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claude-codescience

Babysitter qubo-formulator

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

install
source · Clone the upstream repo
git clone https://github.com/a5c-ai/babysitter
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/a5c-ai/babysitter "$T" && mkdir -p ~/.claude/skills && cp -r "$T/library/specializations/domains/science/quantum-computing/skills/qubo-formulator" ~/.claude/skills/a5c-ai-babysitter-qubo-formulator && rm -rf "$T"
manifest: library/specializations/domains/science/quantum-computing/skills/qubo-formulator/SKILL.md
tags
#qubo-formulation#quantum-computing#optimization#constraint-handling#ising-model
source content

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