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REVIEW 3 major objections 4 minor 53 references

Three-dimensional real space renormalization group with well-controlled approximations

T0 review · 3 major / 4 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read Cube filtering turns Kadanoff's block-spin idea into a reliable 3D renormalization group.

desk verdict Genuinely new 3D cube filtering scheme that produces a critical fixed-point tensor where HOTRG fails, but the 'well-controlled' claim is not yet earned by the non-monotonic scaling-dimension data. read the letter →

arxiv 2412.13758 v2 pith:QLZNB7N7 submitted 2024-12-18 cond-mat.stat-mech hep-thphysics.comp-phquant-ph

Xinliang Lyu , Naoki Kawashima This is my paper · ORCID
classification cond-mat.stat-mechhep-thphysics.comp-phquant-ph PACS 05.10.Cc64.60.F
keywords real-spacerenormalizationgrouptensornetworkentanglementfilteringcube3DIsingmodelscalingdimensionscriticalfixed-pointHOTRG
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper claims that Kadanoff's block-spin renormalization idea, once reformulated as a tensor-network map and augmented with an entanglement-filtering step called cube filtering, becomes a quantitatively reliable real-space RG in three dimensions. The authors apply it to the cubic-lattice Ising model and report RG errors near the critical fixed point reduced from more than 20% for plain higher-order tensor renormalization to about 2% when more couplings are retained, and scaling dimensions of the spin and energy-density fields with relative errors as low as 0.4% and 0.1%. If correct, the method provides a systematically controllable real-space RG in 3D and, for the first time, a numerical critical fixed-point tensor in a high-dimensional tensor space, which carries a complete description of the universality class. The paper is explicit that the best exponent estimates occur at particular bond dimensions and that improvement with increasing bond dimension is not yet monotonic.

What carries the argument

The machinery is a tensor-network RG map $A \to A'$ composed of two stages. First, a cube filtering squeezes the bond dimension of each outer leg of the anchor tensor through filtering matrices $s_x, s_y, s_z$, optimized by maximizing the overlap between the $2\times2\times2$ cube built from the tensor and its transpositions and the filtered cube; the transposition trick is what imports lattice reflection symmetry and cuts the number of independent filtering matrices from 24 to 3. Second, an HOTRG-like block-tensor transformation coarse-grains the filtered network direction by direction (z, then y, then x) using isometric tensors with separate bond dimensions for inner and outer legs. The EF step targets the corner entanglement that a plain block-tensor map cannot eliminate, and the total computational cost is $O(\chi^{12.5})$.

What would settle it

Compare, for a small cubic lattice, the partition function obtained by contracting the transposed-cube tensor network against exact enumeration; any discrepancy at finite lattice size would falsify the transposition assumption. Alternatively, run the RG on a lattice model that lacks reflection symmetry with and without the transposition trick: if the estimated scaling dimensions shift outside the reported error bars, the trick is not symmetry-neutral.

Watch

Extended reading notes

Core claim

On the paper's own terms, the central discovery is that a block-tensor renormalization map in 3D can be made to flow to a genuine critical fixed point if each RG step is preceded by a cube filtering that removes short-range entanglement located at block corners. The cube filtering optimizes three filtering matrices, one per spatial direction, to maximize the overlap between the original 2x2x2 tensor patch and a filtered patch, using a transposition trick that enforces lattice reflection symmetry and reduces the number of independent filtering matrices from 24 to 3. With this enhancement, applied to the cubic-lattice Ising model, the RG errors stay stable near the fixed point and decrease from about 6% at bond dimension 6 to about 2% at bond dimension 14, and the linearized RG map yields estimates of $x_\sigma$ and $x_\epsilon$ whose best relative errors are 0.4% and 0.1% relative to conformal bootstrap values. The paper also reports higher scaling dimensions exhibiting conformal tower structure in 3D, which it notes has previously been seen numerically only in a fuzzy-sphere construction.

Load-bearing premise

The method's accuracy rests on the claim that transposing the tensor in the cube-filtering step leaves the partition function unchanged for the cubic-lattice Ising model; the proof of this is deferred to a later paper, and if it fails, the filtering matrices solve the wrong optimization problem.

