Algorithms that run on quantum computers - so-called quantum circuits - underlie different laws of information processing than conventional computations. By optimizing the physical parameters of quantum circuits we can turn these algorithms into trainable models which learn to generalize from data. This talk highlights different aspects of such "variational quantum machine learning algorithms", including their role in the development of near-term quantum technologies, their interpretation as a cross-breed of neural networks and support vector machines, strategies of automatic differentiation, and how to integrate quantum circuits with machine learning frameworks such as PyTorch and Tensorflow using open-source software.
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