PyZX Simplifier
SkillDev toolsZX-calculus based circuit simplification skill for advanced quantum circuit optimization
Instructions available. Your AI can read the instructions. Execution depends on the setup they require.
Account requirements not reviewed. Check the skill instructions before use; ahel provides instructions and does not run this skill.
Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.
Then ask your AI: use the PyZX Simplifier skill
What this skill tells your AI
The instructions your AI receives, as published by a5c-ai/babysitter in library/specializations/domains/science/quantum-computing/skills/pyzx-simplifier/SKILL.md and read by ahel’s review.
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
- Conversion: Transform quantum circuits to ZX-diagrams for analysis
- Simplification: Apply ZX-calculus rewrite rules for optimization
- T-Minimization: Focus on T-gate reduction for fault-tolerant computing
- Extraction: Convert optimized ZX-diagrams back to circuits
- Visualization: Generate visual representations for understanding and debugging
Tools/Libraries
- PyZX
- ZX-calculus
- NetworkX
- Matplotlib
Signals
- GitHub stars
- 2k
- Forks
- 112
- Last commit
- Sep 2026
Advanced
- Item type
- skill
- Key
pyzx-simplifier- Source
- github.com/a5c-ai/babysitter