Editorial extensions

If this is right

  • A numerical critical fixed-point tensor for the 3D Ising universality class is obtained, so the full RG spectrum, not just a handful of exponents, becomes accessible.
  • Linearization around the fixed point yields scaling dimensions whose best estimates (relative errors 0.4% for $x_\sigma$ and 0.1% for $x_\epsilon$) lie close to accepted high-precision values.
  • The RG error is stable with RG step rather than growing, which is the qualitative failure of the plain HOTRG approach in 3D.
  • The method is graph-independent, so the cube filtering can be inserted into other block-tensor schemes beyond the particular HOTRG-like map used here.
  • The fixed-point tensor can be used to extract operator product expansion coefficients, and the flow provides a link between real-space RG and conformal field theory data.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the deferred proof of the transposition trick goes through, the same construction should apply to other reflection-symmetric lattice models, such as $n$-state Potts models, and plausibly to (2+1)D quantum systems via the quantum-classical map.
  • The non-monotonic improvement with bond dimension, including the 'magical' parameter choices that give the best exponents, suggests that truncation errors in the filtering and block-tensor stages partially cancel; a diagnostic tracking each truncation error separately might allow a more controlled extrapolation to the $\chi \to \infty$ limit.
  • A direct test of systematic improvability would be to push the bond dimension beyond 14 using the cost-reduction ideas the paper cites; if the roughly 2% error keeps decreasing and the exponent estimates converge, the method becomes a practical alternative to Monte Carlo for 3D critical exponents.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 4 minor

Summary. The paper proposes a three-dimensional real-space renormalization group (RG) scheme for classical lattice models, combining a block-tensor (HOTRG-like) transformation with an entanglement filtering (EF) step. The EF step is designed to be graph-independent and to respect lattice reflection symmetry via a transposition trick. The method is benchmarked on the cubic-lattice Ising model. The authors report that the RG errors of the projective truncations are reduced from over 20% (for plain HOTRG) to about 2-6% with the EF enhancement, that a critical fixed-point tensor is obtained with adjacent-step tensor differences of order 4e-3 after gauge fixing, and that scaling dimensions x_sigma and x_epsilon extracted from the linearized RG map have relative errors as low as 0.4% and 0.1% in the best cases. The paper emphasizes that a fixed-point tensor contains much more information than few observables and argues that the method is a promising systematically improvable real-space RG in 3D.

Significance. If the central claims hold, the paper would be an important step toward quantitative real-space RG in three dimensions: it demonstrates a concrete entanglement-filtering construction that is graph-independent and symmetry-aware, provides a numerical fixed-point tensor for the 3D Ising model, reports substantial improvement over plain HOTRG, and makes the code publicly available. The scaling-dimension extraction from the linearized RG map is methodologically interesting and, if controlled, would complement conformal bootstrap and Monte Carlo results. However, the significance as currently stated depends crucially on the 'well-controlled' and 'systematically improvable' claims, which the presented data do not yet establish. The paper is transparent about the lack of clear improvement with bond dimension, but this transparency also exposes the gap between the headline best-case errors and a controlled convergence statement.

