tensor-network-simulator
Tensor network-based simulation skill for large circuit approximation
Profile →npx skills add a5c-ai/babysitter --skill tensor-network-simulator --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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
- Purpose
- Capabilities
- Usage Guidelines
- Tools/Libraries
What does the tensor-network-simulator skill do?
Tensor network-based simulation skill for large circuit approximation
How do I install it?
Run `npx skills add a5c-ai/babysitter --skill tensor-network-simulator --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From a5c-ai/babysitter, a repository with 1,642 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.