The complete QML syllabus.
From quantum state vectors and statistical learning to variational models, quantum kernels, scientific applications, complexity and deployment.
Chapter 01 · 4 lessonsMathematical & quantum foundationsLinear algebra for QML, Qubits and Bloch sphere, Measurement and expectation values, Entanglement and tensor productsChapter 02 · 4 lessonsClassical ML foundationsSupervised learning and risk, Optimization and gradients, Generalization and model selection, Kernel methods and feature spacesChapter 03 · 4 lessonsData encoding & feature mapsAngle encoding, Amplitude encoding, Basis and computational encoding, Data re-uploading and expressivityChapter 04 · 4 lessonsVariational quantum modelsParameterized quantum circuits, Variational quantum classifier, Quantum neural networks, Parameter-shift gradientsChapter 05 · 4 lessonsQuantum kernels & learning theoryFidelity quantum kernels, Quantum support vector machines, Kernel concentration, Generalization and inductive biasChapter 06 · 4 lessonsOptimization, trainability & noiseBarren plateaus, Local costs and layerwise training, Shot noise and measurement budgets, Hardware noise and error mitigationChapter 07 · 4 lessonsQuantum generative learningBorn machines, Quantum circuit GANs, Quantum autoencoders, Quantum Boltzmann modelsChapter 08 · 4 lessonsQuantum data & scientific MLLearning from quantum states, Quantum state tomography, Quantum phase recognition, Quantum sensing with MLChapter 09 · 4 lessonsReinforcement learning & sequential modelsQuantum reinforcement learning, Quantum policy gradients, Quantum reservoir computing, Time series and quantum recurrent ideasChapter 10 · 4 lessonsAlgorithms, complexity & advantageHHL and quantum linear systems, Quantum PCA and linear algebra, Quantum nearest neighbors, What counts as quantum advantage?Chapter 11 · 4 lessonsApplications & case studiesChemistry and materials, Finance and anomaly detection, Medical and biological signals, Computer vision and hybrid modelsChapter 12 · 4 lessonsResearch practice & deploymentBenchmark design and reproducibility, Software stack and simulation, Cloud architecture and MLOps, Open problems and research roadmap