Publisher
Springer Science and Business Media LLC
Subject
Electrical and Electronic Engineering,Modeling and Simulation,Signal Processing,Theoretical Computer Science,Statistical and Nonlinear Physics,Electronic, Optical and Magnetic Materials
Reference30 articles.
1. Arjovsky, M., Bottou, L.: Towards principled methods for training generative adversarial networks. In: Proceedings of the 5th International Conference on Learning Representations (2017)
2. Bauer, B., Wecker, D., Millis, A.J., Hastings, M.B., Troyer, M.: Hybrid quantum-classical approach to correlated materials. Phys. Rev. X 6, 031045 (2016). https://doi.org/10.1103/PhysRevX.6.031045
3. Benedetti, M., Garcia-Pintos, D., Perdomo, O., Leyton-Ortega, V., Nam, Y., Perdomo-Ortiz, A.: A generative modeling approach for benchmarking and training shallow quantum circuits. NPJ Quant. Inf. 5(1), 45 (2019). https://doi.org/10.1038/s41534-019-0157-8
4. Benedetti, M., Lloyd, E., Sack, S., Fiorentini, M.: Parameterized quantum circuits as machine learning models. Quant. Sci. Technol. 4(4), 043001 (2019). https://doi.org/10.1088/2058-9565/ab4eb5
5. Biamonte, J., Wittek, P., Pancotti, N., Rebentrost, P., Wiebe, N., Lloyd, S.: Quantum machine learning. Nature 549(7671), 195–202 (2017). https://doi.org/10.1038/nature23474
Cited by
2 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献