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REVIEW 3 major objections 2 minor 74 references

Time-Incremented Multiscale Evolution (TIME): A Code-Independent Method for Time-Domain 3D Hydrodynamics and its Application to Roche Lobe Overflow

T0 review · 3 major / 2 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read By alternating 3D hydrodynamic bursts with fast evolutionary steps, TIME gives the first grid-based time-domain 3D model of Roche lobe overflow and finds M33 X-7 destabilizes once the donor overfills by about 1 percent.

desk verdict The submitted full text is an unrelated quantum-many-body paper, so the TIME method and the f~1.01 threshold are intriguing but entirely unverifiable as presented. read the letter →

arxiv 2508.14144 v2 pith:XM5GCG2M submitted 2025-08-19 astro-ph.HE astro-ph.SRphysics.comp-ph

David Dickson This is my paper · ORCID
classification astro-ph.HEastro-ph.SRphysics.comp-ph
keywords Rochelobeoverflowtime-domainhydrodynamicsmasstransferstabilityM33X-7high-massX-raybinarymultiscalesimulationaccretiondiskoverfillfactor
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

The paper puts forward a method named Time-Incremented Multiscale Evolution (TIME): instead of running one continuous 3D hydrodynamics simulation for the entire duration of a slow astrophysical process, it alternates brief high-resolution 3D bursts with cheap evolutionary steps, and claims this self-scales the time resolution at greatly reduced cost. Using it, the author reports the first grid-based, time-domain 3D model of Roche lobe overflow, applied to the high-mass X-ray binary M33 X-7. The central physical result is that mass transfer becomes unstable and fully conservative once the donor overfills its Roche lobe by more than about 1 percent (overfill factor $f \gtrsim 1.01$), with the unstable phase accelerating from thermal timescales to under 100 years for $f \geq 1.1$. If correct, this identifies a sharp overfill threshold that terminates stable overflow and opens a class of long-duration multidimensional flows to direct 3D modeling.

What carries the argument

The load-bearing object is the TIME algorithm: a piecewise coupling that runs 3D hydrodynamics for short high-resolution bursts and then hands the resulting state — the instantaneous L1 mass-transfer rate, the donor structure, and the accretion geometry — to a fast evolutionary solver that advances the system until the next burst. The alternating schedule self-scales its time resolution, taking large steps during slow nuclear-timescale phases and resolving fast thermal-timescale runaways in 3D. This hand-off is what allows a grid-based model to cover the full duration of Roche lobe overflow in the time domain.

What would settle it

Run a continuously resolved 3D hydrodynamic simulation of the same M33 X-7 configuration — or a convergence series in which the 3D bursts are made progressively shorter — and check whether stable overflow still terminates at $f \approx 1.01$ and whether the runaway lasts under 100 years at $f \approx 1.1$. If the threshold shifts with the switching cadence, the reported critical point is an artifact of the TIME schedule rather than a property of the flow. Repeating the run with a different grid-based 3D hydrodynamics code would further confirm whether the threshold is physical.

Watch

Extended reading notes

Core claim

On the paper's own terms, the discovery is that Roche lobe overflow in M33 X-7 has a well-defined stability threshold: at overfill factors $f$ below about 1.01 the flow is stable, non-conservative, and evolves on nuclear timescales, while above $f \approx 1.01$ it becomes unstable and fully conservative — meaning the transferred mass and angular momentum all reach the accretion disk. The runaway begins on thermal timescales and, for $f \geq 1.1$, spans under 100 years. The author identifies $f \approx 1.01$ as a critical point terminating stable overflow and suggests that in the general case this point corresponds to the L1 mass-transfer rate equalling the donor's wind mass-loss rate, approximately $\dot{M} \sim 10^{-6}\,M_\odot/\mathrm{yr}$. Underlying the result is the claim that TIME can provide a grid-based time-domain 3D model of this process for the first time.

Load-bearing premise

The method's results stand or fall on the assumption that the variables handed from each 3D burst to the fast evolutionary step — chiefly the instantaneous L1 mass-transfer rate, donor structure, and accretion geometry — carry enough information that the alternating schedule reproduces the flow's topology and stability the same way a continuously resolved 3D simulation would.

Editorial extensions

If this is right

  • M33 X-7 should show a stable, non-conservative mass-transfer phase until the donor overfills by about 1%, then a switch to unstable full conservation of mass and angular momentum onto the disk.
  • At overfill $f \geq 1.1$, the model predicts an accelerating runaway lasting under 100 years, so any observed long-lived overflowing state in such a binary would challenge the result.
  • The TIME schedule, if it works as claimed, can be applied to other long-duration multidimensional hydrodynamics problems where a continuous 3D run is infeasible.
  • The general criterion $\dot{M}_{L1} \sim \dot{M}_{\mathrm{wind}}$, if it holds, turns the geometric overfill threshold into a rate-based criterion usable across binaries with different wind strengths.

