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

CQural: A Novel CNN based Hybrid Architecture for Quantum Continual Machine Learning