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Stochastic noise can be helpful for variational quantum algorithms
Saddle points constitute a crucial challenge for first-order gradient descent algorithms.
Exploiting symmetry in variational quantum machine learning
The success of variational quantum learning models crucially depends on finding a suitable parametrization of the model that encodes an inductive bias relevant to the learning task.
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