Reading between the lines

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

  • A natural test of the switching method is a convergence study in burst length: if the $f \approx 1.01$ threshold moves as the 3D bursts are shortened or lengthened, the threshold is an artifact of the time-incremented coupling rather than a physical property of the flow.
  • Because the coupling is described as code-independent in design, reproducing the M33 X-7 run with a different grid-based 3D code — or with a fully continuous short-window simulation — would separate numerical effects from the claimed hydrodynamical instability.
  • The proposed link between the critical overfill and wind mass-loss suggests a population-level prediction: among high-mass X-ray binaries, systems near the stable/unstable boundary should cluster where the donor's wind loss and Roche lobe transfer rates are comparable.
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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 / 2 minor

Summary. This submission's abstract (arXiv:2508.14144, astro-ph.HE) describes the 'Time-Incremented Multiscale Evolution' (TIME) method, a piecewise scheme alternating between high-resolution 3D hydrodynamic bursts (VH-1) and fast evolutionary modeling, and claims the first grid-based time-domain 3D model of Roche lobe overflow in M33 X-7. The abstract asserts a critical overfill factor f ~ 1.01 separating a conservative unstable phase lasting under 100 years from a non-conservative stable phase, and suggests this threshold corresponds to Mdot_L1 ~ Mdot_wind. However, the full text supplied under this identifier is a completely unrelated paper on neural quantum states (arXiv:2508.14152, 'Towards Interpretability of Neural Quantum States'). Therefore the submitted manuscript, as it stands, contains no methods section, no governing equations for TIME, no VH-1 setup, no M33 X-7 model, and no results or tests that support the abstract's claims.

Significance. If the actual TIME method and the M33 X-7 simulation reproduced the abstract's claims, the result would be significant for time-domain 3D hydrodynamics of mass transfer in high-mass X-ray binaries: a grid-based, piecewise 3D model with a claimed critical overfill factor f ~ 1.01 and a sub-100-year unstable phase would provide concrete, falsifiable timescales for binary evolution. The abstract's identification of a critical point connecting f, Mdot_L1, and Mdot_wind is a physically meaningful hypothesis. However, none of this can be assessed in the present document: there is no method description, no derivation, no convergence or error analysis, and no comparison with continuous 3D simulation. The paper as submitted ships no machine-checked proofs, reproducible code, or testable equations; the only content is the abstract.

major comments (3)
  1. [Full text] The full text of this submission is arXiv:2508.14152, 'Towards Interpretability of Neural Quantum States', a quantum-condensed-matter manuscript with no overlap in topic, authors, or content with the astro-ph.HE abstract. This is a load-bearing failure: the central claims about the TIME method, the VH-1 simulation of M33 X-7, the critical overfill factor f ~ 1.01, and the <100-year unstable phase rest entirely on the abstract. There are no equations defining the time-increment scheme, no description of the hydrodynamics setup, and no results to verify.
  2. [Abstract / Methods] The abstract's description of TIME as a 'piecewise approach which alternates between high-resolution 3D dynamic modeling and computationally fast evolutionary modeling' is not supported by any equation, algorithm, or convergence study in the submitted text. In particular, no definition is given of the variables exchanged between the 3D bursts and the evolutionary steps (e.g., Mdot_L1, donor structure, accretion geometry), nor of the 'self-scaling variable time resolution'. Without these definitions, the claimed 'greatly reduced computational cost' and the physical fidelity of the method cannot be evaluated.
  3. [Abstract / Results] The central quantitative claims, namely the critical overfill factor f ~ 1.01, the onset on thermal timescales, the duration under 100 years for f >= 1.1, and the correspondence to Mdot_L1 ~ Mdot_wind or Mdot_L1 ~ 10^-6 Msun/yr, are asserted without any error analysis, convergence tests over burst length, or comparison against a continuous, uninterrupted 3D run. Because the TIME method relies on switching between two simulation modes, the reported threshold could be an artifact of the switching schedule; the present submission provides no evidence against this possibility.
minor comments (2)
  1. [General] The manuscript lacks the standard apparatus of a journal submission: it has no author list, no references, no section numbering, and no figure or table captions, making it impossible to review as a complete submission.
  2. [Abstract] The quantity f is introduced in the abstract without an explicit definition; based on the context it appears to be an overfill factor, but this should be stated explicitly, and the units of all rates (e.g., Mdot_L1) should be defined.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity detectable; the full-text mismatch is a missing-support issue, not a circular derivation.

