GENERATIVE adversarial networksQUANTUM computingMACHINE learningAdversarial transfer learning is a machine learning method that employs an adversarial training process to learn the datasets of different domains. Recently, this method has attracted attention because it can efficiently decouple the requirements of...
L. Learning internal representations by error propagation. in Parallel Distributed Processing: Explorations in the Microstructure of Cognition: Foundations 318–362 https://ieeexplore.ieee.org/document/6302929 (1987). Goodfellow, I. et al. Generative adversarial nets. In Proc. Advances in Neural ...
In particular, the authors in [89] demonstrate that their approximate techniques can not only preserve the classification accuracy but also increase adversarial robustness of quantum machine leaning models for image classification. Given that our proposed model is a gradient-based machine learning model ...
The concept of quantum generative adversarial learning can be traced back to [35]. [35] discourses the operating efficiency of quantum generative adversarial learning in a variety of situations, such as the training data is classical data or quantum data, and whether the discriminator and generator...
Lloyd, S., Weedbrook, C.: Quantum generative adversarial learning. Phys. Rev. Lett.121(4), 51 (2018) ArticleMathSciNetGoogle Scholar Chakrabarti, S., Yiming, H., Li, T., Feizi, S., Wu, X.: Quantum wasserstein generative adversarial networks. In: Wallach, H., Larochelle, H., Bey...
For the description of the quantum generative adversarial learning method, see [378]. A scheme on the classification with gate-model quantum neural networks can be found in [379]. Show moreView article Preliminaries Mingsheng Ying, in Foundations of Quantum Programming, 2016...
it will be an interesting and important challenge to transfer the learning machinery established here also to deep learning approaches (such as convolution neural networks, graph neural networks or even generative adversarial models) ideally by incorporating symmetries, prior physical knowledge and equivaria...
Self-Learning: Continuously learns from past decisions and outcomes to improve future decision-making accuracy. Methodology: Deep Learning: Employs Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) for processing complex data inputs. ...
COVID-19 CT image synthesis with a conditional generative adversarial network. arXiv preprint arXiv:2007.14638. 2020. Abbas A, Abdelsamea MM, Gaber M. 4S-DT: self supervised super sample decomposition for transfer learning with application to COVID-19 detection, arXivpreprint arXiv:2007.11450. ...
generative adversarial networks can be applied to quantum optics.89Together, our results point towards both a powerful simulation tool for the design of next-generation quantum optical systems and a versatile experimental platform for near-term optical quantum information processing and machine learning. ...