(ScienceDaily) A team of researchers at the Technical University of Munich has now successfully deployed artificial neural networks for image analysis of quantum systems.
Is that a dog or a cat? Such a classification is a prime example of machine learning: artificial neural networks can be trained to analyze images by looking for patterns that are characteristic of specific objects. Using the same principle, neural networks can detect changes in tissue on radiological images. Physicists are now using the method to analyze images — so-called snapshots — of quantum many-body systems and find out which theory describes the observed phenomena best.
In the future the researchers plan to use this new method to assess the accuracy of several theoretical descriptions. The aim is to understand the main physical effects of high-temperature superconductivity, which has many important applications, with lossless electric power transmission and efficient magnetic resonance imaging being just two examples.