Spintronics, or spin-electronics, is a revolutionary approach to information processing that utilizes the intrinsic angular momentum (spin) of electrons, rather than solely relying on electric charge flow. This technology promises faster, more energy-efficient data storage and logic devices. A central challenge in fully realizing spintronics has been the development of materials that can precisely control electron spin direction.

A research team has developed a novel direct sampling method based on deep generative models. Their method enables efficient sampling of the Boltzmann distribution across a continuous temperature range. The findings have been published in Physical Review Letters. The team was led by Prof. Pan Ding, Associate Professor from the Departments of Physics and Chemistry, and Dr. Li Shuo-Hui, Research Assistant Professor from the Department of Physics at the Hong Kong University of Science and Technology (HKUST).

Quantum computers promise enormous computational power, but the nature of quantum states makes computation and data inherently “noisy.” Rice University computer scientists have developed algorithms that account for noise that is not just random but malicious. Their work could help make quantum computers more accurate and dependable.