REVIEW 1 minor 1 cited by
PEPSKit.jl supplies high-level algorithms for infinite projected entangled-pair state simulations of two-dimensional quantum systems with symmetry support.
Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →
T0 review · grok-4.3
2026-06-30 18:10 UTC pith:6RUOETN7
load-bearing objection PEPSKit.jl is a straightforward Julia port of iPEPS with added non-Abelian and fermionic symmetry support that fills a niche for users already in that ecosystem.
PEPSKit.jl: A Julia package for projected entangled-pair state simulations
The pith
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
Core claim
The authors state that PEPSKit.jl builds on tensor computations to deliver high-level algorithms for iPEPS simulations. These algorithms support both Abelian and non-Abelian symmetries together with fermionic systems and enable ground-state, time-evolution, and finite-temperature simulations in systems with different physical symmetries and lattice geometries. The features are shown through examples and technical benchmarks.
What carries the argument
The PEPSKit.jl package, which implements the high-level iPEPS algorithms that incorporate support for Abelian, non-Abelian, and fermionic symmetries.
Load-bearing premise
The high-level algorithms are implemented correctly and produce accurate results for the claimed symmetries and system types.
What would settle it
Running a benchmark on a standard two-dimensional model such as the Heisenberg antiferromagnet, then comparing the package output to results from an independent established method, would test whether the implementations match known values.
If this is right
- Ground-state properties of two-dimensional models with symmetries become computable at scale.
- Time evolution of fermionic systems on lattices can be followed without manual symmetry handling.
- Finite-temperature observables are accessible for a range of lattice geometries.
- Users gain the ability to switch between different symmetry sectors within the same simulation framework.
Where Pith is reading between the lines
- The package structure may allow straightforward addition of new lattice types or interaction terms by other developers.
- Researchers could use it to scan phase diagrams across symmetry classes that were previously hard to access uniformly.
- Integration with existing Julia tensor libraries could reduce setup time for hybrid classical-quantum studies.
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The manuscript presents PEPSKit.jl, a Julia package for simulating two-dimensional quantum many-body systems with infinite projected entangled-pair states (iPEPS). It builds on TensorKit.jl to provide high-level algorithms supporting Abelian and non-Abelian symmetries as well as fermionic systems, covering ground-state, time-evolution, and finite-temperature simulations on various lattice geometries, with these capabilities illustrated through examples and technical benchmarks.
Significance. If the package implements the described features correctly and remains publicly available with reproducible examples, the work supplies a useful open-source tool in the Julia ecosystem for tensor-network studies of strongly correlated systems. The emphasis on symmetry support and the provision of reproducible code and benchmarks constitute a clear strength for the condensed-matter simulation community.
minor comments (1)
- The abstract contains the string "PEPSKit$.$jl"; this appears to be a LaTeX formatting artifact and should be rendered consistently as PEPSKit.jl throughout the manuscript.
Simulated Author's Rebuttal
We thank the referee for their positive review of the manuscript, their recognition of the package features (including symmetry support for Abelian/non-Abelian and fermionic systems, as well as ground-state, time-evolution, and finite-temperature algorithms), and for recommending acceptance. No major comments were raised that require addressing.
Circularity Check
No significant circularity
full rationale
The manuscript is a software package announcement describing PEPSKit.jl for iPEPS simulations. It contains no derivations, equations, predictions, fitted parameters, or load-bearing self-citations that reduce to inputs by construction. Claims rest on the public availability of the package and reproducibility of provided examples/benchmarks, which are externally verifiable without circular logic. This is a standard, non-circular software description.
Axiom & Free-Parameter Ledger
read the original 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.
Figures
Forward citations
Cited by 1 Pith paper
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Implicit differentiation of tensor network algorithms
PEPS energy gradients can be computed by implicit differentiation of characteristic equations for the contraction environment, avoiding unstable subroutine backpropagation and reducing asymptotic cost.
