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

arXiv: https://arxiv.org/abs/2305.09738 · HTML: https://mlsanity.com/paper/2305.09738/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 45 references that are in the map.
- First posted on arXiv 16 May 2023.

## Builds on (most cited first, 21 of 21)

- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps](https://mlsanity.com/paper/1312.6034/) (2013)
- [Experience Replay for Continual Learning](https://mlsanity.com/paper/1811.11682/) (2018)
- [A Survey on Multi-Task Learning](https://mlsanity.com/paper/1707.08114/) (2017)
- [An Overview of Multi-Task Learning in Deep Neural Networks](https://mlsanity.com/paper/1706.05098/) (2017)
- [Variational Quantum Algorithms](https://mlsanity.com/paper/2012.09265/) (2020)
- [An Empirical Study of Example Forgetting during Deep Neural Network Learning](https://mlsanity.com/paper/1812.05159/) (2018)
- [Variational Adversarial Active Learning](https://mlsanity.com/paper/1904.00370/) (2019)
- [Efficient Learning for Deep Quantum Neural Networks](https://mlsanity.com/paper/1902.10445/) (2019)
- [Quantum machine learning: a classical perspective](https://mlsanity.com/paper/1707.08561/) (2017)
- [A Survey of Optimization Methods from a Machine Learning Perspective](https://mlsanity.com/paper/1906.06821/) (2019)
- [Quantum-enhanced machine learning](https://mlsanity.com/paper/1610.08251/) (2016)
- [A Survey on Neural Architecture Search](https://mlsanity.com/paper/1905.01392/) (2019)
- [Quantum Architecture Search via Deep Reinforcement Learning](https://mlsanity.com/paper/2104.07715/) (2021)
- [Quantum Federated Learning with Quantum Data](https://mlsanity.com/paper/2106.00005/) (2021)
- [Remembering for the Right Reasons: Explanations Reduce Catastrophic Forgetting](https://mlsanity.com/paper/2010.01528/) (2020)
- [Quantum Perceptron Models](https://mlsanity.com/paper/1602.04799/) (2016)
- [Quantum Inference on Bayesian Networks](https://mlsanity.com/paper/1402.7359/) (2014)
- [Negative Log Likelihood Ratio Loss for Deep Neural Network Classification](https://mlsanity.com/paper/1804.10690/) (2018)
- [Quantum Continual Learning Overcoming Catastrophic Forgetting](https://mlsanity.com/paper/2108.02786/) (2021)
- [When Deep Learners Change Their Mind: Learning Dynamics for Active Learning](https://mlsanity.com/paper/2107.14707/) (2021)

## Abstract (arXiv)

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.