange or swap gates are an important part of quantum computers with a large number of qubits. Researchers at ETH Zurich have developed a swap gate for qubits made of neutral atoms that is based on a particular physical effect: the state of the particles switches depending on the path they take and is independent of external disturbances. These so-called geometric phases make the system much less susceptible to experimental noise or imperfections. This novel swap gate works with a precision of more than 99,9 percent and can be applied to 17,000 qubits simultaneously.

Every day, fish caught in oceans and seas around the world pass through a long journey before reaching supermarkets, restaurants, and home kitchens. Along the way, their freshness steadily declines, often in ways that are difficult to detect. Now imagine being able to measure how fresh a fish is at any point along this journey. Researchers at Hokkaido University have developed a mathematical model that can do exactly this. The latest development could help reduce food waste and improve seafood quality.