This code can be used to classically simulate deep quantum neural networks. The basic idea is presented in the Jupyter notebook DQNN_basic.ipynb. Further code can be found in the folder DQNN. The DQNNs are proposed in

• K. Beer, D. Bondarenko, T. Farrelly, T. J. Osborne, R. Salzmann, and R. Wolf. Training deep quantum neural networks. Nat Commun 11, 808 (2020). https://doi.org/10.1038/s41467-020-14454-2

The same code is used to prepare the DQNN results in

• Poland, K., Beer, K., & Osborne, T. J. (2020). No free lunch for quantum machine learning. https://arxiv.org/abs/2003.14103

The folder Qutoencoder-MATLAB contains code used for

• D. Bondarenko and P. Feldmann. Quantum Autoencoders to Denoise Quantum Data. Phys. Rev. Lett. 124, 130502 (2020) https://link.aps.org/doi/10.1103/PhysRevLett.124.130502

In GraphQNN the code of the following project is included

• Beer, K., Khosla, M., Köhler, J., & Osborne, T. J. (2021). Quantum machine learning of graph-structured data. https://arxiv.org/abs/2103.10837

Found code in DQNN_on_NISQ belongs to

• Beer, K., List, D., Müller, G., Osborne, T. J., & Struckmann, C. (2021). Training Quantum Neural Networks on NISQ Devices. Beer, K., List, D., Müller, G., Osborne, T. J., & Struckmann, C. (2021). Training Quantum Neural Networks on NISQ Devices. https://arxiv.org/abs/2104.06081

Moreover the folder DQGAN presents code and resuls from

• Beer, K., & Müller, G. (2021). Dissipative quantum generative adversarial networks. https://arxiv.org/abs/2112.06088