Building an OpenSource repo for guiding NOOBS --to-> PRO in QuantumAI 🚀
🤝 Feel free to contribute and help serve the community ! 🌎
- Intermediate Machine Learning and Deep Learning Knowledge.
- Basic idea about Quantum Mechanics
python -m venv .quantumvenv
source .quantumvenv/bin/activate # Windows: .quantumvenv\Scripts\activate
pip install -r requirements.txtEvery notebook in this repo runs end to end on a laptop - no quantum hardware needed.
🏋️ Want to practise? PRACTICE.md has 38 unsolved problems across four tiers - Beginner, Amateur, Semi-Pro and Pro - each with a hint, references, and a checkable "done when".
🗺️ New here? Start with the ROADMAP - it explains the order the problems are meant to be read in, roughly how long each stage takes, and the question each stage is answering.
➡️ Read the 0-ReadFIRST.ipynb to understand the required terminolgies that you will be encountering in the listed problems.
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Problem 1 : Flip a single bit (0 to 1 or 1 to 0) using a quantum gate
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Problem 2 : Creating superposition using the Hadamard (H) Gate
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Problem 3 : Create an entangled Bell State using CNOT Gate
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Problem 4 : Quantum Teleportation - Transfer the state of one qubit to another, using entanglement and classical communication.
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Problem 5 : Quantum State Classification - Introduces the idea of classifying quantum states based on measurement outcomes.
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Problem 6 : Quantum Feature Encoding - The task is to encode classical data (e.g., a numerical feature) into a quantum state.
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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.
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Problem 8 : Quantum Principal Component Analysis (QPCA) - Task is to find the principal components of a dataset encoded as quantum states.
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Problem 8.1 : Quantum Principal Component Analysis (QPCA) Application on a generated dataset - Apply QPCA techniques on a small generated dataset.
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Problem 9 : Quantum Support Vector Machine (QSVM) - Using QSVM determine the accuracy of a basic generated dataset.
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Problem 9.1 : Quantum Support Vector Machine (QSVM) - Application of QSVM on Iris Dataset
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Problem 9.2 : Quantum Support Vector Machine (QSVM) - Application of QSVM with PennyLane, and what actually makes something a kernel.
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Problem 10 : Variational Quantum Classifier (VQC) - The first model here that genuinely trains. The hybrid quantum-classical loop and the parameter-shift rule.
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Problem 11 : Quantum Neural Networks in Qiskit - The same model built with
EstimatorQNNandVQC, trained with a gradient-free optimizer. -
Problem 12 : Barren Plateaus - Measure for yourself why gradients vanish exponentially with qubit count, and why bigger quantum models are worse.
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Problem 13 : Data Re-uploading Classifier - The fix. A single qubit beats the two-qubit model of Problem 10.
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Problem 14 : Noise and Error Mitigation - Realistic noise models, readout error mitigation with a calibration matrix, and zero-noise extrapolation.
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Problem 15 : Variational Quantum Eigensolver (VQE) - Compute the ground state energy and full dissociation curve of the H₂ molecule.
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Problem 16 : QAOA - Solve MaxCut, and see how combinatorial optimization becomes a ground-state problem.
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Problem 17 : Grover's Algorithm - A provable O(√N) speedup, and the amplitude amplification primitive behind most QML speedup claims.
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Problem 18 : Real data, head to head - Breast cancer diagnosis with tuned classical ML vs a tuned quantum kernel, scored on accuracy and wall-clock time. Classical wins, and the best quantum kernel turns out to be the one with no entanglement.
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Problem 19 : Where quantum actually wins - On quantum-generated data the quantum kernel hits 100% while five tuned classical models top out at 80%, and the gap widens with qubit count.
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Problem 20 : Should you use quantum ML? - A validated decision procedure: centered kernel-target alignment, geometric difference, learning curves and a hardware cost estimate. Tested against the known answers from Problems 18 and 19.
< List will be updated as added >
- The Road to Quantum Artificial Intelligence
- Quantum Neuron: an elementary building block for machine learning on quantum computers
- Quantum algorithms for supervised and unsupervised machine learning
- Quantum Artificial Intelligence: A Brief Survey
- Barren plateaus in quantum neural network training landscapes - Problem 12
- Data re-uploading for a universal quantum classifier - Problem 13
- A variational eigenvalue solver on a photonic quantum processor - Problem 15
- A Quantum Approximate Optimization Algorithm - Problem 16
- A fast quantum mechanical algorithm for database search - Problem 17
- Supervised learning with quantum-enhanced feature spaces - Problem 19
- Power of data in quantum machine learning - Problem 20
- Quantum Machine Learning Book by S Pattanayak - Get this book from this repo.
- Overall Summarised Concepts - https://blog.paperspace.com/beginners-guide-to-quantum-machine-learning/
- Lecture Material PDF resources to learn https://github.com/Qiskit/platypus/tree/main/notebooks/summer-school/2021/resources
- Understand the DIRAC NOTATIONS and the basic representations here - https://youtu.be/MrLf6m_AFc0?si=Cjjj40mY11QCPNOd&t=101
- Introduction to Quantum Computing by Qiskit - https://www.youtube.com/playlist?list=PLOFEBzvs-VvrXTMy5Y2IqmSaUjfnhvBHR
- Quantum Machine Learning by Peter Wittek - https://www.youtube.com/playlist?list=PLmRxgFnCIhaMgvot-Xuym_hn69lmzIokg
- Quantum Machine Learning by Seth Lloyd (MIT) - https://www.youtube.com/watch?v=Lbndu5EIWvI
- https://quantummlhandbook.vercel.app/docs/get-started
- https://github.com/krishnakumarsekar/awesome-quantum-machine-learning
- https://qiskit-community.github.io/qiskit-machine-learning/tutorials/index.html
Connect with me on Linkedin - @shreyanbasuray 🪩
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