major comments (3)
  1. [§VII and Table I] The central claim that the proposed RG has 'well-controlled approximations' and is 'systematically improvable' is not supported by the data in Table I and Section VII. The relative errors for x_sigma are 5-8% at chi=6, 4-6% at chi=8, 3-6% at chi=11, and 0.4-0.5% at chi=14, while the errors for x_epsilon are 0.1-1%, 4-5%, 1-6%, and 2-4% for the same sequence. The best x_epsilon error occurs at the smallest bond dimension (chi=6) and the best x_sigma error at chi=14, with no monotonic trend; Section VII itself states there is 'no clear improvement when χ increases from 6 to 14' and refers to chi=14 as a 'magical' bond dimension. The RG errors that do decrease to about 2% are the projective truncation errors of the intermediate tensors (Section VI, Figure 3), not the errors in the scaling dimensions, and no argument is given that connects these two error measures. To support the headline claim, the authors should either show a systematic chi-convergence of the scaling dimensions (e.g., a sequence of chi values with decreasing error bars) or substantially soften the wording to present the accuracies as selected best cases rather than controlled extrapolations.
  2. [§V, Eq. (9a); Appendix A] The transposition trick that imposes lattice reflection symmetry and is used to define the cube-filtering approximation in Eq. (9a) is asserted to preserve the partition function exactly for reflection-symmetric models, but no proof is given. Appendix A says 'we will expound how to exploit lattice reflection symmetry in a TNRG setting in a coming paper' and only states that 'one can show' the partition function is invariant. This is load-bearing because the filtering matrices sx, sy, sz are optimized to maximize the overlap between the filtered state and the transposed target state; if the transposition identity is not exact, the optimized filtering matrices solve the wrong approximation problem and the fixed-point tensor and scaling dimensions inherit an uncontrolled bias. The authors should either include a complete proof of the partition-function invariance in this paper, or explicitly state the approximation status and provide a numerical test of the transposition step (for example, by comparing the partition function before and after the transposition on small finite lattices).
  3. [§VI, Figure 6] The demonstration of a fixed-point tensor relies on the Frobenius norm difference between adjacent RG steps reaching about 4e-3 at a single finite bond dimension (chi=8). While this is an improvement over HOTRG, it does not by itself constitute a controlled demonstration of a critical fixed point: the norm difference is not extrapolated to chi to infinity, and the dependence of the fixed-point tensor on chi is not shown. Since the subsequent extraction of scaling dimensions requires a genuine fixed point, the authors should report the chi-dependence of the fixed-point convergence and of the extracted scaling dimensions, or explicitly discuss the finite-bond-dimension uncertainty in the reported values.
minor comments (4)
  1. [§II] There are a few typographical issues: 'tensor-network representation' is followed by 'tenors' in the sentence about the partition function, and the phrase 'a nature metric' should read 'a natural metric'.
  2. [§VI] The sentence 'near the critical fixed point, the RG errors are reduced from more than 20% to about 6%' should clarify which bond dimension is used, since Figure 3 shows this for chi=6 and chi_s=chi_m=4, while nearby text discusses chi up to 22.
  3. [§VII] The word 'magical' to describe chi=14 is informal and, more importantly, signals the absence of a systematic trend. The authors should either explain the origin of the particularly good result at chi=14 or remove the term and replace it with a quantitative description.
  4. [Appendix C] The hyperparameter choice chi_i = chi^1.5 and chi_ii = chi^2 is described as a 'rule of thumb' from numerical experiments; it would strengthen the paper to show at least one example demonstrating that these choices indeed make the inner-leg projective truncation errors smaller than the outer-leg errors, as claimed.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: scaling-dimension predictions come from eigenvalues of the RG map and are benchmarked against external bootstrap values that never enter the algorithm; remaining concerns are about convergence and a deferred proof, not input-output equivalence.

full rationale

The derivation is self-contained with respect to the quantities claimed as predictions. The scaling dimensions x_sigma and x_epsilon are read off from eigenvalues of the linearized EF-enhanced block-tensor RG map via Eq. (1); the conformal bootstrap values are used only as a posteriori benchmarks (Table I) and never enter the construction of the filtering matrices, the HOTRG-like truncations, or the fixed-point search. The filtering matrices in Eq. (9a) are optimized by maximizing an overlap with a target tensor-network state, an internal variational criterion, not by matching the bootstrap exponents. The only self-citations (Refs. [20] and [40]) are not load-bearing in a circular sense: Ref. [20]'s claim about 3D entanglement growth is corroborated by the paper's own HOTRG runs (errors above 30%), and Ref. [40] provides a published gauge-fixing/linearization technique that is code-reproduced (Ref. [44]) and does not assume the target scaling dimensions. The main non-circularity concerns are (i) the 'magical' bond-dimension selection in Table I and the absence of monotonic chi-convergence, which undercut the 'systematically improvable' wording but constitute a robustness/correctness issue, not a reduction of the prediction to an input; and (ii) the transposition trick's partition-function invariance is asserted in Appendix A with the proof deferred to a future paper, an unverified assumption that could bias the fixed point but again is not an input-output identity. No equation in the paper reduces to another by construction, so no circular step is identified.