Reference graph
Works this paper leans on
-
[1]
F. Verstraete, J. I. Cirac and V. Murg, Matrix product states, projected entangled pair states, and variational renormalization group methods for quantum spin systems, Adv. Phys. 57(2), 143 (2008), doi:10.1080/14789940801912366
-
[2]
J. C. Bridgeman and C. T. Chubb, Hand-waving and interpretive dance: an introductory course on tensor networks, J. Phys. A: Math. Theor. 50(22), 223001 (2017), doi:10.1088/1751-8121/aa6dc3
-
[3]
J. I. Cirac, D. P \'e rez-Garc \'i a , N. Schuch and F. Verstraete, Matrix product states and projected entangled pair states: Concepts , symmetries, theorems , Rev. Mod. Phys. 93(4), 045003 (2021), doi:10.1103/RevModPhys.93.045003
-
[4]
M. B. Hastings, An area law for one-dimensional quantum systems, J. Stat. Mech.: Theory Exp. 2007(08), P08024 (2007), doi:10.1088/1742-5468/2007/08/P08024
-
[5]
J. Eisert, M. Cramer and M. B. Plenio, Colloquium: Area laws for the entanglement entropy , Rev. Mod. Phys. 82(1), 277 (2010), doi:10.1103/RevModPhys.82.277
-
[6]
S. R. White, Density matrix formulation for quantum renormalization groups, Phys. Rev. Lett. 69, 2863 (1992), doi:10.1103/PhysRevLett.69.2863
-
[7]
F. Verstraete and J. I. Cirac, Valence-bond states for quantum computation, Phys. Rev. A 70(6), 060302 (2004), doi:10.1103/PhysRevA.70.060302
-
[8]
L. Vanderstraeten, J. Haegeman and F. Verstraete, Simulating excitation spectra with projected entangled-pair states, Phys. Rev. B 99, 165121 (2019), doi:10.1103/PhysRevB.99.165121
-
[9]
B. Ponsioen and P. Corboz, Excitations with projected entangled pair states using the corner transfer matrix method, Phys. Rev. B 101, 195109 (2020), doi:10.1103/PhysRevB.101.195109
-
[10]
B. Ponsioen, F. F. Assaad and P. Corboz, Automatic differentiation applied to excitations with projected entangled pair states, SciPost Phys. 12, 006 (2022), doi:10.21468/SciPostPhys.12.1.006
-
[11]
B. Ponsioen, J. Hasik and P. Corboz, Improved summations of n -point correlation functions of projected entangled-pair states, Phys. Rev. B 108, 195111 (2023), doi:10.1103/PhysRevB.108.195111
-
[12]
P. Corboz, T. M. Rice and M. Troyer, Competing States in the t - J Model: Uniform d -Wave State versus Stripe State , Phys. Rev. Lett. 113(4), 046402 (2014), doi:10.1103/PhysRevLett.113.046402
-
[13]
P. Czarnik, J. Dziarmaga and P. Corboz, Time evolution of an infinite projected entangled pair state: An efficient algorithm , Phys. Rev. B 99(3), 035115 (2019), doi:10.1103/PhysRevB.99.035115
-
[14]
J. D. Arias Espinoza and P. Corboz, Spectral functions with infinite projected entangled-pair states, Phys. Rev. B 110, 094314 (2024), doi:10.1103/PhysRevB.110.094314
-
[15]
A. Sinha, M. M. Rams, P. Czarnik and J. Dziarmaga, Finite-temperature tensor network study of the Hubbard model on an infinite square lattice , Phys. Rev. B 106(19), 195105 (2022), doi:10.1103/PhysRevB.106.195105
-
[16]
Y. Zhang, A. Sinha, M. M. Rams and J. Dziarmaga, Finite temperature dopant-induced spin reorganization explored via tensor networks in the two-dimensional t- J model , Phys. Rev. B 113(8), 085113 (2026), doi:10.1103/6pcg-qq4p
-
[17]
