IonQ Machine Learning Demo

Description

This repository contains code for a hybrid quantum-classical machine learning classifier. The model is trained on the red wine quality dataset and utilizes both classical neural networks and quantum neural networks (QNNs) from Qiskit.

Ansatz:

Decision Boundary:

Installation

Navigate to the project directory:

cd ionq-ml-demo

Create a virtual environment (optional):

python3 -m venv .venv

Activate the virtual environment:

source .venv/bin/activate  # On Unix or MacOS
.venv\Scripts\Activate     # On Windows

Install the required packages:

pip install -r requirements.txt

Usage

1. Open the main.ipynb Jupyter Notebook:

   jupyter notebook main.ipynb

2. Run the cells in the notebook to train the model. The trained model will be saved as model.pt.

3. To use the model in your applications, you can load it using PyTorch's torch.load() method.