Assumptions & free parameters 4 free parameters · 5 assumptions · 0 invented entities

The method does not invent new physics entities, but it carries several unproved or tuned ingredients: the bond dimensions and hyperparameters chi_s, chi_m, chi_i, chi_ii; the transposition trick's exactness; and the standard truncation assumption that finite bond dimension captures the relevant RG directions. The scaling dimensions themselves are compared with, not fitted to, conformal bootstrap values.

free parameters (4)
  • chi (bond dimension) = 6, 8, 11, 14
    Truncation dimension controlling how many couplings are retained; chosen by hand. The accuracy claims depend on it, but it is a standard truncation parameter, not fitted to the conformal bootstrap values.
  • chi_s (filtering bond dimension) = 4, 5, 6, 6 for the four chi values
    Squeezes the bond dimension during cube filtering; tuned so the filtering error stays below the RG truncation errors.
  • chi_m (intermediate outer-leg bond dimension) = 4, 8, 8, 10 for the four chi values
    Tunable in the HOTRG-like block-tensor step; selected to keep the block-tensor errors stable with RG step.
  • chi_i = chi^1.5 and chi_ii = chi^2 (inner bond dimensions) = rule of thumb from numerical experiments
    Chosen so that inner-leg projective truncation errors are smaller than outer-leg errors; stated as a heuristic in Appendix C, not derived.
assumptions (5)
  • standard math The tensor-network representation of the partition function and the exact block-tensor map preserve the partition function.
    Section II, Eq. (5): this is the standard basis of TNRG and is not in question.
  • domain assumption Truncating tensor legs to a finite bond dimension chi retains the RG-relevant directions of the 3D Ising fixed point.
    Sections II and VI; all TNRG methods rely on this truncation assumption, and the paper provides only empirical evidence, not a proof.
  • ad hoc to paper The transposition trick preserves the partition function for reflection-symmetric lattice models and enforces lattice reflection symmetry.
    Invoked in Eq. (7), Eq. (9), and Appendix A; the proof is deferred to a future paper. If false, the filtering optimizes the wrong object.
  • standard math Linearized RG eigenvalues yield scaling dimensions through b^{d-x_i} = lambda_i.
    Eq. (1) and Section VI; standard RG relation, with subtleties noted by the authors via Ref [38].
  • domain assumption Spin-flip Z2 symmetry and lattice reflection symmetry charges cleanly separate sectors so that scaling dimensions can be extracted unambiguously.
    Section VI and Figure 4 caption; the authors impose these symmetries to avoid relevant perturbations, but the numerical separation is not proven to be exact.

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Cite this review

Pith. "Pith review of Three-dimensional real space renormalization group with well-controlled approximations." pith.science (2026). https://pith.science/paper/QLZNB7N7

@misc{pith2026241213758,
  author       = {Pith},
  title        = {Pith review of: Three-dimensional real space renormalization group with well-controlled approximations},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/QLZNB7N7}},
  note         = {Machine review of arXiv:2412.13758}
}
read the original abstract

We make Kadanoff's block idea into a reliable three-dimensional (3D) real space renormalization group (RG) method. Kadanoff's idea, expressed in spin representation, offers a qualitative intuition for clarifying scaling behavior in criticality, but has difficulty as a quantitative tool due to uncontrolled approximations. A tensor-network reformulation equips the block idea with a measure of RG errors. In 3D, we propose an entanglement filtering scheme to enhance such a block-tensor map, with the lattice reflection symmetry exploited. When the proposed RG is applied to the cubic-lattice Ising model, the RG errors are reduced to about 2% by retaining more couplings. The estimated scaling dimensions of the two relevant fields have errors 0.4% and 0.1% in the best case, compared with the accepted values. The proposed RG is promising as a systematically-improvable real space RG method in 3D. The unique feature of our method is its ability to numerically obtain a 3D critical fixed point in a high-dimensional tensor space. A fixed-point tensor contains much more information than a handful of observables estimated in conventional techniques for analyzing critical systems.

Figures

Figures reproduced from arXiv: 2412.13758 by the authors.