N. Schuch, M. M. Wolf, F. Verstraete and J. I. Cirac, Computational Complexity of Projected Entangled Pair States , Physical Review Letters 98(14), 140506 (2007), doi:10.1103/PhysRevLett.98.140506
-
[18]
T. Nishino and K. Okunishi, Corner Transfer Matrix Renormalization Group Method , J. Phys. Soc. Jpn. 65(4), 891 (1996), doi:10.1143/JPSJ.65.891
-
[19]
R. Or \'u s and G. Vidal, Simulation of two-dimensional quantum systems on an infinite lattice revisited: Corner transfer matrix for tensor contraction , Physical Review B 80(9), 094403 (2009), doi:10.1103/PhysRevB.80.094403
-
[20]
Renormalization algorithms for Quantum-Many Body Systems in two and higher dimensions
F. Verstraete and J. I. Cirac, Renormalization algorithms for quantum-many body systems in two and higher dimensions (2004), cond-mat/0407066
work page internal anchor Pith review Pith/arXiv arXiv 2004
-
[21]
J. Jordan, R. Or \'u s, G. Vidal, F. Verstraete and J. I. Cirac, Classical Simulation of Infinite-Size Quantum Lattice Systems in Two Spatial Dimensions , Phys. Rev. Lett. 101(25), 250602 (2008), doi:10.1103/PhysRevLett.101.250602
-
[22]
L. Vanderstraeten, L. Burgelman, B. Ponsioen, M. Van Damme, B. Vanhecke, P. Corboz, J. Haegeman and F. Verstraete, Variational methods for contracting projected entangled-pair states, Phys. Rev. B 105(19), 195140 (2022), doi:10.1103/PhysRevB.105.195140
-
[23]
Vidal, Efficient Classical Simulation of Slightly Entangled Quantum Computations , Phys
G. Vidal, Efficient Classical Simulation of Slightly Entangled Quantum Computations , Phys. Rev. Lett. 91(14), 147902 (2003), doi:10.1103/PhysRevLett.91.147902
-
[24]
Schollw \"o ck ,\ 10.1016/j.aop.2010.09.012 journal journal Ann
U. Schollw \"o ck, The density-matrix renormalization group in the age of matrix product states, Ann. Phys. (N. Y.) 326(1), 96 (2011), doi:10.1016/j.aop.2010.09.012
-
[25]
X.-Y. Zhang, Q. Yang, P. Corboz, J. Haegeman and W. Tang, Accelerating two-dimensional tensor network optimization by preconditioning, Phys. Rev. B 113, 125111 (2026), doi:10.1103/h396-yc28
- [26]
-
[27]
M. H. Gutknecht, A Brief Introduction to Krylov Space Methods for Solving Linear Systems , p. 53–62, Springer Berlin Heidelberg, ISBN 9783540463757, doi:10.1007/978-3-540-46375-7_5 (2007)
-
[28]
R. Murray, J. Demmel, M. W. Mahoney, N. B. Erichson, M. Melnichenko, O. A. Malik, L. Grigori, P. Luszczek, M. Dereziński, M. E. Lopes, T. Liang, H. Luo et al., Randomized Numerical Linear Algebra : A Perspective on the Field With an Eye to Software (2023), arXiv:2302.11474
-
[29]
H.-J. Liao, J.-G. Liu, L. Wang and T. Xiang, Differentiable Programming Tensor Networks , Phys. Rev. X 9(3), 031041 (2019), doi:10.1103/PhysRevX.9.031041
-
[30]
Christianson, Reverse accumulation and attractive fixed points, Optim
B. Christianson, Reverse accumulation and attractive fixed points, Optim. Methods Softw. 3(4), 311 (1994), doi:10.1080/10556789408805572
-
[31]
M. Fishman, S. White and E. M. Stoudenmire, The ITensor software library for tensor network calculations , SciPost Phys. Codebases p. 004 (2022), doi:10.21468/SciPostPhysCodeb.4
-
[32]
Gray, quimb: A Python package for quantum information and many-body calculations , J
