Machine Learning, Artificial Intelligence, Neural Networks and the Quantum World
Which One Is the Perfect Quantum Theory?
Excerpts and salient points ~
+ For some phenomena in quantum many-body physics several competing theories exist. 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.
Artificial intelligence helps physicists find the optimal description of quantum phenomena.
+ Understanding such extraordinary states of matter is challenging: quantum simulators based on ultracold Lithium atoms have been developed to study the physics of high-temperature superconductors. They take snapshots of the quantum system, which exists simultaneously in different configurations – physicists speak of a superposition. Each snapshot of the quantum system gives one specific configuration according to its quantum mechanical probability. In order to understand such quantum systems, various theoretical models have been developed. But how well do they reflect reality? The question can be answered by analyzing the image data.
+ [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. 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.
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