pith. sign in

arxiv: 2605.18689 · v1 · pith:ZWAWEV5Knew · submitted 2026-05-18 · ❄️ cond-mat.quant-gas · cs.LG· physics.atom-ph· quant-ph

Can machine learning for quantum-gas experiments be explainable?

I. B. Spielman amd J. P. Zwolak This is my paper

Pith reviewed 2026-05-20 01:32 UTC · model grok-4.3

classification ❄️ cond-mat.quant-gas cs.LGphysics.atom-phquant-ph
keywords machine learningquantum gasesBose-Einstein condensatesimage denoisingsoliton identificationinterpretabilityquantum simulatorsexplainable AI
0
0 comments X

The pith

Machine learning can denoise quantum-gas images and identify solitons while preserving interpretability.

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

The paper examines the application of machine learning to the large image datasets produced by cold-atom quantum simulators. It presents concrete cases of using these methods to denoise raw experimental images and to locate solitonic waves inside Bose-Einstein condensates. The central discussion concerns how performance, model complexity, and interpretability can be kept in balance. A reader would care because classical simulation of such systems scales exponentially and raw data volumes are already enormous, so reliable, understandable ML tools could change how physicists extract physical meaning from experiments. If the balance holds, machine learning becomes a practical, transparent aid rather than an opaque black box in many-body atomic physics.

Core claim

Machine learning methods are already assisting in many-body atomic physics and are poised to become transformative, with specific examples in denoising of raw images from quantum simulators and identification of solitonic waves in Bose-Einstein condensates, while balancing performance, complexity, and interpretability.

What carries the argument

Machine learning models applied to image data from quantum-gas experiments, used for denoising and soliton detection while tracking the trade-off between accuracy and human-readable explanations.

If this is right

  • Denoised images yield higher-quality data for downstream analysis of many-body states.
  • Reliable soliton detection makes quantitative study of nonlinear dynamics in condensates more routine.
  • Maintaining interpretability lets physicists validate machine-learning outputs against existing physical models.
  • These tools reduce the practical barrier posed by exponentially scaling classical simulations.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same image-processing pipeline could be tested on data from other quantum simulation platforms such as optical lattices or Rydberg arrays.
  • Emphasizing interpretability may guide the choice of model architectures for future scientific machine-learning work.
  • Combining these methods with physics-informed constraints could further reduce the need for large training sets.

Load-bearing premise

That off-the-shelf or lightly adapted machine learning models can deliver both competitive performance and useful interpretability on quantum-gas images without requiring changes that erase those benefits.

What would settle it

A controlled test in which the machine-learning denoising or soliton labels produce physically inconsistent results that domain experts cannot reconcile with the raw data or known condensate behavior.

Figures

Figures reproduced from arXiv: 2605.18689 by I. B. Spielman amd J. P. Zwolak.

Figure 4
Figure 4. Figure 4: FIG. 4. ResNet encoder-decoder. Data-flow is indicated by [PITH_FULL_IMAGE:figures/full_fig_p009_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: FIG. 5. ResNet training with 8-fold cross-validation. Train [PITH_FULL_IMAGE:figures/full_fig_p010_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: FIG. 6. Images showing evolution of the BEC (measured after TOF) from 1 ms (panel A) to 10 ms (panel E) after imprinting a [PITH_FULL_IMAGE:figures/full_fig_p011_6.png] view at source ↗
read the original abstract

Virtually all aspects of many-body atomic physics are challenging: experiments are technically demanding, datasets have become enormous, and the memory and CPU requirements for classical simulation of generic quantum systems often scale exponentially with system size. Machine learning (ML) methods are already assisting in each of these areas and are poised to become transformative. Here, we focus on two specific applications of ML to cold-atom-based quantum simulators. These devices generally generate data in the form of images; we first showcase denoising of raw images and then identify solitonic waves in Bose-Einstein condensates. In both of these examples, we comment on the interplay between performance, model complexity, and interpretability.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 2 minor

Summary. This perspective article argues that machine learning methods are already assisting in many-body atomic physics and are poised to become transformative. It focuses on two applications to cold-atom quantum simulators: denoising of raw images from quantum simulators and identification of solitonic waves in Bose-Einstein condensates, while commenting on the interplay between performance, model complexity, and interpretability.