Figure 2
Figure 2. The general principle for integrating entanglement [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. (Upper panel) The flow of RG errors of the go-to block-tensor scheme in 3D (HOTRG with [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 4
Figure 4. Estimates of scaling dimensions using a simple block-tensor scheme, the HOTRG, and the proposed RG, which is [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
Figures from the paper (2 more)
Figure 5
Figure 5. Figure 5: RG flow of the degeneracy index X before and after entanglement filtering (a) Just block-tensor RG (the HOTRG) for χ = 8. Without entanglement filtering, the difference only decreases to about 5 × 10−2 until RG step n = 5, after which the difference starts to grow. (b)…
Figure 6
Figure 6. Figure 6: For the tensor RG flow generated at the estimated critical temperature, we plot the Frobenius norm of the difference [PITH_FULL_IMAGE:figures/full_fig_p009_6.png]

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Works this paper leans on

53 extracted references · 33 canonical work pages

  1. [1]

    K. G. Wilson, Renormalization group and critical phe- nomena. i. renormalization group and the kadanoff scaling picture, Phys. Rev. B 4, 3174 (1971)

    work page 1971

  2. [2]

    K. G. Wilson, Renormalization group and critical phe- nomena. ii. phase-space cell analysis of critical behavior, Phys. Rev. B 4, 3184 (1971)

    work page 1971

  3. [3]

    K. G. Wilson and M. E. Fisher, Critical exponents in 3.99 dimensions, Phys. Rev. Lett. 28, 240 (1972)

    work page 1972

  4. [4]

    L. P. Kadanoff, Scaling laws for ising models near Tc, Physics Physique Fizika 2, 263 (1966)

    1966

  5. [5]

    Kardar, Statistical Physics of Fields(Cambridge Uni- versity Press, 2007)

    M. Kardar, Statistical Physics of Fields(Cambridge Uni- versity Press, 2007)

    work page 2007

  6. [6]

    A. A. Migdal, Phase transitions in gauge and spin-lattice systems, Zh. Eksp. Teor. Fiz. 69, 1457 (1975)

    work page 1975

  7. [7]

    L. P. Kadanoff, Notes on migdal’s recursion formulas, Annals of Physics 100, 359 (1976)

    work page 1976

  8. [8]

    Martinelli and G

    G. Martinelli and G. Parisi, A systematical improvement of the migdal recursion formula, Nuclear Physics B 180, 201 (1981)

    work page 1981

Show all 53 references
  1. [9]

    L. P. Kadanoff, Statistical Physics(World Scientific, 2000) see chapter 14

    2000

  2. [10]

    With some modifications, it can also be applied to any (2+1)D quantum system

  3. [11]

    Levin and C

    M. Levin and C. P. Nave, Tensor renormalization group approach to two-dimensional classical lattice models, Phys. Rev. Lett. 99, 120601 (2007)

    2007

  4. [12]

    Evenbly, Algorithms for tensor network renormaliza- tion, Phys

    G. Evenbly, Algorithms for tensor network renormaliza- tion, Phys. Rev. B 95, 045117 (2017)

    2017

  5. [13]

    Hauru, C

    M. Hauru, C. Delcamp, and S. Mizera, Renormalization of tensor networks using graph-independent local trunca- tions, Phys. Rev. B 97, 045111 (2018)

    2018

  6. [14]

    Evenbly, Gauge fixing, canonical forms, and optimal truncations in tensor networks with closed loops, Phys

    G. Evenbly, Gauge fixing, canonical forms, and optimal truncations in tensor networks with closed loops, Phys. Rev. B 98, 085155 (2018)

    2018

  7. [15]

    F. Kos, D. Poland, D. Simmons-Duffin, and A. Vichi, Precision islands in the ising and o(n) models, Journal of High Energy Physics 2016, 10.1007/jhep08(2016)036 (2016)

    2016 doi

  8. [16]

    Chang, V

    C.-H. Chang, V. Dommes, R. S. Erramilli, A. Homrich, P. Kravchuk, A. Liu, M. S. Mitchell, D. Poland, and D. Simmons-Duffin, Bootstrapping the 3d ising stress tensor (2024), arXiv:2411.15300 [hep-th]

    2024 arXiv

  9. [17]

    S. R. White, Density matrix formulation for quantum renormalization groups, Phys. Rev. Lett. 69, 2863 (1992)

    1992

  10. [18]

    Vidal, Efficient classical simulation of slightly entan- gled quantum computations, Phys

    G. Vidal, Efficient classical simulation of slightly entan- gled quantum computations, Phys. Rev. Lett. 91, 147902 (2003)