full rationale

The abstract of arXiv:2508.14144 claims a piecewise TIME method that alternates 3D hydrodynamics and fast evolutionary modeling, and it reports f~1.01 as an emergent critical overfill factor for M33 X-7. The supplied full text, however, is actually arXiv:2508.14152, an unrelated neural-quantum-states manuscript; it contains no equations defining the TIME time increment, the self-scaling variable time resolution, the VH-1 setup, or the mass-transfer calculation. That is a missing-support condition: the TIME method and the f~1.01 threshold cannot be verified from the submission, but absence of evidence is not circularity. Based on the abstract alone, f is an input overfill factor whose critical value (~1.01) is a simulation output, not a fitted parameter renamed as a prediction. The statement that f~1.01 'may correspond to Mdot_L1 ~ Mdot_wind or Mdot_L1 ~ 10^-6 Msun/yr' is explicitly framed as a possible correspondence ('may correspond'), not as an equation that reduces the result to its own inputs. No self-citation chain is visible in the abstract, and no quoted equation shows a definitional equivalence between an input and an output. Under the hard rule that circularity requires a quoted reduction, the appropriate finding is no significant circularity (0). The substantive concern about this submission is completeness—the body does not support the abstract—which belongs to correctness/support risk, not to circularity.

Assumptions & free parameters 0 free parameters · 2 assumptions · 0 invented entities

From the abstract alone, only the two domain assumptions above can be identified. No free parameters are visible: f is an input physical overfill factor, and the critical value f ~ 1.01 is presented as an output. No new physical entities are postulated. The mismatched full text prevents a deeper audit.

assumptions (2)
  • domain assumption The state transferred between the 3D hydrodynamic and fast evolutionary modes is sufficient to reproduce continuous time-domain dynamics.
    The TIME method's validity depends on this closure at each hand-off; the abstract only states that the approach alternates the two modes, not why the coupling is exact.
  • domain assumption VH-1's 3D hydrodynamics captures the RLOF mass-transfer flow at sufficient spatial resolution for the stated stability conclusion.
    The abstract names VH-1 but gives no resolution study; without it the threshold could be resolution-dependent.

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Cite this review

Pith. "Pith review of Time-Incremented Multiscale Evolution (TIME): A Code-Independent Method for Time-Domain 3D Hydrodynamics and its Application to Roche Lobe Overflow." pith.science (2026). https://pith.science/paper/XM5GCG2M

@misc{pith2026250814144,
  author       = {Pith},
  title        = {Pith review of: Time-Incremented Multiscale Evolution (TIME): A Code-Independent Method for Time-Domain 3D Hydrodynamics and its Application to Roche Lobe Overflow},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/XM5GCG2M}},
  note         = {Machine review of arXiv:2508.14144}
}
read the original abstract

Context. Many critical physical processes, such as Roche lobe overflow, strain modern simulation methods due to their durations and multidimensionality. Aims. We employ a novel method of time-domain multidimensional simulations to provide the first grid-based time domain 3D model of Roche lobe overflow using VH-1. Methods. Using a piecewise approach which alternates between high-resolution 3D dynamic modeling and computationally fast evolutionary modeling, we present and test a method capable of self-scaling variable time resolution at greatly reduced computational cost. Results. We find mass transfer in the test high mass x-ray binary M33 X-7 to be unstable and fully conservative in both mass and angular momentum transport onto the accretion disk beyond f >~ 1.01. This phase begins on thermal timescales and accelerates to span < 100 yrs beyond f >= 1.1, while the non-conservative stable phase of f <~ 1.01 occurs on roughly nuclear timescales. Conclusions. We identify a critical point f ~ 1.01 which terminates stable overflow, which may correspond to the point Mdot_L1 ~ Mdot_wind or Mdot_L1 ~ 10^-6 M_solar/yr in the general case.

