unitarylab/quantum-practices/quantum-machine-learning

Quantum Machine Learning Algorithms

Purpose

This file routes requests for variational and hybrid quantum-classical learning algorithms.

Use this category when the user asks about optimization with parameterized circuits, supervised classification, generative modeling, ground-state energy estimation, or continuous-variable quantum neural networks.

Routing Rules

• If the user asks about combinatorial optimization, MaxCut-style objectives, or alternating cost/mixer layers:
• Read ./qaoa/SKILL.md
• If the user asks about generative modeling of bitstring distributions or Born-machine training:
• Read ./qcbm/SKILL.md
• If the user asks about supervised classification with a variational quantum circuit:
• Read ./vqc/SKILL.md
• If the user asks about ground-state energy, Hamiltonian expectation minimization, or chemistry-style variational solving:
• Read ./vqe/SKILL.md
• If the user asks about the Fermi-Hubbard model, Jordan-Wigner mapping, lattice-model VQE, or magnetic-moment measurement:
• Read ./fermi-hubbard-vqe/SKILL.md
• If the user asks about continuous-variable neural networks, photonic modes, or CV quantum layers:
• Read ./cvqnn/SKILL.md

Available Leaf Skills

1. Quantum Approximate Optimization Algorithm: ./qaoa/SKILL.md
2. Quantum Circuit Born Machine: ./qcbm/SKILL.md
3. Variational Quantum Classifier: ./vqc/SKILL.md
4. Variational Quantum Eigensolver: ./vqe/SKILL.md
5. Continuous-Variable Quantum Neural Network: ./cvqnn/SKILL.md
6. Fermi-Hubbard VQE: ./fermi-hubbard-vqe/SKILL.md

Response Contract

1. Identify the learning objective: optimization, classification, generative modeling, energy minimization, or continuous-variable modeling.
2. Read the matching leaf skill before writing code or commands.
3. Keep model-specific APIs, training loops, and examples in the leaf skill.