This repository contains all the code to reproduce the data and figures for the paper The complexity of quantum support vector machines by Gian Gentinetta, Arne Thomsen, David Sutter and Stefan Woerner (https://arxiv.org/abs/2203.00031).
To reproduce the experiments, I highly suggest you create a new Python3 environment (e.g. with the venv tool). Python3.9 was used for the paper, though other versions of Python3 will probably also work fine. In your new environment install the following packages (the exact version numbers are important!):
- numpy 1.21.4
- pandas 1.3.4
- quadprog 0.1.11
- tqdm 4.62.3
- pickleshare 0.7.5
- scikit-learn 1.0.1
- qiskit-aer 0.9.1
- qiskit-ignis 0.6.0
- qiskit-ibmq-provider 0.18.0
- qiskit-aqua 0.9.5
In addition, the qiskit-machine-learning and qiskit-terra packages have to be cloned from the complexity_of_qsvm branches of the repositories
- https://github.com/gentinettagian/qiskit-machine-learning.git
- https://github.com/gentinettagian/qiskit-terra.git
and installed separately using pip install -e.
If you wish to regenerate the plots, you will additionaly require matplotlib and latex to be installed.
The directory is divided into three folders for the three different models discussed in the paper. dual_qsvm contains the code to generate the figures in Section 4.2. pegasos_qsvm contains the code to generate the figures in Section 4.1, 4.3 and Appendix B. approx_qsvm contains the code to generate the figures in Section 4.4.