Testing Quantum Theory, Artificially
Which is the perfect quantum theory?
Excerpts and salient points ~
+ [A] research team at the Technical University of Munich and at Harvard University has successfully employed machine learning: The researchers trained an artificial neural network to distinguish between two competing theories.
“Similar to the detection of cats or dogs in pictures, images of configurations from every quantum theory are fed into the neural network,” says Annabelle Bohrdt, a doctoral student at TUM. “The network parameters are then optimized to give each image the right label – in this case, they are just theory A or theory B instead of cat or dog.”
+ After the training phase with theoretical data, the neural network had to apply what it had learned and assign snapshots from the quantum simulators to theory A or B. The network thus selected the theory which is more predictive.
+ 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.
Source: EurekAlert! Annabelle Bohrdt and Christoph Hohmann, Which is the perfect quantum theory?
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