Distributed Quantum Neural Networks via Partitioned Features Encoding

The paper of "Distributed quantum neural networks via partitioned features encoding" can be read at Quantum Machine Intelligence. Here, we put the codes used in the paper.

Examples

In this repository, we put the jupyter notebooks used in our numerical experiments.

• mnist28x28_14qnn20_c4.75.ipynb: classifying MNIST data set (28x28) with 14 QNNs
• semeion16x16_4qnn20+c10.ipynb: classifying Semeion data set (16x16) with 4 qnns
• semeion16x16_8qnn20+c5.ipynb: classifying Semeion data set (16x16) with 8 qnns
• semeion8x8_2qnn20+c20.ipynb: classifying reduced sized Semeion data set (8x8) with 2 qnns
• semeion8x8_qnn20+c35.ipynb: classifying reduced sized Semeion data set (8x8) 1 qnn

Citation

If you find this repository useful for your research, please consider citing our work:

@article{kawase2024distributed,
  title={Distributed quantum neural networks via partitioned features encoding},
  author={Yoshiaki Kawase},
  journal={Quantum Machine Intelligence},
  volume={6},
  number={1},
  pages={15},
  year={2024},
  publisher={Springer}
}