This repository contains comprehensive documentation on Quantum Machine Learning (QML), aimed at beginners and practitioners interested in exploring the intersection of quantum computing and machine learning.
- How to get started with the documentation
- Contribution Guidelines
- Status of the Project
- Installation
- Project Structure
- License
When you go to the live website or local server after installation, you will see the following tabs in the navigation bar:
ie: the overview, documentation, references, and the search bar.
- The
Overview
tab contains the introduction to the documentation. - The
Documentation
tab contains the main content of the documentation. - The
References
tab contains the references used in the documentation.
The Documentation
tab contains the following sub-tabs:
- The
Documentation
sub-tab contains a very brief about what is quantum machine learning. - The
Notations
sub-tab contains all the notations used in the documentation. - The
Fundamentals
sub-tab contains the in-depth introduction to concepts of quantum mechanics to understand fundamentals of quantum machine learning. - The
Quantum Gates and Circuits
sub-tab contains the in-depth introduction and walkthrough of quantum gates and circuits.
If you are beginner, then you should start with the Notations
sub-tab, then move to the Fundamentals
sub-tab, and then to the Quantum Gates and Circuits
sub-tab.
- The framework of the documentation is ready.
- Overview.
- General issues in QC.
- Notations and representations.
- Fundamentals of Quantum Mechanics.
- Qubits.
- Superposition.
- Entanglement.
- Bloch Sphere.
- Understanding Quantum gates (analogy with classical gates).
- Single qubit gates.
- Pauli gates.
- Hadamard gate.
- Phase gate.
- T gate.
- S gate.
- U gate.
- Multi qubit gates.
- CNOT gate.
- SWAP gate.
- Toffoli gate.
- Fredkin gate.
- Controlled U gate.
- Controlled phase gate.
- Universal gates.
- X, Y, Z gates.
- Hadamard gate.
- CNOT gate.
- Toffoli gate.
- SWAP gate.
- Fredkin gate.
- Single qubit gates.
- Quantum circuits.
- How to construct any circuit.
- Quantum ML algorithms (algos like SVM, KNN etc).
- Quantum Neural Networks.
- Quantum Convolutional Neural Networks.
- Quantum Generative Adversarial Networks.
- Quantum Reinforcement Learning.
- Quantum Transfer Learning.
- Quantum Autoencoders.
before you start, make sure you have the following installed:
- Python 3.6 or later
install virtualenv using pip:
pip install virtualenv
create a virtual environment for windows
using the following command:
python -m virtualenv venv
create a virtual environment for linux
using the following command:
virtualenv venv
activate the virtual environment for windows
using the following command:
venv\Scripts\activate
activate the virtual environment for linux
using the following command:
source venv/bin/activate
install the required packages using the following command:
pip install -r requirements.txt
starting the mkdocs server
mkdocs serve
now you can access the documentation at http://localhost:8000/
in your browser.
The project structure is as follows:
├───.github
│ ├───ISSUE_TEMPLATE
│ └───workflows
├───docs
│ ├───assets
│ ├───documentation
│ | ├───index.md
│ | ├───concepts.md
│ | ├───notations.md
│ ├───javascripts
| | |───katex.js
| | |───mathjax.js
│ ├───overview
│ | ├───index.md
│ ├───references
│ └───stylesheets
│ └───extra.css
└───mkdocs.yml
mkdocs.yml
is the configuration file for the documentation, it contains the site name, the pages, and the theme.
docs
is the folder that contains the documentation pages, it contains the index.md
file which is the home page of the documentation, and the stylesheets
folder which contains the extra.css
file that contains the custom styles for the documentation.
To create a new page (ie the tab), go to docs
folder and create a new markdown file with the name of the page you want to create. Automatically, the new page will be added to the navigation bar.
If you want to add any new pulgins, then refer to this website for reference mkdocs plugins
If you want to change the setup of the documentation, then refer to this website for reference mkdocs setup
MIT License