quantum-machine-learning

name	quantum-machine-learning
description	Variational quantum algorithms for optimization and machine learning, including QAOA for combinatorial optimization, VQE for ground-state energy estimation, Fermi-Hubbard VQE for interacting-fermion ground-state simulation, VQC for supervised learning, QCBM for generative modeling, and CVQNN for continuous-variable workflows.

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, interacting fermions on an open chain, Jordan-Wigner mapping, Hubbard ground-state 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, generic energy minimization, Fermi-Hubbard simulation, 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.