Quantum Machine Learning Tutorials

This repository contains tutorials on Quantum Machine Learning. The tutorials are written in Jupyter Notebooks and are intended to be run on Google Colab.

Table of Contents

• Quantum Machine Learning Tutorials
  • Folder Structure:
  • Colab Links:
• Training Performance:
  • Explicit Model:
  • Data Reuploading Model:
  • Implicit Model:

Folder Structure:

The folder structure for demos follows the following pattern:

• logs/ : Contains the logs from tensorboard.
• Saved_vars/ : Contains the saved data sets to train the models on
• Notebooks:
  • DataGenerator.ipynb : This notebook creates a random quantum circuit from which we sample to create our dataset.
  • ExplicitModel.ipynb : This notebook contains the implementation of the explicit model for the XOR gate.
  • ImplicitModel.ipynb : This notebook contains the implementation of the implicit model for the XOR gate.
  • Data_Reuploading.ipynb : This notebook contains the implementation of the Quantum Neural Network for the XOR gate.
• Media/ : Contains the images from training.

Colab Links:

The notebooks can be run on Google Colab by clicking on the following links:

Notebook	Colab
ExplicitModel.ipynb
ImplicitModel.ipynb
Data_Reuploading.ipynb
DataGenerator.ipynb

Training Performance:

Explicit Model:

Performance Over Time	Observations
	The data starts matching the target distribution within 6 epochs

Data Reuploading Model:

Performance Over Time	Observations
	The data starts matching the target distribution within 6 epochs

Implicit Model:

Performance Over Time	Observations
	The model is very sampling sensitive and the training can take random steps