  The ultimate goal in machine learning is to construct a model function that
has a generalization capability for unseen dataset, based on given training
dataset. If the model function has too much expressibility power, then it may
overfit to the training data and as a result lose the generalization
capability. To avoid such overfitting issue, several techniques have been
developed in the classical machine learning regime, and the dropout is one such
effective method. This paper proposes a straightforward analogue of this
technique in the quantum machine learning regime, the entangling dropout,
meaning that some entangling gates in a given parametrized quantum circuit are
randomly removed during the training process to reduce the expressibility of
the circuit. Some simple case studies are given to show that this technique
actually suppresses the overfitting.