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Works this paper leans on

74 extracted references · 51 canonical work pages

  1. [1]

    Probability and spin-moments The probability distribution of a quantum many body state is given by the square of the wave function�(�) = ��(�)�2 and therefore describe similar things. In partic- ular, in some autoregressive neural quantum states, the network output directly parametrizes�(�), which raises the question how both are related from a boolean fu...

    work page

  2. [2]

    basis spins

    = � ���∈{0�1} � 1�4(�� 1 2)(� � 1 � 1 2) 2 1�4(�� 1 2)(� � 2 � 1 2) 2 � (B1) which reduces to: ���� (� � 1�2) = � � � 1�2 for���= 1� 1�� � 1�2 for���= 0� (B2) This produces a polynomial similar to Eq. (12) (consid- ering that� � 1 = 1�� � 2). Hence, one hot encoding does not offer an immediate advantage (or disadvantage) com- pared to typical spin encodin...

    work page

  3. [3]

    This trick is often applied to gain numerical stability, as�(�) can vary over many orders of magnitude, which is related to requiring large correlation orders

    Logarithmic parametrization of the neural quantum states In practice, neural quantum states are often defined by using log�(�) =�(�) instead of�(�) =�(�), where �(�) is the neural network implementation. This trick is often applied to gain numerical stability, as�(�) can vary over many orders of magnitude, which is related to requiring large correlation o...

    work page

  4. [4]

    Quantum monte carlo simulations of solids,

    W. M. C. Foulkes, L. Mitas, R. J. Needs, and G. Ra- jagopal, “Quantum monte carlo simulations of solids,” Rev. Mod. Phys.73, 33–83 (2001)

    work page 2001

  5. [5]

    Federico Becca and Sandro Sorella,Quantum Monte Carlo Approaches for Correlated Systems(Cambridge University Press, 2017)

    2017

  6. [6]

    The sign problem in quantum monte carlo simulations,

    Gaopei Pan and Zi Yang Meng, “The sign problem in quantum monte carlo simulations,” inEncyclopedia of Condensed Matter Physics(Elsevier, 2024) p. 879–893

    work page 2024

  7. [7]

    The density-matrix renormalization group in the age of matrix product states,

    Ulrich Schollw¨ ock, “The density-matrix renormalization group in the age of matrix product states,” Annals of Physics326, 96–192 (2011)

    work page 2011

  8. [8]

    Solving the quantum many-body problem with artificial neural net- works,

    Giuseppe Carleo and Matthias Troyer, “Solving the quantum many-body problem with artificial neural net- works,” Science355, 602–606 (2017)

    2017

Show all 74 references
  1. [9]

    Empowering deep neural quantum states through efficient optimization,

    Ao Chen and Markus Heyl, “Empowering deep neural quantum states through efficient optimization,” Nature Physics20, 1476–1481 (2024)

    2024

  2. [10]

    Neural Network- Augmented Pfaffian Wave-functions for Scalable Simula- tions of Interacting Fermions,

    Ao Chen, Zhou-Quan Wan, Anirvan Sengupta, An- toine Georges, and Christopher Roth, “Neural Network- Augmented Pfaffian Wave-functions for Scalable Simula- tions of Interacting Fermions,” (2025), arXiv:2507.10705 [cond-mat.str-el]

    2025 arXiv

  3. [11]

    Transformer variational wave functions for frus- trated quantum spin systems,

    Luciano Loris Viteritti, Riccardo Rende, and Federico Becca, “Transformer variational wave functions for frus- trated quantum spin systems,” Phys. Rev. Lett.130, 236401 (2023)

    2023

  4. [12]

    From architectures to applica- tions: a review of neural quantum states,

    Hannah Lange, Anka Van de Walle, Atiye Abedinnia, and Annabelle Bohrdt, “From architectures to applica- tions: a review of neural quantum states,” Quantum Sci- ence and Technology9, 040501 (2024)

    2024

  5. [13]

    Simulating dynam- ics of correlated matter with neural quantum states,

    Markus Schmitt and Markus Heyl, “Simulating dynam- ics of correlated matter with neural quantum states,” (2025), arXiv:2506.03124 [quant-ph]

    2025 arXiv

  6. [14]

    Learning ground states of gapped quantum Hamiltonians with Kernel Methods,

    Clemens Giuliani, Filippo Vicentini, Riccardo Rossi, and Giuseppe Carleo, “Learning ground states of gapped quantum Hamiltonians with Kernel Methods,” Quantum 7, 1096 (2023)

    2023

  7. [15]

    Neural network approach to quasi- particle dispersions in doped antiferromagnets,

    Hannah Lange, Fabian D¨ oschl, Juan Carrasquilla, and Annabelle Bohrdt, “Neural network approach to quasi- particle dispersions in doped antiferromagnets,” Commu- nications Physics7, 187 (2024)

    2024

  8. [16]

    Neural network quan- tum states for the interacting hofstadter model with higher local occupations and long-range interactions,

    Fabian D¨ oschl, Felix A. Palm, Hannah Lange, Fabian Grusdt, and Annabelle Bohrdt, “Neural network quan- tum states for the interacting hofstadter model with higher local occupations and long-range interactions,” Phys. Rev. B111, 045408 (2025)

    2025

  9. [17]

    Comment on “can neural quantum states learn volume-law ground states?

