Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

Cambridge University Press eBooks

Two-Dimensional Tensor Network States

Tao Xiang

Published Aug 31, 2023• 0 citations
Publisher

Abstract

This chapter discusses the methods of solving PEPS or other two-dimensional tensor network states, including variational optimization and the annealing simulation. The variation optimization determines the local terms by minimizing the ground-state energy. The annealing simulation takes the full or simple update strategy to filter out the ground state through the imaginary time evolution. The nonlinear effect arises in evaluating the derivative of uniform PEPS and is avoided by utilizing automatic differentiation. Both variational optimization and the annealing simulation involve a contraction of double-layer tensor network states. This contraction is the primary technical barrier in the study of PEPS. A nested tensor network approach is introduced to combat this difficulty.

DOI
10.1017/9781009398671.023

Related papers

Implicit differentiation of tensor network algorithms

Lander Burgelman, Anna Francuz, Paul Brehmer +4 more

The current leading approach to the variational optimization of projected entangled-pair states (PEPS) is based on automatic differentiation, which allows for a convenient evaluation of the energy gradient with respect to the local variational degrees of freedom. However, evaluating the energy gradient not only remains a major computational bottleneck of the optimization procedure, but also suffers from frequent numerical instabilities. In this work, we adopt recent advances in implicit differentiation techniques to address these challenges in PEPS optimization. By reformulating the core step of the gradient computation in terms of a single characteristic equation for the contraction environment, we reduce the cost of the gradient computation and improve its scaling with the problem size. By choosing a suitable parametrization of this characteristic equation based on the intrinsic symmetries of the contraction environment, we can directly remove instabilities from the global gradient computation that would otherwise arise from the derivatives of subroutines of the contraction algorithm. Finally, we demonstrate how this approach drastically simplifies the practical implementation of stable gradient-based PEPS optimization.

quant-pharXiv:2607.15030•Jul 17, 2026•0 citations

Determination of MPS

Tao Xiang

This chapter introduces two commonly used methods of determining the local tensors of an MPS. The first is the variational optimization method, which determines an MPS by minimizing the energy expectation value. This method is equivalent to solving a generalized eigenequation around the extreme point of the ground-state energy. The second is an update method based on an imaginary time evolution, which cools down a quantum state from finite to zero temperature. We discuss three update approaches: update via canonicalization, full update, and simple update. For an MPS, the canonicalization approach is accurate and easy to implement. However, the full and simple update can be generalized to higher dimensions and applied to, for example, PEPS. The full update is a global minimization approach. It is accurate but has a higher computational cost than the simple update. The simple update is a local optimization approach based on an entanglement mean-field approximation and is easy to implement. Finally, we discuss the purification technique and apply it to evaluate the thermal density matrix or solve a quenched disorder problem in the framework of MPS.

Cambridge University Press eBooks•Aug 31, 2023•0 citations

Efficient 2D Tensor Network Simulation of Quantum Systems

Yuchen Pang, Tianyi Hao, Annika Dugad +2 more

Simulation of quantum systems is challenging due to the exponential size of the state space. Tensor networks provide a systematically improvable approximation for quantum states. 2D tensor networks such as Projected Entangled Pair States (PEPS) are well-suited for key classes of physical systems and quantum circuits. However, direct contraction of PEPS networks has exponential cost, while approximate algorithms require computations with large tensors. We propose new scalable algorithms and software abstractions for PEPS-based methods, accelerating the bottleneck operation of contraction and refactorization of a tensor subnetwork. We employ randomized SVD with an implicit matrix to reduce cost and memory footprint asymptotically. Further, we develop a distributed-memory PEPS library and study accuracy and efficiency of alternative algorithms for PEPS contraction and evolution on the Stampede2 supercomputer. We also simulate a popular near-term quantum algorithm, the Variational Quantum Eigensolver (VQE), and benchmark Imaginary Time Evolution (ITE), which compute ground states of Hamiltonians.

arXiv:2006.15234•International Conference for High Performance Computing, Networking, Storage and Analysis (SC)•Nov 1, 2020•17 citations

Gradient methods for variational optimization of projected entangled-pair states

Laurens Vanderstraeten, Jutho Haegeman, Philippe Corboz +1 more

The authors present a new method for variationally optimizing projected entangled-pair states (PEPS) in the thermodynamic limit based on the global energy functional and novel tensor contraction strategies. It is successfully benchmarked using Ising and Heisenberg Hamiltonians. The method is completely independent from previous PEPS implementations and improves on the variational energies and order parameters as compared to other state-of-the-art PEPS simulations. The algorithm presented here should prove valuable in simulating ground-state properties of strongly correlated quantum spin systems, can be used with other variational ansatzes, and can be applied to tensor network states in general.

arXiv:1606.09170•Physical review. B./Physical review. B•Oct 14, 2016•206 citations

Variational methods for contracting projected entangled-pair states

Laurens Vanderstraeten, Lander Burgelman, Boris Ponsioen +5 more

Projected entangled-pair states (PEPS) provide excellent variational wave functions for ground states of strongly correlated quantum systems in two dimensions. Determining the expectation value of such a PEPS wave function requires the contraction of a two-dimensional tensor network. Here, the authors show that this contraction can itself be formulated as a variational problem, allowing one to compare the accuracies of two commonly used contraction schemes. The authors find good agreement between the different schemes, demonstrating the robustness of PEPS simulations.

arXiv:2110.12726•Physical review. B./Physical review. B•May 25, 2022•26 citations

An introduction to infinite projected entangled-pair state methods for variational ground state simulations using automatic differentiation