J. Gray, quimb: A Python package for quantum information and many-body calculations , J. Open Source Softw. 3(29), 819 (2018), doi:10.21105/joss.00819
-
[33]
Y. Motoyama, T. Okubo, K. Yoshimi, S. Morita, T. Kato and N. Kawashima, TeNeS : Tensor network solver for quantum lattice systems , Comput. Phys. Commun. 279, 108437 (2022), doi:10.1016/j.cpc.2022.108437
-
[34]
Y. Motoyama, T. Okubo, K. Yoshimi, S. Morita, T. Aoyama, T. Kato and N. Kawashima, TeNeS-v2 : Enhancement for real-time and finite temperature simulations of quantum many-body systems , Comput. Phys. Commun. 315, 109692 (2025), doi:10.1016/j.cpc.2025.109692
-
[35]
M. Rams, G. Wojtowicz, A. Sinha and J. Hasik, YASTN : Yet another symmetric tensor networks; A Python library for Abelian symmetric tensor network calculations , SciPost Phys. Codebases p. 052 (2025), doi:10.21468/SciPostPhysCodeb.52
-
[36]
J. Hasik and contributors , peps-torch: Solving two-dimensional spin models with tensor networks (powered by PyTorch ) (2020), https://github.com/jurajHasik/peps-torch
work page 2020
-
[37]
J. Naumann, E. L. Weerda, M. Rizzi, J. Eisert and P. Schmoll, An introduction to infinite projected entangled-pair state methods for variational ground state simulations using automatic differentiation, SciPost Phys. Lect. Notes p. 086 (2024), doi:10.21468/SciPostPhysLectNotes.86
-
[38]
J. Bezanson, A. Edelman, S. Karpinski and V. B. Shah, Julia: A Fresh Approach to Numerical Computing , SIAM Rev. 59(1), 65 (2017), doi:10.1137/141000671
-
[39]
QuantumKitHub: A Julia ecosystem for tensor networks and quantum many-body physics (2024), https://github.com/QuantumKitHub
work page 2024
-
[40]
Q. Mortier, L. Devos, L. Burgelman, B. Vanhecke, N. Bultinck, F. Verstraete, J. Haegeman and L. Vanderstraeten, Fermionic tensor network methods, SciPost Phys. 18(1), 012 (2025), doi:10.21468/SciPostPhys.18.1.012
-
[41]
TensorKit.jl: A Julia package for large-scale tensor computations, with a hint of category theory
L. Devos and J. Haegeman, TensorKit .jl: A Julia package for large-scale tensor computations, with a hint of category theory (2025), arXiv:2508.10076
work page internal anchor Pith review Pith/arXiv arXiv 2025
-
[42]
P. Corboz and G. Vidal, Fermionic multiscale entanglement renormalization ansatz, Phys. Rev. B 80, 165129 (2009), doi:10.1103/PhysRevB.80.165129
-
[43]
L. Vanderstraeten, J. Haegeman, P. Corboz and F. Verstraete, Gradient methods for variational optimization of projected entangled-pair states, Phys. Rev. B 94(15), 155123 (2016), doi:10.1103/PhysRevB.94.155123
-
[44]
Corboz, Variational optimization with infinite projected entangled-pair states, Phys
P. Corboz, Variational optimization with infinite projected entangled-pair states, Phys. Rev. B 94(3), 035133 (2016), doi:10.1103/PhysRevB.94.035133
-
[45]
Don't Unroll Adjoint: Differentiating SSA-Form Programs
M. Innes, Don't Unroll Adjoint: Differentiating SSA-Form Programs (2019), arXiv:1810.07951
work page internal anchor Pith review Pith/arXiv arXiv 2019
-
[46]
P. Brehmer, L. Burgelman, Z.-Y. Yue and L. Devos, PEPSKit.jl: A Julia package for projected entangled-pair state simulations , doi:10.5281/zenodo.13938736 (2026), https://github.com/QuantumKitHub/PEPSKit.jl
