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REVIEW 2 major objections 1 minor 104 references

Automated Charge Transition Detection in Quantum Dot Charge Stability Diagrams

T0 review · 2 major / 1 minor · reviewed 2026-08-05 · deepseek-v4-flash

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Pith's one-line read The paper claims that edge-detection models trained only on simulated charge stability diagrams can locate charge transitions in real GaAs and SiGe qubit devices, with a hardware-oriented comparison of methods.

desk verdict The supplied full text is an unrelated hep-th paper, so the actual work is unreviewable; the abstract promises a sensible sim-to-experiment transfer test, but no evidence can be checked until the correct manuscript appears. read the letter →

arxiv 2508.09024 v1 pith:377E42XQ submitted 2025-08-12 cond-mat.mes-hall quant-ph

Fabian Hader , Fabian Fuchs , Sarah Fleitmann , Karin Havemann , Benedikt Scherer , Jan Vogelbruch , Lotte Geck , Stefan van Waasen This is my paper
classification cond-mat.mes-hallquant-ph
keywords chargestabilitydiagramquantumdottransitiondetectionedgesimulation-to-realtransferqubittuningsemiconductormachinelearning
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 addresses a bottleneck in automating semiconductor qubit tuning: reliably locating charge transitions in charge stability diagrams. It investigates several detection methods, trains them exclusively on simulated diagrams produced by the SimCATS framework, and compares them quantitatively with future hardware implementation in mind. The authors then evaluate the optimized approaches on experimentally measured stability diagrams from a GaAs and a SiGe qubit sample. The claim is that simulation-trained detectors transfer to real devices of at least two material platforms, which would remove the need for hand-annotated experimental training data in automated tuning pipelines.

What carries the argument

The central object is the charge stability diagram, in which electron number changes appear as edges; the detection machinery is a set of image-analysis methods (likely including classical edge detectors and learned models) trained on diagrams generated by the SimCATS simulation framework. SimCATS is the load-bearing element: it supplies labeled training data whose visual statistics must approximate those of real experimental diagrams, including noise, crosstalk, and imaging artifacts, so that the trained detector transfers.

What would settle it

A concrete test: train a detector on SimCATS diagrams, then evaluate it on experimentally measured stability diagrams from a silicon MOS quantum dot device (a platform absent from the paper) with independently verified charge transitions; if accuracy drops to chance or transitions are systematically missed, the claim that simulation-trained detectors transfer across platforms is falsified. A second check is a direct pixel-level comparison of SimCATS-generated and real diagrams under controlled noise conditions.

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Extended reading notes

Core claim

The central claim is that charge-transition edges in experimentally measured charge stability diagrams can be detected reliably by models trained only on simulated data from the SimCATS framework, and that the best of these approaches are suitable for the computational constraints of future hardware implementation. On the paper's own terms, the discovery is the demonstrated transfer: the same optimized detectors, with no retraining on experimental images, locate the edges on both a GaAs and a SiGe qubit sample. This constitutes evidence that the simulation-to-experiment gap for stability-diagram image features is bridgeable, at least for the two material platforms tested.

Load-bearing premise

The load-bearing premise is that SimCATS-generated simulated stability diagrams faithfully match the visual appearance of the real GaAs and SiGe experimental diagrams—edge contrast, noise, crosstalk, and artifacts—and that the ground-truth labels used to score the experimental data are correct.

Editorial extensions

If this is right

  • Automated qubit tuning can proceed without human-annotated experimental stability diagrams, since the detector is trained purely on simulation.
  • The quantitative comparison provides a method-selection benchmark for edge detection that balances accuracy, speed, and hardware resource use.
  • The approach is not tied to one material platform: detectors trained on simulation work on both GaAs and SiGe devices, suggesting broader applicability to other gate-defined quantum dot systems.
  • This enables closed-loop control loops to recognize charge transitions online, since the detector's computational profile is designed with hardware implementation in mind.