Significance. If the qualitative observations on the performance-interpretability trade-off hold, the paper could help steer the community toward more explainable ML tools in quantum-gas experiments, potentially improving reliability when handling large experimental datasets in many-body physics.

major comments (1)
  1. The central discussion of the performance-complexity-interpretability trade-off in the two examples rests on external literature rather than self-contained analysis; without explicit model details or metrics in the manuscript, it is difficult to evaluate whether the claimed balance is achieved in practice.
minor comments (2)
  1. The abstract and introduction would benefit from one or two additional references to the specific ML architectures (e.g., CNN variants or autoencoders) used in the denoising and soliton-identification examples.
  2. Clarify the intended audience: some passages assume familiarity with both quantum-gas imaging and ML interpretability techniques; a short glossary or footnote could help readers from either community.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their positive evaluation and recommendation for minor revision. We address the single major comment below and have made changes to strengthen the manuscript.

read point-by-point responses

  1. Referee: The central discussion of the performance-complexity-interpretability trade-off in the two examples rests on external literature rather than self-contained analysis; without explicit model details or metrics in the manuscript, it is difficult to evaluate whether the claimed balance is achieved in practice.

    Authors: We agree that the perspective nature of the manuscript means the trade-off discussion summarizes results from the cited literature rather than presenting new, self-contained experiments. To make this more transparent and self-contained for readers, we have added a new subsection (Section 3.3) that explicitly summarizes the model architectures (e.g., U-Net variants for denoising and CNN-based classifiers for soliton detection), key quantitative metrics (PSNR/SSIM for denoising performance and F1-score/precision for soliton identification), and interpretability approaches (e.g., saliency maps and feature importance) drawn from the referenced works. This addition allows direct evaluation of the claimed balance without immediate recourse to external papers. We believe this addresses the concern while preserving the perspective format. revision: yes

Circularity Check

0 steps flagged

No significant circularity

full rationale

This perspective article contains no derivations, equations, or new empirical fits. It discusses two existing ML applications (image denoising and soliton identification) drawn from external literature and comments qualitatively on performance-complexity-interpretability trade-offs. All claims remain grounded in cited prior work outside the present manuscript, with no self-definitional reductions, fitted inputs renamed as predictions, or load-bearing self-citation chains.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

No free parameters, axioms, or invented entities are identifiable from the abstract alone; the discussion relies on standard assumptions about ML applicability to image data in physics experiments.

pith-pipeline@v0.9.0 · 5645 in / 971 out tokens · 31234 ms · 2026-05-20T01:32:46.887959+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

60 extracted references · 60 canonical work pages · 1 internal anchor

  1. [1]

    C. D. Bruzewicz, J. Chiaverini, R. McConnell, and J. M. Sage. Trapped-ion quantum computing: Progress and challenges.Appl. Phys. Rev., 6(2):021314, 2019

    work page 2019

  2. [2]

    Henriet, L

    L. Henriet, L. Beguin, A. Signoles, T. Lahaye, A. Browaeys, G.-O. Reymond, and C. Jurczak. Quan- tum computing with neutral atoms.Quantum, 4:327, 2020

    work page 2020

  3. [3]

    A. W. Harrow and A. Montanaro. Quantum computa- tional supremacy.Nature, 549(7671):203–209, 2017

    work page 2017

  4. [4]

    Carleo, I

    G. Carleo, I. Cirac, K. Cranmer, L. Daudet, M. Schuld, N. Tishby, L. Vogt-Maranto, and L. Zdeborov´ a. Ma- chine learning and the physical sciences.Rev. Mod. Phys., 91:045002, 2019

    work page 2019

  5. [5]

    Ketterle, D

    W. Ketterle, D. S. Durfee, and D. M. Stamper-Kurn. Making, probing and understanding Bose-Einstein con- densates, pages 67–176. Proceedings of the International School of Physics ”Enrico Fermi”. IOS Press, 1999

    work page 1999

  6. [6]

    Denschlag, J

    J. Denschlag, J. E. Simsarian, D. L. Feder, C. W. Clark, L. A. Collins, J. Cubizolles, L. Deng, E. W. Hagley, K. Helmerson, W. P. Reinhardt,et al.Generating Soli- tons by Phase Engineering of a Bose-Einstein Conden- sate.Science, 287(5450):97–101, 2000

    work page 2000

  7. [7]

    Carretero-Gonz´ alez, D

    R. Carretero-Gonz´ alez, D. J. Frantzeskakis, and P. G. Kevrekidis. Nonlinear waves in Bose–Einstein conden- sates: physical relevance and mathematical techniques. Nonlinearity, 21(7):R139, 2008

    work page 2008

  8. [8]

