An Evolutionary Deep Learning Approach for Efficient Quantum Algorithms Transpilation
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-56855-8_15
Reference21 articles.
1. Acampora, G., Schiattarella, R.: Deep neural networks for quantum circuit mapping. Neural Comput. Appl. 33(20), 13723–13743 (2021). https://doi.org/10.1007/s00521-021-06009-3
2. Bahreini, T., Mohammadzadeh, N.: An minlp model for scheduling and placement of quantum circuits with a heuristic solution approach. J. Emerg. Technol. Comput. Syst. 12(3) (2015). https://doi.org/10.1145/2766452
3. Brooks, M.: Beyond quantum supremacy: the hunt for useful quantum computers. Nature 574(7776), 19–21 (2019). https://doi.org/10.1038/d41586-019-02936-3
4. DAHI, Z.A., Chicano, F., Luque, G.: Replication package for the paper “An evolutionary deep learning approach for efficient quantum algorithms transpilation" (2023). https://doi.org/10.5281/zenodo.10141872
5. Dahi, Z.A., Chicano, F., Luque, G., Alba, E.: Genetic algorithm for qubits initialisation in noisy intermediate-scale quantum machines: the IBM case study. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2022), pp. 1164–1172. Association for Computing Machinery, New York (2022). https://doi.org/10.1145/3512290.3528830
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