PEPSKit.jl: A Julia package for projected entangled-pair state simulations

• URL: http://arxiv.org/abs/2605.19960v1
• Date: Tue, 19 May 2026 15:12:10 GMT
• Title: PEPSKit.jl: A Julia package for projected entangled-pair state simulations
• Abstract summary: PEPSKit.jl is a Julia package for simulating two-dimensional quantum many-body systems with infinite projected entangled-pair states (iPEPS)
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: We present PEPSKit.jl, a Julia package for simulating two-dimensional quantum many-body systems with infinite projected entangled-pair states (iPEPS). PEPSKit.jl builds on the TensorKit.jl package for tensor computations and provides high-level algorithms for iPEPS simulations that support both Abelian and non-Abelian symmetries, as well as fermionic systems. This work gives an overview of the main package features, which include support for ground-state, time-evolution, and finite-temperature simulations in systems with different physical symmetries and lattice geometries. These capabilities are illustrated through various examples and technical benchmarks.

Related papers

• Qiboml: towards the orchestration of quantum-classical machine learning [53.28668485072944]
  We present Qiboml, an open-source software library for orchestrating quantum and classical machine learning.<n>We showcase its functionalities, including diverse simulation options, noise-aware simulations and real-time error mitigation and calibration.
  arXiv  Detail & Related papers  (2025-10-13T18:00:00Z)
• FewBodyToolkit.jl: a Julia package for solving quantum few-body problems [0.0]
  FewBodyToolkit.jl is a Julia package for quantum few-body simulations.<n>It supports general two- and three-body systems in various spatial dimensions with arbitrary pair-interactions.
  arXiv  Detail & Related papers  (2025-10-06T02:41:58Z)
• TensorKit.jl: A Julia package for large-scale tensor computations, with a hint of category theory [0.0]
  This paper introduces the design philosophy, core functionalities, and distinctive features, including how to handle abelian, non-abelian, and anyonic symmetries.<n>We highlight the software's flexibility, performance, and its capability to extend to new tensor types and symmetries.
  arXiv  Detail & Related papers  (2025-08-13T16:35:11Z)
• SpinGlassPEPS.jl: Tensor-network package for Ising-like optimization on quasi-two-dimensional graphs [0.0]
  This work introduces SpinGlassPEPS.jl, a software package implemented in Julia to find low-energy configurations of generalized Potts models.<n>The modular architecture of SpinGlassPEPS.jl supports various contraction schemes and hardware acceleration.
  arXiv  Detail & Related papers  (2025-02-04T13:40:00Z)
• Equivariant Graph Network Approximations of High-Degree Polynomials for Force Field Prediction [62.05532524197309]
  equivariant deep models have shown promise in accurately predicting atomic potentials and force fields in molecular dynamics simulations. In this work, we analyze the equivariant functions for equivariant architecture, and introduce a novel equivariant network, named PACE. As experimented in commonly used benchmarks, PACE demonstrates state-of-the-art performance in predicting atomic energy and force fields.
  arXiv  Detail & Related papers  (2024-11-06T19:34:40Z)
• Projected Entangled Pair States with flexible geometry [0.0]
  Projected Entangled Pair States (PEPS) are a class of quantum many-body states that generalize Matrix Product States for one-dimensional systems to higher dimensions. PEPS have advanced understanding of strongly correlated systems, especially in two dimensions, e.g., quantum spin liquids. We present a PEPS algorithm to simulate low-energy states and dynamics defined on arbitrary, fluctuating, and densely connected graphs.
  arXiv  Detail & Related papers  (2024-07-30T19:03:52Z)
• GIC: Gaussian-Informed Continuum for Physical Property Identification and Simulation [60.33467489955188]
  This paper studies the problem of estimating physical properties (system identification) through visual observations. To facilitate geometry-aware guidance in physical property estimation, we introduce a novel hybrid framework. We propose a new dynamic 3D Gaussian framework based on motion factorization to recover the object as 3D Gaussian point sets. In addition to the extracted object surfaces, the Gaussian-informed continuum also enables the rendering of object masks during simulations.
  arXiv  Detail & Related papers  (2024-06-21T07:37:17Z)
• YASTN: Yet another symmetric tensor networks; A Python library for abelian symmetric tensor network calculations [0.0]
  We present an open-source tensor network Python library for quantum many-body simulations. At its core is an abelian-symmetric tensor, implemented as a sparse block structure managed by logical layer on top of dense multi-dimensional array backend. We show the library performance in simulations with infinite projected entangled-pair states, such as finding the ground states with AD, or simulating thermal states of the Hubbard model via imaginary time evolution.
  arXiv  Detail & Related papers  (2024-05-20T17:32:16Z)
• Gaussian Entanglement Measure: Applications to Multipartite Entanglement of Graph States and Bosonic Field Theory [50.24983453990065]
  An entanglement measure based on the Fubini-Study metric has been recently introduced by Cocchiarella and co-workers. We present the Gaussian Entanglement Measure (GEM), a generalization of geometric entanglement measure for multimode Gaussian states. By providing a computable multipartite entanglement measure for systems with a large number of degrees of freedom, we show that our definition can be used to obtain insights into a free bosonic field theory.
  arXiv  Detail & Related papers  (2024-01-31T15:50:50Z)
• Deep Learning Symmetries and Their Lie Groups, Algebras, and Subalgebras from First Principles [55.41644538483948]
  We design a deep-learning algorithm for the discovery and identification of the continuous group of symmetries present in a labeled dataset. We use fully connected neural networks to model the transformations symmetry and the corresponding generators. Our study also opens the door for using a machine learning approach in the mathematical study of Lie groups and their properties.
  arXiv  Detail & Related papers  (2023-01-13T16:25:25Z)
• BayesSimIG: Scalable Parameter Inference for Adaptive Domain Randomization with IsaacGym [59.53949960353792]
  BayesSimIG is a library that provides an implementation of BayesSim integrated with the recently released NVIDIA IsaacGym. BayesSimIG provides an integration with NVIDIABoard to easily visualize slices of high-dimensional posteriors.
  arXiv  Detail & Related papers  (2021-07-09T16:21:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.