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.