QuantumAI-Noob2Pro

Building an OpenSource repo for guiding NOOBS --to-> PRO in QuantumAI 🚀

🤝 Feel free to contribute and help serve the community ! 🌎

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👨‍💻 Pre-requisites :

• Intermediate Machine Learning and Deep Learning Knowledge.
• Basic idea about Quantum Mechanics

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How to proceed with this repository ? 🤔

➡️ Read the 0-ReadFIRST.ipynb to understand the required terminolgies that you will be encountering in the listed problems.

• Problem 1 : Flip a single bit (0 to 1 or 1 to 0) using a quantum gate

• Problem 2 : Creating superposition using the Hadamard (H) Gate

• Problem 3 : Create an entangled Bell State using CNOT Gate

• Problem 4 : Quantum Teleportation - Transfer the state of one qubit to another, using entanglement and classical communication.

• Problem 5 : Quantum State Classification - Introduces the idea of classifying quantum states based on measurement outcomes.

• Problem 6 : Quantum Feature Encoding - The task is to encode classical data (e.g., a numerical feature) into a quantum state.

• Problem 7 : Quantum Nearest Neighbour(QNN) - Compare quantum states and classify a new data point based on its similarity to a set of labeled quantum states.

• Problem 8 : Quantum Principal Component Analysis (QPCA) - Task is to find the principal components of a dataset encoded as quantum states.

• Problem 8.1 : Quantum Principal Component Analysis (QPCA) Application on a generated dataset - Apply QPCA techniques on a small generated dataset.

• Problem 9 : Quantum Support Vector Machine (QSVM) - Using QSVM determine the accuracy of a basic generated dataset.

• Problem 9.1 : Quantum Support Vector Machine (QSVM) - Application of QSVM on Iris Dataset

• Problem 9.2 : Quantum Support Vector Machine (QSVM) - Application of QSVM with PennyLane.

< List will be updated as added >

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What to read/ study for this ? 🤓

Foundational Papers to Study : 📑

1. The Road to Quantum Artificial Intelligence
2. Quantum Neuron: an elementary building block for machine learning on quantum computers
3. Quantum algorithms for supervised and unsupervised machine learning
4. Quantum Artificial Intelligence: A Brief Survey

Books and Materials : 📚

1. Quantum Machine Learning Book by S Pattanayak - Get this book from this repo.
2. Overall Summarised Concepts - https://blog.paperspace.com/beginners-guide-to-quantum-machine-learning/
3. Lecture Material PDF resources to learn https://github.com/Qiskit/platypus/tree/main/notebooks/summer-school/2021/resources

Video Resources : 🎥

1. Understand the DIRAC NOTATIONS and the basic representations here - https://youtu.be/MrLf6m_AFc0?si=Cjjj40mY11QCPNOd&t=101
2. Introduction to Quantum Computing by Qiskit - https://www.youtube.com/playlist?list=PLOFEBzvs-VvrXTMy5Y2IqmSaUjfnhvBHR
3. Quantum Machine Learning by Peter Wittek - https://www.youtube.com/playlist?list=PLmRxgFnCIhaMgvot-Xuym_hn69lmzIokg
4. Quantum Machine Learning by Seth Lloyd (MIT) - https://www.youtube.com/watch?v=Lbndu5EIWvI

TOP OVERALL RESOURCES : 🥇

1. https://quantummlhandbook.vercel.app/docs/get-started
2. https://github.com/krishnakumarsekar/awesome-quantum-machine-learning
3. https://qiskit-community.github.io/qiskit-machine-learning/tutorials/index.html

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Connect with me on Linkedin - @shreyanbasuray 🪩

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