    Zakari Denis, Alessandro Sinibaldi, and Giuseppe Car- leo, “Comment on “can neural quantum states learn volume-law ground states?”,” Phys. Rev. Lett.134, 079701 (2025)

    2025

  10. [18]

    Ap- proximately symmetric neural networks for quantum spin liquids,

    Dominik S. Kufel, Jack Kemp, DinhDuy Vu, Simon M. Linsel, Chris R. Laumann, and Norman Y. Yao, “Ap- proximately symmetric neural networks for quantum spin liquids,” Phys. Rev. Lett.135, 056702 (2025)

    2025

  11. [19]

    Efficient representation of quantum many-body states with deep neural networks,

    Xun Gao and Lu-Ming Duan, “Efficient representation of quantum many-body states with deep neural networks,” Nature Communications8, 662 (2017)

    2017

  12. [20]

    Neural tensor contractions and the expressive power of deep neural quantum states,

    Or Sharir, Amnon Shashua, and Giuseppe Carleo, “Neural tensor contractions and the expressive power of deep neural quantum states,” Phys. Rev. B106, 205136 (2022)

    2022

  13. [21]

    Quantum entanglement in neural network states,

    Dong-Ling Deng, Xiaopeng Li, and S. Das Sarma, “Quantum entanglement in neural network states,” Phys. Rev. X7, 021021 (2017)

    2017

  14. [22]

    Neural-network quan- tum states, string-bond states, and chiral topological states,

    Ivan Glasser, Nicola Pancotti, Moritz August, Ivan D. Rodriguez, and J. Ignacio Cirac, “Neural-network quan- tum states, string-bond states, and chiral topological states,” Phys. Rev. X8, 011006 (2018)

    2018

  15. [23]

    Interpretable scaling behavior in sparse subnetwork representations of quantum states,

    Brandon Barton, Juan Carrasquilla, Christopher Roth, and Agnes Valenti, “Interpretable scaling behavior in sparse subnetwork representations of quantum states,” (2025), arXiv:2505.22734 [quant-ph]

    2025

  16. [24]

    Constructing exact representations of quantum many-body systems with deep neural networks,

    Giuseppe Carleo, Yusuke Nomura, and Masatoshi Imada, “Constructing exact representations of quantum many-body systems with deep neural networks,” Nature Communications9(2018)

    2018

  17. [25]

    Neural network ground state from the neural tangent kernel perspective: The sign bias,

    Harel Kol-Namer and Moshe Goldstein, “Neural network ground state from the neural tangent kernel perspective: The sign bias,” (2024), arXiv:2411.03980 [quant-ph]

    2024 arXiv

  18. [26]

    A simple linear algebra identity to optimize large-scale neural net- work quantum states,

    Riccardo Rende, Luciano Loris Viteritti, Lorenzo Bar- done, Federico Becca, and Sebastian Goldt, “A simple linear algebra identity to optimize large-scale neural net- work quantum states,” Communications Physics7, 260 (2024)

    2024

  19. [27]

    Efficiency of neural quantum states in light of the quantum geometric ten- sor,

    Sidhartha Dash, Luca Gravina, Filippo Vicentini, Michel Ferrero, and Antoine Georges, “Efficiency of neural quantum states in light of the quantum geometric ten- sor,” Communications Physics8(2025)

    2025

  20. [28]

    Double descent: When do neural quantum states generalize?

    M. Schuyler Moss, Alev Orfi, Christopher Roth, Anir- van M. Sengupta, Antoine Georges, Dries Sels, Anna Dawid, and Agnes Valenti, “Double descent: When do neural quantum states generalize?” (2025), arXiv:2508.00068 [cond-mat.dis-nn]

    2025

  21. [29]

    Can neural quantum states learn volume-law ground states?

