using-pepskit

Configure and execute 2D tensor network simulations with PEPSKit.jl and TensorKit.jl.

60|92|Updated Apr 30, 2026
One-click install
npx skills add https://github.com/QuantumBFS/quantum.harness --skill using-pepskit
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: using-pepskit
Source: https://github.com/QuantumBFS/quantum.harness/tree/main/skills/using-pepskit
Command: npx skills add https://github.com/QuantumBFS/quantum.harness --skill using-pepskit

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires TensorKit, PEPSKit, QuadGK, MPSKit, and includes references (resource) components.

What problem does it solve?

This skill addresses the complexity of setting up and running high-performance 2D tensor network calculations, such as PEPS and CTMRG, by providing expert-curated convergence controls and verification checks.

Core Features & Use Cases

  • Convergence Management: Provides specific knobs for environment dimension, fixed-point tolerance, and iteration bounds to ensure stable physics.
  • Model Support: Includes built-in constructors for standard models like Heisenberg, Hubbard, and Ising, with support for U(1) and fermionic symmetries.
  • Use Case: Use this skill to perform a ground-state search for a 2D Heisenberg model or to reproduce a classical partition function calculation while ensuring the environment is properly converged.

Quick Start

Use the using-pepskit skill to initialize a PEPS ground state search for the Heisenberg model with a bond dimension of 2 and environment dimension of 20.

Frequently Asked Questions about using-pepskit

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I run a 2D PEPS ground-state search for the Heisenberg model in Julia?▼

To run a 2D PEPS ground-state search for the Heisenberg model in Julia, initialize your PEPS with a specified bond dimension and environment dimension, then use the provided convergence controls to execute variational optimization. The skill supports built-in model constructors for direct setup.

What is CTMRG boundary contraction and how does it affect tensor network simulations?▼

CTMRG boundary contraction is a technique used to approximate the infinite environment of a 2D tensor network. It affects simulations by requiring precise management of environment convergence and fixed-point tolerance to ensure stable physical results during variational ground-state optimization.

Can I use fermionic symmetries and U(1) symmetry sectors with PEPSKit?▼

Yes, you can use fermionic symmetries and U(1) symmetry sectors with PEPSKit. The skill includes built-in constructors for standard models like Heisenberg, Hubbard, and Ising, with explicit support for incorporating these symmetries into your tensor network simulations.

How do I manage bond dimensions and environment convergence in 2D tensor network calculations?▼

You manage bond dimensions and environment convergence in 2D tensor network calculations by adjusting specific knobs for environment dimension, fixed-point tolerance, and iteration bounds. These controls ensure stable physics and proper convergence during CTMRG boundary contractions.

What are the limitations of using 2D tensor networks for quantum simulation?▼

Limitations of using 2D tensor networks for quantum simulation include the need for precise gauge-fixed automatic differentiation and careful management of environment convergence. Incorrect bond dimensions or insufficient iteration bounds can lead to unstable physical results and failed optimizations.

Does using-pepskit work with TensorKit and MPSKit for variational optimization?▼

Yes, using-pepskit works with TensorKit and MPSKit. It facilitates 2D tensor network simulations using PEPSKit.jl and TensorKit.jl, relying on these dependencies for quantum and classical system calculations including variational ground-state optimization and CTMRG.