---
name: tensor-network-simulator-67492921c71f
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


---
Source attribution: a5c-ai
Original project: https://github.com/a5c-ai/babysitter/blob/feb68abe397acc14f32b34984975c48fedba2b33/library/specializations/domains/science/quantum-computing/skills/tensor-network-simulator/SKILL.md
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Mega AI Hub.

## Upstream license notice

# MIT License

Copyright (c) 2025 A5C AI

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.



# Mega AI Hub resource metadata

Creator / publisher: a5c-ai
Original project: https://github.com/a5c-ai/babysitter/blob/feb68abe397acc14f32b34984975c48fedba2b33/library/specializations/domains/science/quantum-computing/skills/tensor-network-simulator/SKILL.md
Attribution basis: Source repository record

Fields of work: Data & Analytics, Development & Engineering, Education & Research, Product Management
Roles: Data Scientist, ML / AI Engineer, Product Manager, Researcher, Technical Product Manager
Specialties: Specialty not specified
Tools: Itensor, Tensornetwork, Cotengra, Cutensornet, Quimb
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: a4d24268bd6d52a5fb01885fa21b6cc3af7858802dce6e8819366eef0b949c0c