variational-qml-model

Steps

1. Choose a data encoding and ansatz suited to the qubit budget.
2. Check gradient variance early for barren plateaus.
3. Train with a hybrid classical optimiser.
4. Track loss, accuracy and circuit evaluations.

Done When

• [ ] Gradient variance check recorded
• [ ] Training reproducible from a seed

Registry

Links

• Up: Quantum ML/AI Engineer skills
• Siblings: classical-baseline-check, quantum-kernel-methods
• Frameworks: chain-of-thought, risen, xml-structured
• Tests: testing guide