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Babysitter pyzx-simplifier

ZX-calculus based circuit simplification skill for advanced quantum circuit optimization

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 "$T/library/specializations/domains/science/quantum-computing/skills/pyzx-simplifier" ~/.claude/skills/a5c-ai-babysitter-pyzx-simplifier && rm -rf "$T"
manifest: library/specializations/domains/science/quantum-computing/skills/pyzx-simplifier/SKILL.md
tags
#zx-calculus#circuit-simplification#quantum-optimization#t-count-minimization#circuit-design
source content

PyZX Simplifier

Purpose

Provides expert guidance on ZX-calculus based circuit simplification, enabling powerful optimization through graphical quantum circuit representation.

Capabilities

  • ZX-diagram representation of circuits
  • Full simplification via ZX-calculus rules
  • T-count minimization
  • Clifford circuit extraction
  • Ancilla-free circuit optimization
  • Visualization of ZX-diagrams
  • Circuit-to-graph conversion
  • Equality verification

Usage Guidelines

  1. Conversion: Transform quantum circuits to ZX-diagrams for analysis
  2. Simplification: Apply ZX-calculus rewrite rules for optimization
  3. T-Minimization: Focus on T-gate reduction for fault-tolerant computing
  4. Extraction: Convert optimized ZX-diagrams back to circuits
  5. Visualization: Generate visual representations for understanding and debugging

Tools/Libraries

  • PyZX
  • ZX-calculus
  • NetworkX
  • Matplotlib