tensor-network-simulator

name	tensor-network-simulator
description	Tensor network-based simulation skill for large circuit approximation
allowed-tools	["Bash","Read","Write","Edit","Glob","Grep"]
metadata	{"specialization":"quantum-computing","domain":"science","category":"simulation","phase":6}
graph	{"domains":["domain:quantum-computing"],"specializations":["specialization:quantum-computing"],"skillAreas":["skill-area:physics-simulation","skill-area:mathematical-reasoning","skill-area:dynamic-programming"],"workflows":["workflow:experiment-design"],"roles":["role:computational-scientist","role:research-scientist"]}

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