This code was used to generate the data and plots in the paper: https://doi.org/10.3390/electronics10141690. This work explores the use of coresets - small sets of data which accurately summarize a larger data set - to fit large machine learning problems onto small quantum computers.
The bulk on the code is contained in the kMeans directory. The benchmark data sets used in the paper are contained in the Datasets directory stored as binary .npy files (see https://numpy.org/doc/stable/reference/generated/numpy.load.html).
The code for generating the coresets studied in the paper are contained in coreset.py.
- Practical Coreset Constructions for Machine Learning https://arxiv.org/pdf/1703.06476.pdf
- New Frameworks for Offline and Streaming Coreset Constructions https://arxiv.org/pdf/1612.00889.pdf
The file kmeans_qaoa.py contains all of the functions used to solve the k-means clustering problem using the Quantum Approximate Optimization Algorithm (QAOA). The functions within this file load in and plot the coreset points, construct and optimize the variational QAOA circuit, and execute them on real IBMQ hardware. The jupyter notebook qaoa.ipynb is a useful demonstration of these functions.
The catdog.ipynb, cifar10.ipynb, epilepsy.ipynb, pulsar.ipynb, synthetic.ipynb, val2017.ipynb, and yeast.ipynb notebooks were used to generate the coreset comparison results in the paper.