  Quantum convolutional neural network (QCNN), an early application for quantum
computers in the NISQ era, has been consistently proven successful as a machine
learning (ML) algorithm for several tasks with significant accuracy. Derived
from its classical counterpart, QCNN is prone to overfitting. Overfitting is a
typical shortcoming of ML models that are trained too closely to the availed
training dataset and perform relatively poorly on unseen datasets for a similar
problem. In this work we study the adaptation of one of the most successful
overfitting mitigation method, knows as the (post-training) dropout method, to
the quantum setting. We find that a straightforward implementation of this
method in the quantum setting leads to a significant and undesirable
consequence: a substantial decrease in success probability of the QCNN. We
argue that this effect exposes the crucial role of entanglement in QCNNs and
the vulnerability of QCNNs to entanglement loss. To handle overfitting, we
proposed a softer version of the dropout method. We find that the proposed
method allows us to handle successfully overfitting in the test cases.

Quantum Physics;Machine Learning