    Giacomo Passetti, Damian Hofmann, Pit Neitemeier, Lukas Grunwald, Michael A. Sentef, and Dante M. Kennes, “Can neural quantum states learn volume-law ground states?” Phys. Rev. Lett.131, 036502 (2023)

    2023

  22. [30]

    Approximation by superpositions of a sig- moidal function,

    G. Cybenko, “Approximation by superpositions of a sig- moidal function,” Mathematics of Control, Signals and Systems2, 303–314 (1989)

    1989

  23. [31]

    Multilayer feedforward networks are universal approxi- mators,

    Kurt Hornik, Maxwell Stinchcombe, and Halbert White, “Multilayer feedforward networks are universal approxi- mators,” Neural Networks2, 359–366 (1989)

    1989

  24. [32]

    Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networks,

    Kurt Hornik, Maxwell Stinchcombe, and Halbert White, “Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networks,” Neural Networks3, 551–560 (1990)

    1990

  25. [33]

    Investigating topological order using recurrent neural networks,

    Mohamed Hibat-Allah, Roger G. Melko, and Juan Car- rasquilla, “Investigating topological order using recurrent neural networks,” Phys. Rev. B108, 075152 (2023)

    2023

  26. [34]

    Variational neural annealing,

    Mohamed Hibat-Allah, Estelle M. Inack, Roeland Wiersema, Roger G. Melko, and Juan Carrasquilla, “Variational neural annealing,” Nature Machine Intelli- gence3, 952–961 (2021)

    2021

  27. [35]

    Accurate compu- tation of quantum excited states with neural networks,

    David Pfau, Simon Axelrod, Halvard Sutterud, Ingrid von Glehn, and James S. Spencer, “Accurate compu- tation of quantum excited states with neural networks,” Science385(2024)

    2024

  28. [36]

    Solving the 20 hubbard model with neural quantum states,

    Yuntian Gu, Wenrui Li, Heng Lin, Bo Zhan, Ruichen Li, Yifei Huang, Di He, Yantao Wu, Tao Xiang, Mingpu Qin, Liwei Wang, and Dingshun Lv, “Solving the 20 hubbard model with neural quantum states,” (2025), arXiv:2507.02644 [cond-mat.str-el]

    2025 arXiv

  29. [37]

    Convolutional transformer wave functions,

    Ao Chen, Vighnesh Dattatraya Naik, and Markus Heyl, “Convolutional transformer wave functions,” (2025), arXiv:2503.10462 [cond-mat.dis-nn]

    2025 arXiv

  30. [38]

    Accurate neural quantum states for interacting lattice bosons,

    Zakari Denis and Giuseppe Carleo, “Accurate neural quantum states for interacting lattice bosons,” Quantum 9, 1772 (2025)

    2025

  31. [39]

    Machine learning topological states,

    Dong-Ling Deng, Xiaopeng Li, and S. Das Sarma, “Machine learning topological states,” Phys. Rev. B96, 195145 (2017)

    2017

  32. [40]

    Chiral topological phases from artificial neural networks,

    Raphael Kaubruegger, Lorenzo Pastori, and Jan Carl Budich, “Chiral topological phases from artificial neural networks,” Phys. Rev. B97, 195136 (2018)

    2018

  33. [41]

    Efficient repre- sentation of topologically ordered states with restricted boltzmann machines,

    Sirui Lu, Xun Gao, and L.-M. Duan, “Efficient repre- sentation of topologically ordered states with restricted boltzmann machines,” Phys. Rev. B99, 155136 (2019)

    2019

  34. [42]

    When can classical neu- ral networks represent quantum states?

    Tai-Hsuan Yang, Mehdi Soleimanifar, Thiago Bergam- aschi, and John Preskill, “When can classical neu- ral networks represent quantum states?” (2024), arXiv:2410.23152 [quant-ph]

    2024 arXiv

  35. [43]

    Correlation-enhanced neural net- works as interpretable variational quantum states,

    Agnes Valenti, Eliska Greplova, Netanel H. Lindner, and Sebastian D. Huber, “Correlation-enhanced neural net- works as interpretable variational quantum states,” Phys. Rev. Res.4, L012010 (2022)

    2022

  36. [44]

    Corre- lator convolutional neural networks as an interpretable architecture for image-like quantum matter data,

    Cole Miles, Annabelle Bohrdt, Ruihan Wu, Christie Chiu, Muqing Xu, Geoffrey Ji, Markus Greiner, Kilian Q. Weinberger, Eugene Demler, and Eun-Ah Kim, “Corre- lator convolutional neural networks as an interpretable architecture for image-like quantum matter data,” Na- ture Commu...

