tensor-network-simulator

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Tensor network-based simulation skill for large circuit approximation

AI & Automation 814 stars 53 forks Updated today MIT

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Skill Content

# 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

Details

Author
a5c-ai
Repository
a5c-ai/babysitter
Created
4 months ago
Last Updated
today
Language
JavaScript
License
MIT

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