Linear Structure of Training Samples in Quantum Neural Network Applications

Author:

Mandl AlexanderORCID,Barzen JohannaORCID,Bechtold MarvinORCID,Keckeisen MichaelORCID,Leymann FrankORCID,Vaudrevange Patrick K. S.ORCID

Publisher

Springer Nature Singapore

Reference25 articles.

1. Documentation on torch.optim.Adam. https://pytorch.org/docs/stable/generated/torch.optim.Adam.html. Accessed 31 Aug 2023

2. Qiskit: Summary of quantum operations: Standard rotations. https://qiskit.org/documentation/tutorials/circuits/3_summary_of_quantum_operations.html#Standard-Rotations. Accessed 30 Aug 2023

3. Beer, K., et al.: Training deep quantum neural networks. Nat. Commun. 11(1), 808 (2020)

4. 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)

5. Benedetti, M., Lloyd, E., Sack, S., Fiorentini, M.: Parameterized quantum circuits as machine learning models. Quant. Sci. Technol. 4(4), 043001 (2019)

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