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The Quantum Autoencoder was proposed as a device which can compress quantum states efficiently.
One possible application on near term quantum devices would be the compression of quantum data in order to protect it from decoherence effects. The compression however, also introduces noise to the quantum states so that it will not always be beneficial to apply it. This will depend on the individual circuit representing the autoencoder and also on the specific time for which the quantum state has to be stored.
In this project we aim to find the critical points in storage time after which the compression of the quantum states is beneficial in order to keep most of the quantum information for a variety building blocks which can be trained to be autoencoders.
Apart from specific examples from the literature we will try to categorize those building blocks with respect those storage times.
Alán Aspuru-Guzik
Williams College
Computer science
Technology; Information and Communications Technology; Other; Quantum Science
University of Toronto
Globalink Research Award
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