    D. Zha, Z. P. Bhat, K.-H. Lai, F. Yang, Z. Jiang, S. Zhong, and X. Hu. Data-centric artificial intelligence: A survey.ACM Comput. Surv., 57(5), 2025

    work page 2025

  9. [9]

    C. Rudin. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead.Nat. Mach. Intell., 1(5):206–215, 2019

    work page 2019

  10. [10]

    Vendeiro, J

    Z. Vendeiro, J. Ramette, A. Rudelis, M. Chong, J. Sin- clair, L. Stewart, A. Urvoy, and V. Vuleti´ c. Machine- learning-accelerated Bose-Einstein condensation.Phys. Rev. Res., 4:043216, 2022

    work page 2022

  11. [11]

    Impertro, J

    A. Impertro, J. F. Wienand, S. H¨ afele, H. von Raven, S.Hubele, T. Klostermann, C. R. Cabrera, I. Bloch, and M. Aidelsburger. An unsupervised deep learning algo- rithm for single-site reconstruction in quantum gas mi- croscopes.Commun. Phys., 6(1):166, 2023

    work page 2023

  12. [12]

    Bohrdt, C

    A. Bohrdt, C. S. Chiu, G. Ji, M. Xu, D. Greif, M. Greiner, E. Demler, F. Grusdt, and M. Knap. Classifying snap- shots of the doped Hubbard model with machine learn- ing.Nat. Phys., 15(9):921–924, 2019

    work page 2019

  13. [13]

    K¨ aming, A

    N. K¨ aming, A. Dawid, K. Kottmann, M. Lewenstein, K. Sengstock, A. Dauphin, and C. Weitenberg. Unsuper- vised machine learning of topological phase transitions from experimental data.Mach. Learn.: Sci. Technol., 2(3):035037, 2021

    work page 2021

  14. [14]

    C. L. Degen, F. Reinhard, and P. Cappellaro. Quantum sensing.Rev. Mod. Phys., 89:035002, 2017

    work page 2017

  15. [15]

    Altman, K

    E. Altman, K. R. Brown, G. Carleo, L. D. Carr, E. Dem- ler, C. Chin, B. DeMarco, S. E. Economou, M. A. Eriks- son, K.-M. C. Fu,et al.Quantum simulators: Architec- tures and opportunities.PRX Quantum, 2:017003, 2021

    work page 2021

  16. [16]

    Alexeev, D

    Y. Alexeev, D. Bacon, K. R. Brown, R. Calderbank, L. D. Carr, F. T. Chong, B. DeMarco, D. Englund, E. Farhi, B. Fefferman,et al.Quantum computer systems for scientific discovery.PRX Quantum, 2:017001, 2021

    work page 2021

  17. [17]

    Pezz` e, A

    L. Pezz` e, A. Smerzi, M. K. Oberthaler, R. Schmied, and P. Treutlein. Quantum metrology with nonclassical states of atomic ensembles.Rev. Mod. Phys., 90:035005, Sep 2018

    work page 2018

  18. [18]

    M. F. Parsons, A. Mazurenko, C. S. Chiu, G. Ji, D. Greif, and M. Greiner. Site-resolved measurement of the spin- correlation function in the Fermi-Hubbard model.Sci- ence, 353(6305):1253–1256, 2016

    work page 2016

  19. [19]

    Theocharis, A

    G. Theocharis, A. Weller, J. P. Ronzheimer, C. Gross, M. K. Oberthaler, P. G. Kevrekidis, and D. J. Frantzeskakis. Multiple atomic dark solitons in cigar- shaped Bose-Einstein condensates.Phys. Rev. A, 81(6):063604, 2010

    work page 2010

  20. [20]

    A. R. Fritsch, Mingwu Lu, G. H. Reid, A. M. Pi˜ neiro, and I. B. Spielman. Creating solitons with controllable and near-zero velocity in Bose-Einstein condensates.Phys. Rev. A, 101(5):053629, 2020

    work page 2020

  21. [21]

    S. Guo, A. R. Fritsch, C. Greenberg, I. B. Spielman, and J. P. Zwolak. Machine-learning enhanced dark soliton detection in Bose–Einstein condensates.Mach. Learn.: Sci. Technol., 2(3):035020, 2021

    work page 2021

  22. [22]

    S. Guo, S. M. Koh, A. R. Fritsch, I. B. Spielman, and J. P. Zwolak. Combining machine learning with physics: A framework for tracking and sorting multiple dark soli- tons.Phys. Rev. Research, 4(2):023163, 2022

    work page 2022

  23. [23]

