Track
Quantum Machine Learning
Variational classifiers, kernel methods, QML in PennyLane, and an honest look at when QML beats classical ML.
- Level
- Intermediate → Advanced
- Tutorials
- 3
- Reading time
- ~69 min
Curriculum
- 01
Quantum ML Foundations: Encoding, Variational Circuits, and the Parameter-Shift Rule
Quantum machine learning trains parameterized quantum circuits as models for classical data. This tutorial covers the three classical-to-quantum encoding strategies, the parameter-shift rule that makes gradient-based training possible, and a complete PennyLane example training a variational classifier on a real dataset.
intermediate · ~24 min · prereq: Tutorial 14: QAOA for Combinatorial Optimization
- 02
Quantum Kernels and Feature Maps
Quantum kernels sidestep variational training entirely: they embed data into a quantum Hilbert space via a fixed feature map and use the inner product as a kernel for a classical SVM. This tutorial builds the ZZ feature map from Havlíček et al. 2019, implements a quantum SVC in Qiskit, and explains the reproducing-kernel view that unifies the approach.
advanced · ~22 min · prereq: Tutorial 15: QML Foundations
- 03
Is QML Worth It? A Skeptic's Benchmark
Most published QML results test against toy baselines that serious classical ML would demolish. This tutorial runs a bake-off — variational QML, quantum kernels, XGBoost, and a small MLP — on real tabular data, surveys the 'dequantization' results that have taken quantum advantages back, and gives an honest recommendation on when to reach for QML vs not.
advanced · ~23 min · prereq: Tutorial 16: Quantum Kernels and Feature Maps