natestemen / qml

Quantum Machine Learning group project ⚛️ 🤖

Quantum Machine Learning Group Project

Team Name: Coast to Coast

Team Members: Xiaoran Li, Mark Long, Maryam Mirkamali, Nate Stemen

This is our repository for the QSciTech-QuantumBC Virtual Workshop 2022 (Jan 24-Feb 10).

In this project we work with the banknote authentication data set provided by UCI's Machine Learning Repository as well as a fraud detection bank data set. Our goal was to use quantum machine learning to do classification on both of these data sets.

The workshop ended in poster presentations (you can find ours here) and we won 2nd place for the competition and were awarded $75CAD each.

qml's Issues

Data exploration

Just want to summarize what I've looked at so far, and what I've found in one thread. This analysis was performed with data/feature_exploration.py and some slight modifications to generate some of the plots.

I chose to inspect the entire dataset (1371) entries since any pattern we want to exploit should also be found in our sampled dataset. (I guess this is not technically true, as our 100 data points is small and we might have sampled outliers, but what can you do).

First, we can scatter plot the data to get a sense of how the data might be separating among the features.

From here the "variance" seems to be our "principal component". We can also generate these scatter plots with 3 features at once.

This doesn't seem all that helpful on it's own, but @mmirkamali pointed out that the points that will be most difficult to classify have variance in the range [-2.5, 2] based on the 2D scatter plots. We can then create this same 3D scatter plot with only data points with variance in [-2.5, 2] to obtain

This shows there is a pretty clear 2D plane (in the first and last plot it's most apparent) that separates our data. Pretty nice! I understand this will help us pick better parameters for our classification problem, but I'm not sure what that process looks like from here. Perhaps someone can expand.