  Training machine learning models in an incremental fashion is not only
important but also an efficient way to achieve artificial general intelligence.
The ability that humans possess of continuous or lifelong learning helps them
to not forget previously learned tasks. However, current neural network models
are prone to catastrophic forgetting when it comes to continual learning. Many
researchers have come up with several techniques in order to reduce the effect
of forgetting from neural networks, however, all techniques are studied
classically with a very less focus on changing the machine learning model
architecture. In this research paper, we show that it is not only possible to
circumvent catastrophic forgetting in continual learning with novel hybrid
classical-quantum neural networks, but also explains what features are most
important to learn for classification. In addition, we also claim that if the
model is trained with these explanations, it tends to give better performance
and learn specific features that are far from the decision boundary. Finally,
we present the experimental results to show comparisons between classical and
classical-quantum hybrid architectures on benchmark MNIST and CIFAR-10
datasets. After successful runs of learning procedure, we found hybrid neural
network outperforms classical one in terms of remembering the right evidences
of the class-specific features.
