qubo-formulator
QUBO (Quadratic Unconstrained Binary Optimization) formulation skill for optimization problems
Profile →npx skills add a5c-ai/babysitter --skill qubo-formulator --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# QUBO Formulator ## Purpose Provides expert guidance on formulating optimization problems as QUBO/Ising models for execution on quantum annealers and variational algorithms. ## Capabilities - Problem encoding to QUBO/Ising - Constraint handling (penalty methods) - Variable reduction techniques - D-Wave integration - QAOA cost Hamiltonian construction - Solution decoding - Embedding optimization - Penalty weight tuning ## Usage Guidelines 1. **Problem Definition**: Formalize optimization problem mathematically 2. **Binary Encoding**: Convert variables to binary representation 3. **Constraint Handling**: Add penalty terms for constraints 4. **QUBO Construction**: Build quadratic matrix form 5. **Solution Interpretation**: Decode binary solutions to original problem ## Tools/Libraries - D-Wave Ocean - PyQUBO - Qiskit Optimization - dimod - dwavebinarycsp
- Purpose
- Capabilities
- Usage Guidelines
- Tools/Libraries
What does the qubo-formulator skill do?
QUBO (Quadratic Unconstrained Binary Optimization) formulation skill for optimization problems
How do I install it?
Run `npx skills add a5c-ai/babysitter --skill qubo-formulator --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From a5c-ai/babysitter, a repository with 1,642 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.