  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 post-training approaches for mitigating
overfitting in QCNNs. We find that a straightforward adaptation of a classical
post-training method, known as neuron dropout, to 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.
Hence, we propose a parameter adaptation method as an alternative method. Our
method is computationally efficient and is found to successfully handle
overfitting in the test cases.
