qubo-formulator

QUBO (Quadratic Unconstrained Binary Optimization) formulation skill for optimization problems

509 stars

bya5c-ai

View on GitHub Installation ↓

Best use case

qubo-formulator is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

QUBO (Quadratic Unconstrained Binary Optimization) formulation skill for optimization problems

Teams using qubo-formulator should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

You only need a quick one-off answer and do not need a reusable workflow.
You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/qubo-formulator/SKILL.md --create-dirs "https://raw.githubusercontent.com/a5c-ai/babysitter/main/library/specializations/domains/science/quantum-computing/skills/qubo-formulator/SKILL.md"

Manual Installation

Download SKILL.md from GitHub
Place it in .claude/skills/qubo-formulator/SKILL.md inside your project
Restart your AI agent — it will auto-discover the skill

How qubo-formulator Compares

Feature / Agent	qubo-formulator	Standard Approach
Platform Support	Not specified	Limited / Varies
Context Awareness	High	Baseline
Installation Complexity	Unknown	N/A

Frequently Asked Questions

What does this skill do?

QUBO (Quadratic Unconstrained Binary Optimization) formulation skill for optimization problems

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# 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

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