A Deep-Learning-Based Proposal to Aid Users in Quantum Computing Programming

Author:

Cruz-Benito Juan,Faro Ismael,Martín-Fernández Francisco,Therón Roberto,García-Peñalvo Francisco J.

Publisher

Springer International Publishing

Reference31 articles.

1. Cross, A.W., Bishop, L.S., Smolin, J.A., Gambetta, J.M.: Open quantum assembly language (2017). arXiv preprint arXiv:1707.03429

2. Cross, A.: The IBM Q experience and QISKit open-source quantum computing software. Bull. Am. Phys. Soc. (2018)

3. IBM Research, QISKit. Quantum Information Software Kit. Accessed 18 Feb 2018. https://www.qiskit.org/

4. Kandala, A., Mezzacapo, A., Temme, K., Takita, M., Brink, M., Chow, J.M., Gambetta, J.M.: Hardware-efficient variational quantum eigensolver for small molecules and quantum magnets. Nature 549, 242 (2017)

5. Ristè, D., da Silva, M.P., Ryan, C.A., Cross, A.W., Córcoles, A.D., Smolin, J.A., Gambetta, J.M., Chow, J.M., Johnson, B.R.: Demonstration of quantum advantage in machine learning. npj Quantum. Information 3, 16 (2017)

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