    2003

  11. [19]

    Vidal, Efficient simulation of one-dimensional quantum many-body systems, Phys

    G. Vidal, Efficient simulation of one-dimensional quantum many-body systems, Phys. Rev. Lett. 93, 040502 (2004). 11

    2004

  12. [20]

    Lyu and N

    X. Lyu and N. Kawashima, Essential difference between 2D and 3D from the perspective of real-space renormal- ization group (2023), arXiv:2311.05891 [cond-mat.stat- mech]

    2023

  13. [21]

    Z. Y. Xie, J. Chen, M. P. Qin, J. W. Zhu, L. P. Yang, and T. Xiang, Coarse-graining renormalization by higher-order singular value decomposition, Phys. Rev. B 86, 045139 (2012)

    2012

  14. [22]

    Adachi, T

    D. Adachi, T. Okubo, and S. Todo, Anisotropic tensor renormalization group, Phys. Rev. B 102, 054432 (2020)

    2020

  15. [23]

    Kadoh and K

    D. Kadoh and K. Nakayama, Renormalization group on a triad network (2019), arXiv:1912.02414 [hep-lat]

    2019 arXiv

  16. [24]

    Adachi, T

    D. Adachi, T. Okubo, and S. Todo, Bond-weighted tensor renormalization group, Phys. Rev. B 105, L060402 (2022)

    2022

  17. [25]

    J. B. Kogut, An introduction to lattice gauge theory and spin systems, Rev. Mod. Phys. 51, 659 (1979)

    1979

  18. [26]

    Shankar, Quantum Field Theory and Condensed Mat- ter: An Introduction(Cambridge University Press, 2017)

    R. Shankar, Quantum Field Theory and Condensed Mat- ter: An Introduction(Cambridge University Press, 2017)

    2017

  19. [27]

    Evenbly, Example codes: TRG, tensors.net/p-trg (2019)

    G. Evenbly, Example codes: TRG, tensors.net/p-trg (2019)

    2019

  20. [28]

    Gu and X.-G

    Z.-C. Gu and X.-G. Wen, Tensor-entanglement-filtering renormalization approach and symmetry-protected topo- logical order, Phys. Rev. B 80, 155131 (2009)

    2009

  21. [29]

    Vidal, Entanglement renormalization, Phys

    G. Vidal, Entanglement renormalization, Phys. Rev. Lett. 99, 220405 (2007)

    2007

  22. [30]

    Evenbly and G

    G. Evenbly and G. Vidal, Tensor network renormalization, Phys. Rev. Lett. 115, 180405 (2015)

    2015

  23. [31]

    Yang, Z.-C

    S. Yang, Z.-C. Gu, and X.-G. Wen, Loop optimization for tensor network renormalization, Phys. Rev. Lett. 118, 110504 (2017)

    2017

  24. [32]

    M. Bal, M. Mari¨ en, J. Haegeman, and F. Verstraete, Renormalization group flows of hamiltonians using tensor networks, Phys. Rev. Lett. 118, 250602 (2017)

    2017

  25. [33]

    Ying, Tensor network skeletonization, Multiscale Mod- eling & Simulation 15, 1423 (2017)

    L. Ying, Tensor network skeletonization, Multiscale Mod- eling & Simulation 15, 1423 (2017)

    2017

  26. [34]

    Harada, Entanglement branching operator, Phys

    K. Harada, Entanglement branching operator, Phys. Rev. B 97, 045124 (2018)

    2018

  27. [35]

    Singh, R

    S. Singh, R. N. C. Pfeifer, and G. Vidal, Tensor network decompositions in the presence of a global symmetry, Phys. Rev. A 82, 050301 (2010)

    2010

  28. [36]

    Singh, R

    S. Singh, R. N. C. Pfeifer, and G. Vidal, Tensor network states and algorithms in the presence of a global u(1) symmetry, Phys. Rev. B 83, 115125 (2011)

    2011

  29. [37]

    Rychkov, EPFL Lectures on Conformal Field Theory in D ≥ 3 Dimensions (Springer International Publishing, 2017)

