pablomateo / quantum_computing_learning_stack

Resources & References to learn Quantum Computing

Quantum Computing Learning Stack

An awesome stack of Quantum Computing resources!
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Table of Contents

1. Fundamental Topics
• Linear Algebra & Complex Numbers
• Introduction Concepts
2. Quantum Topics
• General Knowledge
3. Ecosystem
• Quantum Companies
• IBM
• Google
• Microsoft
• D-Wave
4. Blogs & News
5. Podcast

Getting Started

This repo is meant to be a reference site of Quantum Computing Learning resources. We have a curated list of references, tutorials, books, videos and courses.

But we also want to reference all that information in a Quantum Learning Roadmap:

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Read the Contributing section if you want to help build this community with us. Thank you!

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Resources

Fundamental Topics

Linear Algebra & Complex Numbers

• 📘 The Mathematics of Quantum Mechanics - Martin Laforest PhD.
• 📘 Linear Algebra 4th Edition - Jim Hefferon

Introduction concepts

• 📘 An Introduction to Quantum Computing - Suitable for undergraduate students with basic concepts and mathematics.

Quantum Topics

General Knowledge

• 📘 Dancing with Qubits - How quantum computing works and how it can change the world.
• 📘 Classical and Quantum Computation - Intro to fundamentals of classical and quantum computing.

Advanced Topics

Quantum Machine Learning

• 🎒 IBM Qiskit Quantum Machine Learning Course - IBM Qiskit Quantum Machine Learning Course

Ecosystem

Quantum Companies

IBM

• 🎒 Qiskit Textbook - Open-source textbook covering quantum algorithms using Qiskit.
• 🎒 IBM Q Full User Guide - Short tutorials providing a gentle introduction to quantum computing and IBM Q.
• 🔗 IBM Quantum Experience - IBM Quantum Experience
• 🔗 Qiskit SDK - Software development kit by IBM for writing and running quantum algorithms on simulators and real hardware.
• 📺 Coding with Qiskit video series - YouTube video series showing how to write quantum algorithms.
• 📣 IBM Q Community - IBM Q Community page with list of upcoming events and latest programs.
• 📣 IBM Q Qiskit Slack Community - Slack Channel for Qiskit and quantum computing discussions.

Google

• 🎒 Google CirQ
• 🔗 Cirq Library - Python library for writing, manipulating, and optimizing NISQ circuits to run on quantum computers.

Microsoft

• 📺 Quantum Computing for Computer Scientists - Microsoft Research Talk on introductory quantum computing for computer scientists.

D-Wave

• 📣 D-Wave Leap Community - D-Wave System's Leap Community Forum.

Blogs & News

• 📰 Decodoku - Interesting posts on quantum computation, by James Wootton.
• 📰 Microsoft Quantum blog - Microsoft Quantum program-wide updates.

Podcasts

• 🔉 Meet the meQuanics - Interviews with key quantum computing figures.
• 🔉 Quantum Computing Now - Podcast by Ethan Hansen covering three main topics: the basics of quantum computing, interviews and the latest news.

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Contributing

Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

You can contribute in two ways.

• Adding resources, links and information to the repo.
• Improving the Quantum Learning Roadmap -> quantum_roadmap.drawio.

Adding information

If you have a suggestion that would make this repo better, please fork it and create a Pull Request. You can also simply open an issue with the tag "improvement".

Don't forget to give the project a star!

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/Resource)
3. Commit your Changes (git commit -m 'Add this resource')
4. Push to the Branch (git push origin feature/Resource)
5. Open a Pull Request

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Improving Roadmap

If you want to improve the Quantum Learning Roadmap file, please follow these steps:

1. Fork the project

2. Go to diagrams.net

3. Choose to "Save diagrams to" GitHub

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4. Choose "Open Existing Diagram"

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5. Authorize the app through OAuth2 if asked

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6. Choose the fork of this repository

7. Choose what branch the file you want to edit is on (the branch already needs to exist)

8. Choose the file you want to edit: quantum_roadmap.draw.io

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9. You will now see the online editor; you can now edit your diagram as you like

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10. When you make any changes; you will see a "Unsaved changes. Click here to save"-button.

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11. Be careful to save it in draw.io xml format.
12. When you are ready to save your changes into a commit, click that button and write your commit message.
13. Create a Pull Request to our main branch.
14. Once approved, the GitHub Action detects the change, and automatically renders the "raw" .drawio file into the format of your liking.

Generating Roadmap

Once a Pull Request is merged to main branch, a GitHub Action runs to generate the new roadmap files.

• GitHub Action -> Render Quantum Learning Stack -> main.yml The workflow will create the png and svgversions.

Nevertheless, you can import the quantum_roadmap.drawio file directly to diagrams.net and export it the format you desire.

But please, share any changes with us!

License

This project is licensed under the MIT license.
See LICENSE for more information.

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Acknowledgments