-
[47]
F. Verstraete, M. M. Wolf, D. Perez-Garcia and J. I. Cirac, Criticality, the Area Law, and the Computational Power of Projected Entangled Pair States , Phys. Rev. Lett. 96, 220601 (2006), doi:10.1103/PhysRevLett.96.220601
-
[48]
T. Nishino and K. Okunishi, Corner Transfer Matrix Algorithm for Classical Renormalization Group , J. Phys. Soc. Jpn. 66(10), 3040 (1997), doi:10.1143/JPSJ.66.3040
-
[49]
Y. Zhang, Q. Yang and P. Corboz, Accelerating two-dimensional tensor network contractions using QR decompositions , Phys. Rev. B 113, L201106 (2026), doi:10.1103/ptls-kr9z
-
[50]
P. Corboz, J. Jordan and G. Vidal, Simulation of fermionic lattice models in two dimensions with projected entangled-pair states: Next-nearest neighbor Hamiltonians , Phys. Rev. B 82(24), 245119 (2010), doi:10.1103/PhysRevB.82.245119
-
[51]
J. Haegeman and F. Verstraete, Diagonalizing Transfer Matrices and Matrix Product Operators: A Medley of Exact and Computational Methods , Annu. Rev. Condens. Matter Phys. 8(1), 355 (2017), doi:10.1146/annurev-conmatphys-031016-025507
-
[52]
V. Zauner-Stauber , L. Vanderstraeten, M. T. Fishman, F. Verstraete and J. Haegeman, Variational optimization algorithms for uniform matrix product states, Phys. Rev. B 97(4), 045145 (2018), doi:10.1103/PhysRevB.97.045145
-
[53]
L. Devos, M. Van Damme and J. Haegeman, MPSKit.jl , Zenodo, doi:10.5281/zenodo.18792879 (2026)
-
[54]
B. Vanhecke, M. Van Damme, J. Haegeman, L. Vanderstraeten and F. Verstraete, Tangent-space methods for truncating uniform MPS , SciPost Phys. Core 4(1), 004 (2021), doi:10.21468/SciPostPhysCore.4.1.004
-
[55]
M. Hauru, M. Van Damme and J. Haegeman, Riemannian optimization of isometric tensor networks, SciPost Phys. 10(2), 040 (2021), doi:10.21468/SciPostPhys.10.2.040
-
[56]
A. Nietner, B. Vanhecke, F. Verstraete, J. Eisert and L. Vanderstraeten, Efficient variational contraction of two-dimensional tensor networks with a non-trivial unit cell, Quantum 4, 328 (2020), doi:10.22331/q-2020-09-21-328
-
[57]
N. Srebro and T. Jaakkola, Weighted Low-Rank Approximations , In Proceedings of the Twentieth International Conference on International Conference on Machine Learning, ICML '03, pp. 720--727. AAAI Press, Washington, DC, USA, ISBN 1-57735-189-4 (2003)
work page 2003
-
[58]
H. C. Jiang, Z. Y. Weng and T. Xiang, Accurate Determination of Tensor Network State of Quantum Lattice Models in Two Dimensions , Phys. Rev. Lett. 101(9), 090603 (2008), doi:10.1103/PhysRevLett.101.090603
-
[59]
Vidal, Classical Simulation of Infinite-Size Quantum Lattice Systems in One Spatial Dimension , Phys
G. Vidal, Classical Simulation of Infinite-Size Quantum Lattice Systems in One Spatial Dimension , Phys. Rev. Lett. 98(7), 070201 (2007), doi:10.1103/PhysRevLett.98.070201
-
[60]
H. N. Phien, J. A. Bengua, H. D. Tuan, P. Corboz and R. Or \'u s, Infinite projected entangled pair states algorithm improved: Fast full update and gauge fixing , Phys. Rev. B 92(3), 035142 (2015), doi:10.1103/PhysRevB.92.035142
-
[61]