Jan Naumann, Erik Lennart Weerda, Matteo Rizzi +2 more

Tensor networks capture large classes of ground states of phases of quantum matter faithfully and efficiently. Their manipulation and contraction has remained a challenge over the years, however. For most of the history, ground state simulations of two-dimensional quantum lattice systems using (infinite) projected entangled pair states have relied on what is called a time-evolving block decimation. In recent years, multiple proposals for the variational optimization of the quantum state have been put forward, overcoming accuracy and convergence problems of previously known methods. The incorporation of automatic differentiation in tensor networks algorithms has ultimately enabled a new, flexible way for variational simulation of ground states and excited states. In this work we review the state-of-the-art of the variational iPEPS framework, providing a detailed introduction to automatic differentiation, a description of a general foundation into which various two-dimensional lattices can be conveniently incorporated, and demonstrative benchmarking results.

arXiv:2308.12358•SciPost Physics Lecture Notes•Sep 10, 2024•18 citations
This chapter discusses the methods of solving PEPS or other two-dimensional tensor network states, including variational optimization and the annealing simulation. The variation optimization determines the local terms by minimizing the ground-state energy. The annealing simulation takes the full or simple update strategy to filter out the ground state through the imaginary time evolution. The nonlinear effect arises in evaluating the derivative of uniform PEPS and is avoided by utilizing automatic differentiation. Both variational optimization and the annealing simulation involve a contraction of double-layer tensor network states. This contraction is the primary technical barrier in the study of PEPS. A nested tensor network approach is introduced to combat this difficulty.
The current leading approach to the variational optimization of projected entangled-pair states (PEPS) is based on automatic differentiation, which allows for a convenient evaluation of the energy gradient with respect to the local variational degrees of freedom. However, evaluating the energy gradient not only remains a major computational bottleneck of the optimization procedure, but also suffers from frequent numerical instabilities. In this work, we adopt recent advances in implicit differentiation techniques to address these challenges in PEPS optimization. By reformulating the core step of the gradient computation in terms of a single characteristic equation for the contraction environment, we reduce the cost of the gradient computation and improve its scaling with the problem size. By choosing a suitable parametrization of this characteristic equation based on the intrinsic symmetries of the contraction environment, we can directly remove instabilities from the global gradient computation that would otherwise arise from the derivatives of subroutines of the contraction algorithm. Finally, we demonstrate how this approach drastically simplifies the practical implementation of stable gradient-based PEPS optimization.
This chapter introduces two commonly used methods of determining the local tensors of an MPS. The first is the variational optimization method, which determines an MPS by minimizing the energy expectation value. This method is equivalent to solving a generalized eigenequation around the extreme point of the ground-state energy. The second is an update method based on an imaginary time evolution, which cools down a quantum state from finite to zero temperature. We discuss three update approaches: update via canonicalization, full update, and simple update. For an MPS, the canonicalization approach is accurate and easy to implement. However, the full and simple update can be generalized to higher dimensions and applied to, for example, PEPS. The full update is a global minimization approach. It is accurate but has a higher computational cost than the simple update. The simple update is a local optimization approach based on an entanglement mean-field approximation and is easy to implement. Finally, we discuss the purification technique and apply it to evaluate the thermal density matrix or solve a quenched disorder problem in the framework of MPS.
Simulation of quantum systems is challenging due to the exponential size of the state space. Tensor networks provide a systematically improvable approximation for quantum states. 2D tensor networks such as Projected Entangled Pair States (PEPS) are well-suited for key classes of physical systems and quantum circuits. However, direct contraction of PEPS networks has exponential cost, while approximate algorithms require computations with large tensors. We propose new scalable algorithms and software abstractions for PEPS-based methods, accelerating the bottleneck operation of contraction and refactorization of a tensor subnetwork. We employ randomized SVD with an implicit matrix to reduce cost and memory footprint asymptotically. Further, we develop a distributed-memory PEPS library and study accuracy and efficiency of alternative algorithms for PEPS contraction and evolution on the Stampede2 supercomputer. We also simulate a popular near-term quantum algorithm, the Variational Quantum Eigensolver (VQE), and benchmark Imaginary Time Evolution (ITE), which compute ground states of Hamiltonians.
The authors present a new method for variationally optimizing projected entangled-pair states (PEPS) in the thermodynamic limit based on the global energy functional and novel tensor contraction strategies. It is successfully benchmarked using Ising and Heisenberg Hamiltonians. The method is completely independent from previous PEPS implementations and improves on the variational energies and order parameters as compared to other state-of-the-art PEPS simulations. The algorithm presented here should prove valuable in simulating ground-state properties of strongly correlated quantum spin systems, can be used with other variational ansatzes, and can be applied to tensor network states in general.
Projected entangled-pair states (PEPS) provide excellent variational wave functions for ground states of strongly correlated quantum systems in two dimensions. Determining the expectation value of such a PEPS wave function requires the contraction of a two-dimensional tensor network. Here, the authors show that this contraction can itself be formulated as a variational problem, allowing one to compare the accuracies of two commonly used contraction schemes. The authors find good agreement between the different schemes, demonstrating the robustness of PEPS simulations.
Tensor networks capture large classes of ground states of phases of quantum matter faithfully and efficiently. Their manipulation and contraction has remained a challenge over the years, however. For most of the history, ground state simulations of two-dimensional quantum lattice systems using (infinite) projected entangled pair states have relied on what is called a time-evolving block decimation. In recent years, multiple proposals for the variational optimization of the quantum state have been put forward, overcoming accuracy and convergence problems of previously known methods. The incorporation of automatic differentiation in tensor networks algorithms has ultimately enabled a new, flexible way for variational simulation of ground states and excited states. In this work we review the state-of-the-art of the variational iPEPS framework, providing a detailed introduction to automatic differentiation, a description of a general foundation into which various two-dimensional lattices can be conveniently incorporated, and demonstrative benchmarking results.