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All issues Volume 251 (2021) EPJ Web Conf., 251 (2021) 03050 References
Open Access
Issue
EPJ Web Conf.
Volume 251, 2021
25th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2021)
Article Number 03050
Number of page(s) 11
Section Offline Computing
DOI https://doi.org/10.1051/epjconf/202125103050
Published online 23 August 2021
  1. M. Granieri, A. Estrada, Down Converter Characterization in a Synthetic Instrument Context, in 2006 IEEE Autotestcon (2006), pp. 20–25, ISSN 1558-4550 [Google Scholar]
  2. J. Allison et al., Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 835, 186 (2016) [Google Scholar]
  3. G. Amadio et al., Journal of Physics: Conference Series 664, 072006 (2015) [Google Scholar]
  4. I. Goodfellow et al., in Advances in Neural Information Processing Systems 27, edited by Z. Ghahramani, M. Welling, C. Cortes, N.D. Lawrence, K.Q. Weinberger (Curran Associates, Inc., 2014), pp. 2672–2680 [Google Scholar]
  5. M. Paganini, L. de Oliveira, B. Nachman, Phys. Rev. D 97, 014021 (2018) [Google Scholar]
  6. S. Vallecorsa, Journal of Physics: Conference Series 1085, 022005 (2018) [Google Scholar]
  7. F. Arute et al., Nature 574, 505 (2019) [Google Scholar]
  8. N. Moll et al., Quantum Science and Technology 3, 030503 (2018) [Google Scholar]
  9. A. Gilliam, S. Woerner, C. Gonciulea, Grover adaptive search for constrained polynomial binary optimization (2019), arXiv:1912.04088 [Google Scholar]
  10. P. Choy et al., Cryptography in Quantum Computing, in Proceedings of the Future Technologies Conference (FTC) 2019 (Springer International Publishing, Cham, 2020), pp. 377–393, ISBN 978-3-030-32520-6 [Google Scholar]
  11. A. Radford, L. Metz, S. Chintala, Unsupervised representation learning with deep convolutional generative adversarial networks (2015), arXiv:1511.06434 [Google Scholar]
  12. A. Odena, C. Olah, J. Shlens, Conditional Image Synthesis with Auxiliary Classifier GANs, in Proceedings of the 34th International Conference on Machine Learning, edited by D. Precup, Y.W. Teh (PMLR, International Convention Centre, Sydney, Australia, 2017), Vol. 70 of Proceedings of Machine Learning Research, pp. 2642–2651 [Google Scholar]
  13. T. Karras et al., CoRR abs/1710.10196 (2017), 1710.10196 [Google Scholar]
  14. F. Carminati, G. Khattak, V. Loncar, T.Q. Nguyen, M. Pierini, R.B.D. Rocha, K. Samaras-Tsakiris, S. Vallecorsa, J.R. Vlimant, Journal of Physics: Conference Series 1525, 012064 (2020) [Google Scholar]
  15. S. Lloyd, C. Weedbrook, Phys. Rev. Lett. 121, 040502 (2018) [Google Scholar]
  16. C. Zoufal, A. Lucchi, S. Woerner, Npj Quantum Information 5, 103 (2019) [Google Scholar]
  17. J. Romero, A. Aspuru-Guzik, Variational quantum generators: Generative adversarial quantum machine learning for continuous distributions (2019), 1901.00848 [Google Scholar]
  18. H. Situ, Z. He, Y. Wang, L. Li, S. Zheng, Information Sciences 538, 193 (2020) [Google Scholar]
  19. P.L. Dallaire-Demers, N. Killoran, Physical Review A 98 (2018) [Google Scholar]
  20. L. Hu et al., Science Advances 5 (2019) [Google Scholar]
  21. M. Benedetti et al., New Journal of Physics 21, 043023 (2019) [Google Scholar]
  22. N. Shrivastava et al., Opticalgan : Generative adversarial networks for continuous variable quantum computation (2019), 1909.07806 [Google Scholar]
  23. A. Anand et al., Experimental demonstration of a quantum generative adversarial network for continuous distributions (2020), 2006.01976 [Google Scholar]
  24. Y. Du, M.H. Hsieh, D. Tao, Efficient online quantum generative adversarial learning algorithms with applications (2019), 1904.09602 [Google Scholar]
  25. M.S. Rudolph et al., Generation of high-resolution handwritten digits with an ion-trap quantum computer (2020), 2012.03924 [Google Scholar]
  26. J.R. McClean, J. Romero, R. Babbush, A. Aspuruv-Guzik, New Journal of Physics 18, 023023(2016) [Google Scholar]
  27. M. Benedetti, E. Lloyd, S. Sack, M. Fiorentini, Quantum Science and Technology 4, 043001 (2019) [Google Scholar]
  28. I. Kerenidis, A. Prakash, Phys. Rev. A 101, 022316 (2020) [Google Scholar]
  29. P. Rebentrost, M. Schuld, L. Wossnig, F. Petruccione, S. Lloyd, New Journal of Physics 21, 073023 (2019) [Google Scholar]
  30. A. Harrow, J. Napp, Low-depth gradient measurements can improve convergence in variational hybrid quantum-classical algorithms (2019), arXiv:1901.05374 [Google Scholar]
  31. T. Kanungo et al., IEEE Trans. Pattern Anal. Mach. Intell. 24, 881–892 (2002) [Google Scholar]
  32. A.L. Maas, Rectifier Nonlinearities Improve Neural Network Acoustic Models (2013) [Google Scholar]
  33. J. Han, C. Moraga, The influence of the sigmoid function parameters on the speed of backpropagation learning, in From Natural to Artificial Neural Computation, edited by J. Mira, F. Sandoval (Springer Berlin Heidelberg, Berlin, Heidelberg, 1995), pp. 195–201, ISBN 978-3-540-49288-7 [Google Scholar]
  34. K. Roth, A. Lucchi, S. Nowozin, T. Hofmann, in Advances in Neural Information Processing Systems 30, edited by I. Guyon, U.V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, R. Garnett (Curran Associates, Inc., 2017), pp. 2018–2028 [Google Scholar]

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