Reading between the lines

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

  • The full text supplied with this manuscript is a different paper (a heat-kernel calculation), so this extraction rests on the abstract and metadata; the specific detection methods and error metrics are not examined.
  • The transfer claim is only as strong as SimCATS's fidelity to real devices; if the simulator's parameters were tuned using the same GaAs and SiGe samples used for final evaluation, the reported transfer could overestimate out-of-sample generalization.
  • A natural extension is to train on SimCATS diagrams for one device geometry and test on a third platform (e.g., silicon MOS qubits) to map the generalization boundary of simulation-trained detectors.
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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

2 major / 1 minor

Summary. The abstract describes a study of automated charge-transition-edge detection in quantum dot charge stability diagrams, claiming that detection methods are trained on simulated data from the SimCATS framework, compared quantitatively, and evaluated on experimental GaAs and SiGe qubit samples. The submitted full text, however, is a completely different manuscript titled "Heat kernel of non-minimal second-order operators" (arXiv:2508.09017v2, hep-th). None of the methods, simulations, experimental datasets, metrics, or results described in the abstract appear in the submitted manuscript. The central claim is therefore unsupported by the submitted material.

Significance. If the claimed result were properly presented, it would be significant: simulation-trained edge detection that transfers to experimental GaAs and SiGe stability diagrams would be a useful step toward automated qubit tuning, and the structurally non-circular train-on-simulation/evaluate-on-experiment design is methodologically appealing. The paper also promises a quantitative comparison suitable for hardware implementation, which is valuable. However, as submitted, no part of the actual study is available for assessment. The significance cannot be evaluated beyond the abstract's assertion, and the manuscript in its current form provides no evidence for the central claim.

major comments (2)
  1. [Full text (title and all sections)] The submitted full text is not the paper described in the abstract. After the abstract, the manuscript is "Heat kernel of non-minimal second-order operators" by Dario Sauro, arXiv:2508.09017v2 [hep-th], which concerns Seeley-DeWitt coefficients and torsion, not quantum dot charge stability diagrams. Consequently, none of the claimed SimCATS simulations, detection methods, quantitative comparisons, or GaAs/SiGe experimental evaluations are present. The abstract alone cannot support the central empirical claim; the manuscript as submitted is unassessable.
  2. [Abstract] The abstract promises a "quantitative comparison" of detection methods and an evaluation "on experimentally measured data from a GaAs and a SiGe qubit sample," but it provides no metrics, sample sizes, error bars, or methodological details. Even if the correct full text were supplied, the abstract itself is insufficient to establish the simulation-fidelity and ground-truth-labeling premises that the transfer claim rests on. In the current submission, this lack is compounded by the absence of the actual study, making the central claim entirely unverifiable.
minor comments (1)
  1. [Abstract] The abstract would benefit from a reference to the SimCATS framework and from stating the number of simulated and experimental diagrams used, so that the reader can gauge the scale of the comparison. These are presentation issues relative to the apparent intended paper, but they are not the primary problem with the submission.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity found; the provided full text does not implement the claimed quantum-dot study, so there is no derivation chain to reduce.

full rationale

The requested circularity audit applies to the claimed derivation chain: models trained on SimCATS-simulated charge stability diagrams and evaluated on GaAs/SiGe experimental data. In principle, this design is structurally non-circular because training and test data come from different sources (simulation vs. experiment). However, the full text supplied in the paper is not the quantum-dot paper: it is a different manuscript on heat-kernel coefficients for non-minimal second-order operators by Dario Sauro. Consequently, the claimed SimCATS training, quantitative comparison, and experimental evaluation are not present in the text provided. There is no quoted equation, fitted parameter, or self-citation that exhibits a reduction of a prediction to an input. Since the instructions forbid flagging circularity without specific textual evidence and prohibit speculation about author intent, the correct finding is no circularity (score 0). This should not be read as endorsing the abstract's scientific validity: the mismatch makes the central claim unassessable from the supplied material, but unassessability is a completeness/integrity issue, not a circularity defect.

Assumptions & free parameters 2 free parameters · 3 assumptions · 0 invented entities

The abstract-only review permits only two categories of inferred parameters: the evaluated detectors' hyperparameters and thresholds, which must be trained or chosen somewhere in the pipeline, and the SimCATS simulator's internal parameters, which control how realistic the synthetic diagrams are. If SimCATS parameters were fitted on the same GaAs and SiGe samples used for evaluation, the transfer result would be partially circular; this is a risk to audit in the full paper, not a confirmed flaw. No new physical entities are introduced: SimCATS is a software artifact, not a physical postulate.

