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Quantum machine learning (QML) requires powerful, flexible and efficiently trainable models to be successful in solving challenging problems.
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K. Sharma, S. Khatri, M. Cerezo, and P. J. Coles, “Noise resilience of variational quantum compiling,” New J. Phys. , vol. 22, no. 4, p. 043006, Apr. 2020. [Online]. Available: https://dx.doi.org/10.1088/1367-2630/ab784c
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A. Ramesh, M. Pavlov, G. Goh, S. Gray, C. Voss, A. Radford, M. Chen, and I. Sutskever, “Zero-Shot Text-to-Image Generation,” in Proceedings of the 38th International Conference on Machine Learning . PMLR, Jul. 2021, pp. 8821–8831. [Online]. Available: https://proceedings.mlr.press/v139/ramesh21a.html
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