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Babysitter tensor-network-simulator

Tensor network-based simulation skill for large circuit approximation

install
source · Clone the upstream repo
git clone https://github.com/a5c-ai/babysitter
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/a5c-ai/babysitter "$T" && mkdir -p ~/.claude/skills && cp -r "$T/library/specializations/domains/science/quantum-computing/skills/tensor-network-simulator" ~/.claude/skills/a5c-ai-babysitter-tensor-network-simulator && rm -rf "$T"
manifest: library/specializations/domains/science/quantum-computing/skills/tensor-network-simulator/SKILL.md
tags
#quantum-simulation#tensor-network#circuit-approximation#mps-simulation#peps-simulation
source 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