But which of them describes a quantum phenomenon best?

A team of researchers from the Technical University of Munich (TUM) and Harvard University in the United States has now successfully deployed artificial neural networks for image analysis of quantum systems.

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.

For example, so far it remains elusive why the electrical resistance of high-temperature superconductors drops to zero at temperatures of about -200 degrees Celsius.

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