/QML-Lecture-Baseline

Primary LanguageJupyter Notebook

Qiskit lecture help

This repository can be used to help you construct lectures on quantum machine learning concepts using proven and tested circuits.

Installation

We suggest the installation of miniconda to run the experiments. miniconda allows the creation of virtual python environments and keeps things clean. You can find the correct install for you here. Once installed, you can create a virtual environment for this repository using conda create -n desiredName python=3.9. Afterwards activate the environment using conda activate desiredName, and then run pip install -r requirements.txt to install all required packages.

Content

  • requirements.txt
    • Just run pip install -r requirements.txt to install all packages. Make sure you are on Python >= 3.9.0!
  • datasets.ipynb
    • This file will generate/load all the datasets you require to run the scripts in this repository. This helps keep the repository small
  • experiments/
    • qsvm.ipynb
      • This notebook is used to demonstrate quantum enhanced support vector machines, as well as compare them to classical counterparts using the given datasets
    • qnn.ipynb
      • In this notebook you will see the quantum equivalent of a neural network, and perform experiments on it.