    2021 doi

  37. [45]

    Decomposing neural networks as mappings of correlation functions,

    Kirsten Fischer, Alexandre Ren´ e, Christian Keup, Moritz Layer, David Dahmen, and Moritz Helias, “Decomposing neural networks as mappings of correlation functions,” Phys. Rev. Res.4, 043143 (2022)

    2022

  38. [46]

    Interpretable correlator trans- former for image-like quantum matter data,

    Abhinav Suresh, Henning Schl¨ omer, Baran Hashemi, and Annabelle Bohrdt, “Interpretable correlator trans- former for image-like quantum matter data,” Machine Learning: Science and Technology6, 025006 (2025)

    2025

  39. [47]

    Speak so a physicist can understand you! tetriscnn for detecting phase transitions and order pa- rameters,

    Kacper Cybi´ nski, James Enouen, Antoine Georges, and Anna Dawid, “Speak so a physicist can understand you! tetriscnn for detecting phase transitions and order pa- rameters,” (2024), arXiv:2411.02237 [quant-ph]

    2024 arXiv

  40. [48]

    Gauge equivariant neural networks for quan- tum lattice gauge theories,

    Di Luo, Giuseppe Carleo, Bryan K. Clark, and James Stokes, “Gauge equivariant neural networks for quan- tum lattice gauge theories,” Physical Review Letters127 (2021)

    2021

  41. [49]

    Preparing topological states with finite depth simultaneous com- muting gates,

    Yarden Sheffer, Erez Berg, and Ady Stern, “Preparing topological states with finite depth simultaneous com- muting gates,” Phys. Rev. B111, 115160 (2025)

    2025

  42. [50]

    Ryan O’Donnell,Analysis of Boolean Functions(Cam- bridge University Press, 2014)

    2014

  43. [51]

    Recur- rent neural network wave functions,

    Mohamed Hibat-Allah, Martin Ganahl, Lauren E. Hay- ward, Roger G. Melko, and Juan Carrasquilla, “Recur- rent neural network wave functions,” Phys. Rev. Res.2, 023358 (2020)

    2020

  44. [52]

    Supplementing recurrent neural network wave functions with symmetry and annealing to improve accu- racy,

    Mohamed Hibat-Allah, Roger G. Melko, and Juan Car- rasquilla, “Supplementing recurrent neural network wave functions with symmetry and annealing to improve accu- racy,” (2024), arXiv:2207.14314 [cond-mat.dis-nn]

    2024 arXiv

  45. [53]

    Learning the ground state of a non-stoquastic quantum Hamiltonian in a rugged neural network landscape,

    Marin Bukov, Markus Schmitt, and Maxime Dupont, “Learning the ground state of a non-stoquastic quantum Hamiltonian in a rugged neural network landscape,” Sci- Post Phys.10, 147 (2021)

    2021

  46. [54]

    Learn- ing complexity of many-body quantum sign structures through the lens of boolean fourier analysis,

    Ilya Schurov, Anna Kravchenko, Mikhail I. Katsnel- son, Andrey A. Bagrov, and Tom Westerhout, “Learn- ing complexity of many-body quantum sign structures through the lens of boolean fourier analysis,” (2025), arXiv:2508.09870 [cond-mat.dis-nn]

    2025 arXiv

  47. [55]

    En- hancing neural-network variational monte carlo through basis transformation,

    Zhixuan Liu, Dongheng Qian, and Jing Wang, “En- hancing neural-network variational monte carlo through basis transformation,” (2026), arXiv:2604.15888 [cond- mat.str-el]

    2026 arXiv

  48. [56]

    Enhancing the expressivity of variational neural, and hardware-efficient quantum states through orbital rotations,

    Javier Robledo Moreno, Jeffrey Cohn, Dries Sels, and Mario Motta, “Enhancing the expressivity of variational neural, and hardware-efficient quantum states through orbital rotations,” (2023), arXiv:2302.11588 [quant-ph]

    2023 arXiv

  49. [57]

    Physics-informed transformers for electronic quantum states,

    Jo˜ ao Augusto Sobral, Michael Perle, and Mathias S. Scheurer, “Physics-informed transformers for electronic quantum states,” Nature Communications16, 10811 (2025)

    2025

  50. [58]

    Kolmogorov complexity,

    “Kolmogorov complexity,” inElements of Information Theory(John Wiley & Sons, Ltd, 2005) Chap. 14, pp. 463–508

    2005

  51. [59]

    Three tutorial lectures on entropy and counting,

    David Galvin, “Three tutorial lectures on entropy and counting,” (2014), arXiv:1406.7872 [math.CO]

    2014 arXiv

  52. [60]

    Nnqs-transformer: an efficient and scal- able neural network quantum states approach for ab ini- tio quantum chemistry,

    Yangjun Wu, Chu Guo, Yi Fan, Pengyu Zhou, and Honghui Shang, “Nnqs-transformer: an efficient and scal- able neural network quantum states approach for ab ini- tio quantum chemistry,” inProceedings of the Interna- tional Conference for High Performance Computing, Net- working, ...