R. Haghshenas and D. N. Sheng, U(1)-symmetric infinite projected entangled-pair states study of the spin-1/2 square J_1 - J_2 Heisenberg model , Phys. Rev. B 97(17), 174408 (2018), doi:10.1103/PhysRevB.97.174408
-
[62]
J. Dziarmaga, Time evolution of an infinite projected entangled pair state: Neighborhood tensor update , Phys. Rev. B 104(9), 094411 (2021), doi:10.1103/PhysRevB.104.094411
-
[63]
A. D. King, A. Nocera, M. M. Rams, J. Dziarmaga, R. Wiersema, W. Bernoudy, J. Raymond, N. Kaushal, N. Heinsdorf, R. Harris, K. Boothby, F. Altomare et al., Beyond-classical computation in quantum simulation, Science 388(6743), 199 (2025), doi:10.1126/science.ado6285
-
[64]
J. Tindall and M. Fishman, Gauging tensor networks with belief propagation, SciPost Phys. 15, 222 (2023), doi:10.21468/SciPostPhys.15.6.222
-
[65]
Nocedal, Updating Quasi-Newton Matrices with Limited Storage , Math
J. Nocedal, Updating Quasi-Newton Matrices with Limited Storage , Math. Comput. 35(151), 773 (1980)
work page 1980
-
[66]
A. Griewank and A. Walther, Evaluating Derivatives: Principles and Techniques of Algorithmic Differentiation , Society for Industrial and Applied Mathematics, second edn., doi:10.1137/1.9780898717761 (2008), https://epubs.siam.org/doi/abs/10.1137/1.9780898717761
-
[67]
A. Francuz, N. Schuch and B. Vanhecke, Stable and efficient differentiation of tensor network algorithms, Phys. Rev. Res. 7(1), 013237 (2025), doi:10.1103/PhysRevResearch.7.013237
-
[68]
E. Dagotto and A. Moreo, Phase diagram of the frustrated spin-1/2 Heisenberg antiferromagnet in 2 dimensions , Phys. Rev. Lett. 63(19), 2148 (1989), doi:10.1103/PhysRevLett.63.2148
-
[69]
H. Rosner, R. R. P. Singh, W. H. Zheng, J. Oitmaa and W. E. Pickett, High-temperature expansions for the J _1 - J _2 Heisenberg models: Applications to ab initio calculated models for Li _ 2 VOSiO _4 and Li _ 2 VOGeO _ 4 , Phys. Rev. B 67(1), 014416 (2003), doi:10.1103/PhysRevB.67.014416
-
[70]
J. Hasik, D. Poilblanc and F. Becca, Investigation of the N\'eel phase of the frustrated Heisenberg antiferromagnet by differentiable symmetric tensor networks , SciPost Phys. 10, 012 (2021), doi:10.21468/SciPostPhys.10.1.012
-
[71]
D. C. Liu and J. Nocedal, On the limited memory BFGS method for large scale optimization , Math. Program. 45(1), 503 (1989), doi:10.1007/BF01589116
-
[72]
W. W. Hager and H. Zhang, A New Conjugate Gradient Method with Guaranteed Descent and an Efficient Line Search , SIAM J. Optim. 16(1), 170 (2005), doi:10.1137/030601880
-
[73]
M. Lubasch, J. I. Cirac and M.-C. Ba\ nuls, Algorithms for finite projected entangled pair states, Phys. Rev. B 90, 064425 (2014), doi:10.1103/PhysRevB.90.064425
-
[74]
T. Shirakawa, T. Tohyama, J. Kokalj, S. Sota and S. Yunoki, Ground-state phase diagram of the triangular lattice Hubbard model by the density-matrix renormalization group method , Phys. Rev. B 96(20), 205130 (2017), doi:10.1103/PhysRevB.96.205130
-
[75]
S. Bird, S. Huber and J. Nys, Partial suppression of magnetism in the square lattice SU(3) Hubbard model , Phys. Rev. B 112, L161115 (2025), doi:10.1103/jfxt-9r1c
- [76]
-
[77]
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-
[78]
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