pyzx-simplifier

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

509 stars

bya5c-ai

View on GitHub Installation ↓

Best use case

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

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

Teams using pyzx-simplifier 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/pyzx-simplifier/SKILL.md --create-dirs "https://raw.githubusercontent.com/a5c-ai/babysitter/main/library/specializations/domains/science/quantum-computing/skills/pyzx-simplifier/SKILL.md"

Manual Installation

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

How pyzx-simplifier Compares

Feature / Agent	pyzx-simplifier	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?

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

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

# 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

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