Tensor networks for quantum simulation and machine learning

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Tensor networks are a method to describe quantum wavefunctions. They capture the important aspects of the wavefunctions so that the simulation of quantum systems is possible. Methods using tensor networks for 1D systems are some of the best performing classical methods of quantum simulation. These methods translate naturally to use on quantum computers, allowing the identication of a quantum advantage. Tensor networks have also been shown to be useful in classical machine learning algorithms. The translations of these algorithms to quantum computers may similarly provide an advantage. The insights gained from using tensor networks for quantum simulation may aid in the training of quantum machine learning circuits.