Module 8
Discover how quantum computing and machine learning combine to create powerful hybrid algorithms.
Combine quantum mechanics with machine learning. Master quantum feature maps, Hilbert space kernels, parameter-shift gradient estimation, and Variational Quantum Classifiers (VQC).
Explore the intersection of quantum computing and artificial intelligence: four quadrants of QML, quantum speedups, and hybrid architectures.
Examine the bidirectional synergy: how Quantum accelerates AI, and how AI calibrates, controls, and optimizes quantum hardware.
Differentiate between Classical Data (images, tables) and Native Quantum Data (molecular wavefunctions, quantum sensor feeds).
Master the three primary strategies to load classical numbers into quantum registers: Basis Encoding, Angle Encoding, and Amplitude Encoding.
Understand how quantum feature maps project classical data into exponentially large Hilbert spaces to make non-linear data linearly separable.
Master Quantum Kernel Estimation: computing state overlaps |<Phi(x)|Phi(x’)>|^2 to power Quantum Support Vector Machines.
Master Parameterized Quantum Circuits (Ansätze), classical optimization loops, and the Parameter Shift Rule for computing quantum gradients.
Construct a complete hybrid supervised learning pipeline: Feature Map + Variational Ansatz + Measurement Readout + Classical Optimizer.
Explore multi-qubit Quantum Neural Networks: Strongly Entangling Layers, Quantum Convolutional Neural Networks (QCNN), and Expressibility.
Synthesize Module 8: integrate a Quantum Circuit as a PyTorch layer inside a Deep Convolutional Neural Network (Hybrid Quantum Transfer Learning).