    2023

  53. [61]

    Transformer wave function for two dimensional frustrated magnets: Emer- gence of a spin-liquid phase in the shastry-sutherland model,

    Luciano Loris Viteritti, Riccardo Rende, Alberto Parola, Sebastian Goldt, and Federico Becca, “Transformer wave function for two dimensional frustrated magnets: Emer- gence of a spin-liquid phase in the shastry-sutherland model,” Physical Review B111(2025), 10.1103/phys- revb....

    2025 doi

  54. [62]

    Transformer neural networks and quantum simulators: a hybrid approach for simulating strongly correlated systems,

    Hannah Lange, Guillaume Bornet, Gabriel Emperauger, Cheng Chen, Thierry Lahaye, Stefan Kienle, Antoine Browaeys, and Annabelle Bohrdt, “Transformer neural networks and quantum simulators: a hybrid approach for simulating strongly correlated systems,” Quantum9, 1675 (2025)

    2025

  55. [63]

    Fermionic wave func- tions from neural-network constrained hidden states,

    Javier Robledo Moreno, Giuseppe Carleo, Antoine Georges, and James Stokes, “Fermionic wave func- tions from neural-network constrained hidden states,” Proceedings of the National Academy of Sciences119, e2122059119 (2022)

    2022

  56. [64]

    Efficiency of the hidden fermion determinant states ansatz in the light of different complexity measures,

    Bj¨ orn J. Wurst, Dante M. Kennes, and Jonas B. Profe, “Efficiency of the hidden fermion determinant states ansatz in the light of different complexity measures,” Phys. Rev. Res.7, 023316 (2025)

    2025

  57. [65]

    Simulating the two-dimensional�� �model at finite doping with neural quantum states,

    Hannah Lange, Annika B¨ ohler, Christopher Roth, and Annabelle Bohrdt, “Simulating the two-dimensional�� �model at finite doping with neural quantum states,” (2024), arXiv:2411.10430 [cond-mat.str-el]

    2024 arXiv

  58. [66]

    Backflow transformations via neural networks for quantum many-body wave func- tions,

    Di Luo and Bryan K. Clark, “Backflow transformations via neural networks for quantum many-body wave func- tions,” Physical Review Letters122(2019)

    2019

  59. [67]

    Fine-tuning neural network quantum states,

    Riccardo Rende, Sebastian Goldt, Federico Becca, and Luciano Loris Viteritti, “Fine-tuning neural network quantum states,” Phys. Rev. Res.6, 043280 (2024)

    2024

  60. [68]

    Fermionic neural-network states for ab-initio electronic structure,

    Kenny Choo, Antonio Mezzacapo, and Giuseppe Carleo, “Fermionic neural-network states for ab-initio electronic structure,” Nature Communications11, 2368 (2020). 21

    2020

  61. [69]

    Netket: A machine learning toolkit for many-body quantum systems,

    Giuseppe Carleo, Kenny Choo, Damian Hofmann, James E. T. Smith, Tom Westerhout, Fabien Alet, Emily J. Davis, Stavros Efthymiou, Ivan Glasser, Sheng-Hsuan Lin, Marta Mauri, Guglielmo Mazzola, Christian B. Mendl, Evert van Nieuwenburg, Ossian O’Reilly, Hugo Th´ eveniaut, Giacomo...

    2019

  62. [70]

    Vii. problems concerning interpola- tions,

    Edward Waring, “Vii. problems concerning interpola- tions,” Philosophical Transactions of the Royal Society of London69, 59–67 (1779)

  63. [71]

    Chapter 14 - interpolation,

    Alexandre M. Bayen and Timmy Siauw, “Chapter 14 - interpolation,” inAn Introduction to MATLAB�Pro- gramming and Numerical Methods for Engineers, edited by Alexandre M. Bayen and Timmy Siauw (Academic Press, Boston, 2015) pp. 211–223

    2015

  64. [72]

    Multiscale interpolative construction of quantized tensor trains,

    Michael Lindsey, “Multiscale interpolative construction of quantized tensor trains,” (2024), arXiv:2311.12554 [math.NA]

    2024 arXiv

  65. [73]

    Minimal in- stances for toric code ground states,

    Nicolai Lang and Hans Peter B¨ uchler, “Minimal in- stances for toric code ground states,” Phys. Rev. A86, 022336 (2012)

    2012

  66. [74]

    A graph- based approach to entanglement entropy of quantum er- ror correcting codes,

    Wuxu Zhao, Menglong Fang, and Daiqin Su, “A graph- based approach to entanglement entropy of quantum er- ror correcting codes,” (2025), arXiv:2501.06407 [quant- ph]

    2025 arXiv

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.