This is a repository for all code written for the article "Data re-uploading for a universal quantum classifier. Adrián Pérez-Salinas, Alba Cervera-Lierta, Elies Gil-Fuster, and José I. Latorre." It gives numerical simulations of the quantum classifier in arXiv:1907.02085.

All code is written Python. Libraries required:

• matplotlib for plots
• numpy, os, scipy
• scikit-learn

• QuantumState.py: Simulator of a quantum circuit using only basic Python packages such as numpy
• big_functions.py: Functions acting as the master of all other subroutines in the simulator
• circuitery.py: Translates the problem to the quantum circuit basic level.
• classical_benchmark.py: Provides some classical examples using scikit learn.
• data_gen.py: Generates random training and data set for different problems.
• fidelity_minimization.py: All the code needed for the fidelity cost function.
• main.py: This is the only file one needs to change. Everything can be set up there: number of qubits, layers, entanglement, cost function, problem, etc. The only thing one has to do is to run this file.
• problem__gen.py: Generates data of the problem we need for other files.
• save_data.py: Saves results in text files and images.
• test_data.py: Tests the performance of the classifier, and outputs variables needed for saving data.
• weighted_fidelity_minimization.py: All the code needed for the weighted fidelity cost function.

If you use this code in your research, please cite it as follows:

A. Pérez-Salinas, A. Cervera-Lierta, E. Gil-Fuster and J.I. Latorre, "Data re-uploading for a universal quantum classifier", (2019), arXiv: quant-ph: 1907.02085

BibTeX:

@misc{PerezSalinas2019DRUQC,
  author = {Pérez-Salinas, A. and Cervera-Lierta, A. and Gil-Fuster, E. and Latorre, J. I.},
  title = {{Data} re-uploading for a universal quantum classifier},
  year = {2019},
  journal={arXiv:},
  url = {https://arxiv.org/abs/1907.02085}
}