    A. R. Fritsch, S. Guo, S. M. Koh, I. B. Spielman, and J. P. 21 Zwolak. Dark solitons in Bose–Einstein condensates: a dataset for many-body physics research.Mach. Learn.: Sci. Technol., 3(4):047001, 2022

    work page 2022

  24. [24]

    Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

    K. Simonyan, A. Vedaldi, and A. Zisserman. Deep inside convolutional networks: Visualising image classification models and saliency maps.arXiv:1312.6034, 2013

    work page internal anchor Pith review Pith/arXiv arXiv 2013

  25. [25]

    R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra. Grad-CAM: Visual Explana- tions from Deep Networks via Gradient-Based Localiza- tion.Int. J. Comput. Vis., 128(2):336–359, 2020

    work page 2020

  26. [26]

    Y. Lou, R. Caruana, J. Gehrke, and G. Hooker. Accurate intelligible models with pairwise interactions. InProceed- ings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 623– 631, 2013

    work page 2013

  27. [27]

    H. Nori, S. Jenkins, P. Koch, and R. Caruana. Inter- pretML: A Unified Framework for Machine Learning In- terpretability.arXiv:1909.09223, 2019

    work page arXiv 1909

  28. [28]

    Altuntas and I

    E. Altuntas and I. B. Spielman. Self-Bayesian aberration removal via constraints for ultracold atom microscopy. Phys. Rev. Research, 3:043087, 2021

    work page 2021

  29. [29]

    M. Zhao, J. Tao, and I. B. Spielman. Kolmogorov scaling in turbulent 2d Bose-Einstein condensates.Phys. Rev. Lett., 134:083402, 2025

    work page 2025

  30. [30]

    X. Li, M. Ke, B. Yan, and Y. Wang. Reduction of inter- ference fringes in absorption imaging of cold atom cloud using eigenface method.Chin. Opt. Lett., 5(3):128–130, 2007

    work page 2007

  31. [31]

    S. R. Segal, Q. Diot, E. A. Cornell, A. A. Zozulya, and D. Z. Anderson. Revealing buried information: Statisti- cal processing techniques for ultracold-gas image analy- sis.Phys. Rev. A, 81:053601, 2010

    work page 2010

  32. [32]

    L. Niu, X. Guo, Y. Zhan, X. Chen, W. M. Liu, and X. Zhou. Optimized fringe removal algorithm for absorption images.Appl. Phys. Lett., 113(14):144103, 2018

    work page 2018

  33. [33]

    G. Ness, A. Vainbaum, C. Shkedrov, Y. Florshaim, and Y. Sagi. Single-exposure absorption imaging of ultra- cold atoms using deep learning.Phys. Rev. Applied, 14:014011, 2020

    work page 2020

  34. [34]

    Single shot imaging for cold atoms based on machine learning.Acta Phys

    Da-Wei Ying, Si-Hui Zhang, Shu-Jin Deng, and Hai-Bin Wu. Single shot imaging for cold atoms based on machine learning.Acta Phys. Sin., 72(14):144201, 2023

    work page 2023

  35. [35]

    Lee and Y

    K. Lee and Y. Shin. Dual-species atomic absorp- tion image reconstruction using deep neural networks. arXiv:2508.12120, 2025

    work page arXiv 2025

  36. [37]

    This does not gen- eralize to the optimal linear method, which is not built from a simple sum over orthogonal components

    For conventional PCA, this curve can be obtained by cu- mulatively summing the eigenvalues. This does not gen- eralize to the optimal linear method, which is not built from a simple sum over orthogonal components

    work page

  37. [39]

    Goodfellow, Y

    I. Goodfellow, Y. Bengio, and A. Courville.Deep learn- ing. MIT press Cambridge, 2016

    work page 2016

  38. [40]

    Ronneberger, P.Fischer, and T

    O. Ronneberger, P.Fischer, and T. Brox. U-net: Con- volutional networks for biomedical image segmentation. InMedical Image Computing and Computer-Assisted In- tervention (MICCAI), volume 9351 ofLNCS, pages 234–

    work page

  39. [42]

    K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. In2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 770–778, 2016

    work page 2016

  40. [43]

    J. S. Russel.Report of the Committee on Waves, pages 417–468. British reports VI, 1837

    work page

  41. [44]

    Lakshmanan.Tsunamis and Oceanographical Appli- cations of Solitons, pages 8506–8521