    S. Rychkov, EPFL Lectures on Conformal Field Theory in D ≥ 3 Dimensions (Springer International Publishing, 2017)

    2017

  30. [38]

    N. Ebel, T. Kennedy, and S. Rychkov, Transfer ma- trix and lattice dilatation operator for high-quality fixed points in tensor network renormalization group (2024), arXiv:2409.13012 [cond-mat.stat-mech]

    2024

  31. [39]

    (7) are called physical legs, while the black solid legs virtual legs

    When a tensor network is used as a ground state ansatz, like a matrix product state (MPS) or a projected entangled pair state (PEPS), the blue wavy legs on the left-hand side of Eq. (7) are called physical legs, while the black solid legs virtual legs

  32. [40]

    X. Lyu, R. G. Xu, and N. Kawashima, Scaling dimensions from linearized tensor renormalization group transforma- tions, Phys. Rev. Res. 3, 023048 (2021)

    2021

  33. [41]

    Ebel, 3D tensor renormalisation group at high tem- peratures, Annales Henri Poincar´ e 10.1007/s00023-024- 01464-9 (2024)

    N. Ebel, 3D tensor renormalisation group at high tem- peratures, Annales Henri Poincar´ e 10.1007/s00023-024- 01464-9 (2024)

    2024 doi

  34. [42]

    It fuses two tensor leg i, jinto a single tensor leg α

    An isometric tensor pijα satisfies P ij pijαpijβ = δαβ. It fuses two tensor leg i, jinto a single tensor leg α. A pair of p head-by-head contracting together, P α pijαpmnα, forms a projection operator from index pair ( ij) to ( mn), or vice versa

  35. [43]

    N. Ebel, T. Kennedy, and S. Rychkov, Rotations, nega- tive eigenvalues, and newton method in tensor network renormalization group (2024), arXiv:2408.10312 [cond- mat.stat-mech]

    2024

  36. [44]

    Lyu, Entanglement Filtering Renormalization Group in 3D, github.com/brucelyu/efrg3D (2024)

    X. Lyu, Entanglement Filtering Renormalization Group in 3D, github.com/brucelyu/efrg3D (2024)

    2024

  37. [45]

    W. Zhu, C. Han, E. Huffman, J. S. Hofmann, and Y.- C. He, Uncovering conformal symmetry in the 3d ising transition: State-operator correspondence from a quan- tum fuzzy sphere regularization, Phys. Rev. X 13, 021009 (2023)

    2023

  38. [46]

    Guo and T.-C

    W. Guo and T.-C. Wei, Tensor network methods for extracting conformal field theory data from fixed-point tensors and defect coarse graining, Phys. Rev. E 109, 034111 (2024)

    2024

  39. [47]

    Kennedy and S

    T. Kennedy and S. Rychkov, Tensor rg approach to high- temperature fixed point, Journal of Statistical Physics 187, 10.1007/s10955-022-02924-4 (2022)

    2022 doi

  40. [48]

    Kennedy and S

    T. Kennedy and S. Rychkov, Tensor renormalization group at low temperatures: Discontinuity fixed point, Annales Henri Poincar´ e25, 773–841 (2023)

    2023

  41. [49]

    A. B. Zamolodchikov, Irreversibility of the flux of the renormalization group in a 2d field theory, Soviet Journal of Experimental and Theoretical Physics Letters 43, 730 (1986)

    1986

  42. [50]

    Casini and M

    H. Casini and M. Huerta, A c-theorem for entanglement entropy, Journal of Physics A: Mathematical and Theo- retical 40, 7031 (2007)

    2007

  43. [51]

    Casini, M

    H. Casini, M. Huerta, R. C. Myers, and A. Yale, Mutual information and the F -theorem, Journal of High Energy Physics 2015, 10.1007/jhep10(2015)003 (2015)

    2015 doi

  44. [52]

    Nishioka, Entanglement entropy: Holography and renormalization group, Rev

    T. Nishioka, Entanglement entropy: Holography and renormalization group, Rev. Mod. Phys. 90, 035007 (2018)

    2018

  45. [53]

    Kadoh, H

    D. Kadoh, H. Oba, and S. Takeda, Triad second renor- malization group, Journal of High Energy Physics 2022, 10.1007/jhep04(2022)121 (2022)

    2022 doi

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