devilkiller-ag / unravelquantum

Platform to get demo of all quantum algorithms.

UnravelQuantum

UnravelQuantum is your gateway to the world of quantum computing. Discover and explore quantum algorithms with ease, whether you're a newcomer or an experienced researcher. Dive into interactive tutorials, experiment with quantum circuits, and challenge yourself with quizzes. Join a vibrant community of quantum enthusiasts and embark on a quantum journey like never before.

Visit Unravel Quantum Website

Discover Quantum Algorithm

Delve into a comprehensive collection of quantum algorithms meticulously organized from beginner to advanced levels. Each algorithm comes with a wealth of educational resources. Learn about the underlying principles, explore detailed Qiskit Implementation, and or directly run the algorithm without going into the details and code.

Contribute to this project 🚀

We are welcoming contributors to this project. If you are interested to add demonstration to your favorite quantum algorithm to this project, just open an issue regarding it here. If you like this project, give us a star on GitHub: UnravelQuantum.

Contributors

• Ashmit JaiSarita Gupta (Website | LinkedIn | GitHub)

unravelquantum's Issues

Add Demonstration for Quantum Fourier Transform

Describe the quantum algorithm
A clear and concise abstract of what you want to implement.

Minimum required sections on the demonstration page
This page should have:

• Demonstration (Users should be able to tweak options. Show the output circuit, measurement results, and histogram to the user.)
• Detailed explanation including mathematics and examples (wherever required) divided into multiple sections.
• Generalized and modular code for the algorithm. (See the code given in Bernstein Vazirani and Deutsch Josza Algo Pages for reference).
• Links to external sources and further study material.
• About the author (small description and socials).

Add demonstration for Quantum Support Vector Machine with IRIS Dataset

Add demonstration for Quantum Support Vector Machine with IRIS Dataset as discussed in #4

^{Originally posted by devilkiller-ag January 20, 2024}
Add demonstration for QSVM

This page should have:

• Demonstration (Users should be able to tweak options.Show the output circuit, measurement results, histogram to the user.)
• Detailed explanation including mathematics and examples (wherever required) divided into multiple sections
• Generalised and modular code written (See the code given in Bernstein Vazirani and Deutsch Josza Algo Pages for reference)
• Links to external sources and further study material
• About the author (small description and socials)

Example:
Implementation of Deutsch Josza Algo . Here is the page for the Deutsch Josza Algo Demonstration created from this code.

Add a demonstration of Bell states

This page should have:

• Demonstration (Users should be able to tweak options.Show the output circuit, measurement results, histogram to the user.)
• Detailed explanation including mathematics and examples (wherever required) divided into multiple sections
• Generalised and modular code written (See the code given in Bernstein Vazirani and Deutsch Josza Algo Pages for reference)
• Links to external sources and further study material
• About the author (small description and socials)

Example:
Implementation of Deutsch Josza Algo . Here is the page for the Deutsch Josza Algo Demonstration created from this code.

Demonstration for Quantum Convolutional Neural Network with Chest X-ray Pneumonia Dataset

Title` of the quantum algorithm
Demonstration for Quantum Convolutional Neural Network with Chest X-ray Pneumonia Dataset

Description of the quantum algorithm
Quantum Convolutional Neural Networks (QCNNs) represent an innovative fusion of quantum computing principles with classical convolutional neural network (CNN) architectures. Unlike their classical counterparts, QCNNs harness the unique properties of quantum bits (qubits), including superposition and entanglement, to perform convolutional operations in a highly parallelized manner. These networks aim to enhance the processing capabilities of classical CNNs, particularly in tasks such as image recognition. QCNNs follow a hybrid quantum-classical approach, leveraging quantum resources for specific computations while relying on classical computing for other aspects. The integration of quantum features in QCNNs holds the potential to outperform classical counterparts in certain scenarios, though challenges such as quantum noise and error correction must be addressed to fully exploit the advantages of quantum computing in the realm of machine learning.

This page should have:

• Demonstration (Users should be able to tweak options. Show the output circuit, measurement results, and histogram to the user.)
• Detailed explanation including mathematics and examples (wherever required) divided into multiple sections.
• Generalized and modular code for the algorithm. (See the code given in Bernstein Vazirani and Deutsch Josza Algo Pages for reference).
• Links to external sources and further study material.
• About the author (small description and socials).

Add Contributors Section

Set up a GitHub workflow to add a contributors page.

Sample Example:
A similar implementation can be found here: uptane/uptane.github.io#163

Implement Simon's Algorithms

Create a demonstration page for Simon's Algorithms

This page should have:

• Demonstration (Users should be able to tweak options.Show the output circuit, measurement results, histogram to the user.)
• Detailed explanation including mathematics and examples (wherever required) divided into multiple sections
• Generalised and modular code written (See the code given in Bernstein Vazirani and Deutsch Josza Algo Pages for reference)
• Links to external sources and further study material
• About the author (small description and socials)

Example:
Implementation of Deutsch Josza Algo . Here is the page for the Deutsch Josza Algo Demonstration created from this code.