tensor-network-simulator
Tensor network-based simulation skill for large circuit approximation
Best use case
tensor-network-simulator is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Tensor network-based simulation skill for large circuit approximation
Teams using tensor-network-simulator 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
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/tensor-network-simulator/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How tensor-network-simulator Compares
| Feature / Agent | tensor-network-simulator | 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?
Tensor network-based simulation skill for large circuit approximation
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
# Tensor Network Simulator ## Purpose Provides expert guidance on tensor network-based quantum circuit simulation for approximate evaluation of circuits beyond state vector limits. ## Capabilities - MPS (Matrix Product State) simulation - PEPS simulation for 2D circuits - Contraction path optimization - Truncation error control - GPU-accelerated contraction - Circuit cutting support - Entanglement-limited approximation - Memory-time tradeoff tuning ## Usage Guidelines 1. **Structure Analysis**: Identify circuit entanglement structure 2. **Method Selection**: Choose MPS, PEPS, or general tensor network 3. **Bond Dimension**: Set appropriate truncation threshold 4. **Contraction Ordering**: Optimize contraction path for efficiency 5. **Error Monitoring**: Track approximation errors through simulation ## Tools/Libraries - TensorNetwork - quimb - ITensor - cuTensorNet (NVIDIA cuQuantum) - cotengra
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