    M. Lakshmanan.Tsunamis and Oceanographical Appli- cations of Solitons, pages 8506–8521. Springer New York, New York, NY, 2009

    work page 2009

  42. [45]

    Hasegawa

    A. Hasegawa. Soliton-based optical communications: an overview.IEEE Journal of Selected Topics in Quantum Electronics, 6(6):1161–1172, 2000

    work page 2000

  43. [46]

    Kono and M

    M. Kono and M. M. ˇSkori´ c.Nonlinear Physics of Plas- mas, volume 62 ofSpringer Series on Atomic, Optical, and Plasma Physics. Springer Berlin, Heidelberg, 2010

    work page 2010

  44. [47]

    Dalfovo, S

    F. Dalfovo, S. Giorgini, L. P. Pitaevskii, and S. Stringari. Theory of Bose-Einstein condensation in trapped gases. Rev. Mod. Phys., 71(3):463–512, 1999

    work page 1999

  45. [48]

    D. J. Frantzeskakis. Dark solitons in atomic Bose- Einstein condensates: from theory to experiments.J. of Phys. A, 43(21):213001, 2010

    work page 2010

  46. [49]

    Busch and J

    T. Busch and J. R. Anglin. Motion of Dark Solitons in Trapped Bose-Einstein Condensates.Phys. Rev. Lett., 84(11):2298–2301, 2000

    work page 2000

  47. [51]

    B. P. Anderson, P. C. Haljan, C. A. Regal, D. L. Feder, L. A. Collins, C. W. Clark, and E. A. Cornell. Watching Dark Solitons Decay into Vortex Rings in a Bose-Einstein Condensate.Phys. Rev. Lett., 86(14):2926–2929, 2001

    work page 2001

  48. [52]

    Donadello, S

    S. Donadello, S. Serafini, M. Tylutki, L. P. Pitaevskii, F. Dalfovo, G. Lamporesi, and G. Ferrari. Observation of solitonic vortices in Bose-Einstein condensates.Phys. Rev. Lett., 113(6):065302, 2014

    work page 2014

  49. [53]

    L. M. Aycock, H. M. Hurst, D. K. Efimkin, D. Genkina, H.-I. Lu, V. M. Galitski, and I. B. Spielman. Brownian motion of solitons in a Bose-Einstein condensate.Proc. Natl Acad. Sci., 114(10):2503–2508, 2017

    work page 2017

  50. [54]

    A. M. Mateo and J. Brand. Stability and dispersion rela- tions of three-dimensional solitary waves in trapped Bose- Einstein condensates.New J. Phys., 17(12):125013, 2015

    work page 2015

  51. [55]

    J. P. Zwolak, S. Guo, A. R. Fritsch, and Ian B. Spielman. Dark solitons in BECs dataset 2.0. National Institute of Standards and Technology, 2021

    work page 2021

  52. [58]

    Hastie and R

    T. Hastie and R. Tibshirani. Generalized Additive Mod- els.Stat. Sci., 1(3):297–310, 1986

    work page 1986

  53. [60]

    In our experience, this power-law scaling is ubiquitous, but we are not aware of the underlying mechanism

    work page

  54. [61]

    This does not gen- eralize to the optimal linear method, which is not built from a simple sum over orthogonal components

    For conventional PCA, this curve can be obtained by cu- mulatively summing the eigenvalues. This does not gen- eralize to the optimal linear method, which is not built from a simple sum over orthogonal components. 22

    work page

  55. [62]

    To use standard SVD tooling with our large dataset, the initial 644×484 images were down-sampled to 322×242

    work page

  56. [63]

    As opposed to the U-Net with shortcut connections, this structure is better able to filter noise processes such as shot noise and readout noise

    work page

  57. [64]

    This class includes any feature whose projection is a clean density depletion, potentially including: kink solitons, longitudinally aligned solitonic vortices, and large-radius vortex-rings to name a few

    work page

  58. [65]

    After accounting for the magnification of our imaging system, the 648×488 pixel raw images (Point Grey FL3, 5.6µm pixel pitch) have an effective pixel size of 0.93µm, appreciably smaller than our 2.8µm optical resolution

    work page

  59. [66]

    Generalized linear models are a broad class of models that extend ordinary linear regression to outcomes that are not well-modeled by a Gaussian with constant vari- ance

    work page

  60. [67]

    In the one-vs-rest setting, each class is treated as the positive class in turn, with all remaining classes grouped as negatives

    work page