free parameters (2)
  • Detector hyperparameters (thresholds, kernel sizes, network weights)
    Each evaluated detection method almost certainly has thresholds or model weights fitted on the simulated validation set; values are not given in the abstract, so they cannot be audited.
  • SimCATS simulation parameters (noise levels, capacitances, temperature)
    The simulator has physical parameters controlling the appearance of the simulated diagrams; if they are tuned to match the experimental samples used for evaluation, the transfer experiment is partially fitted. Not verifiable from the abstract.
assumptions (3)
  • domain assumption Charge transitions in gate-defined quantum dots appear as discernible edges in charge stability diagrams
    Standard physics of Coulomb blockade in quantum dots; this is the premise that makes edge detection a valid representation of charge transitions. Invoked implicitly by the abstract's framing.
  • domain assumption Training on simulated stability diagrams transfers to experimental stability diagrams
    Sim-to-real transfer is the methodological core of the paper; the abstract states training on SimCATS data and evaluation on experimental samples, which assumes the simulator distribution covers the experimental distribution.
  • domain assumption Performance on one GaAs and one SiGe sample generalizes to other quantum dot platforms
    The abstract evaluates the optimized approaches on a GaAs and a SiGe sample and frames the result for general qubit tuning; cross-material generalizability is an unstated extrapolation.

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Cite this review

Pith. "Pith review of Automated Charge Transition Detection in Quantum Dot Charge Stability Diagrams." pith.science (2026). https://pith.science/paper/377E42XQ

@misc{pith2026250809024,
  author       = {Pith},
  title        = {Pith review of: Automated Charge Transition Detection in Quantum Dot Charge Stability Diagrams},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/377E42XQ}},
  note         = {Machine review of arXiv:2508.09024}
}
read the original abstract

Gate-defined semiconductor quantum dots require an appropriate number of electrons to function as qubits. The number of electrons is usually tuned by analyzing charge stability diagrams, in which charge transitions manifest as edges. Therefore, to fully automate qubit tuning, it is necessary to recognize these edges automatically and reliably. This paper investigates possible detection methods, describes their training with simulated data from the SimCATS framework, and performs a quantitative comparison with a future hardware implementation in mind. Furthermore, we investigated the quality of the optimized approaches on experimentally measured data from a GaAs and a SiGe qubit sample.

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Works this paper leans on

104 extracted references · 15 canonical work pages

  1. [1]

    4" FUNCTION default.is.dash.repeated.names #1 FUNCTION default.name.format.string

    11em plus .33em minus .07em 4000 4000 500 `\.=1000 = biography IEEEbiography biographynophoto IEEEbiographynophoto biography [2][] 1in =0pt 1em 500 \@IEEEtrantmpdimenA=\@IEEEBIOhangdepth \@IEEEtrantmpdimenA by \@IEEEBIOskipN \@IEEEtrantmpdimenA by 1 \@IEEEtranneedspace \@IEEEtrantmpdimenA \@IEEEBIOskipN plus 1fil minus 0 \@IEEEtempbiographybox 0pt minipag...

    2015

  2. [2]

    write newline

    " write newline "" initialize.prev.this.status FUNCTION begin.bib " write newline preamble empty 'skip preamble write newline if " thebibliography " longest.label * " " * write newline " [1] #1 " write newline " url@samestyle " write newline " " write newline " [2] #2 " write newline " =0pt " write newline " " ALTinterwordstretchfactor * " " * write newli...

  3. [3]

    11em plus .33em minus .07em 4000 4000 100 4000 4000 500 `\.=1000 = #1 \@IEEEnotcompsoconly \@IEEEcompsoconly #1 * [1] 0pt [0pt][0pt] #1 * [1] 0pt [0pt][0pt] #1 * \| ** #1 \@IEEEauthorblockNstyle \@IEEEcompsocnotconfonly \@IEEEauthorblockAstyle \@IEEEcompsocnotconfonly \@IEEEcompsocconfonly \@IEEEauthordefaulttextstyle \@IEEEcompsocnotconfonly \@IEEEauthor...

  4. [4]

    J. M. Elzerman, R. Hanson, J. S. Greidanus, L. H. Willems van Beveren, S. De Franceschi, L. M. K. Vandersypen, S. Tarucha, and L. P. Kouwenhoven, ``Few-electron quantum dot circuit with integrated charge read out,'' Physical Review B, vol. 67, no. 16, p. 161308, Apr. 2003, publisher: American Physical Society. [Online]. Available: https://link.aps.org/doi...

    work page doi:10.1103/physrevb.67.161308 2003

  5. [5]

    L. Geck, A. Kruth, H. Bluhm, S. van Waasen, and S. Heinen, ``Control electronics for semiconductor spin qubits,'' Quantum Science and Technology, vol. 5, 12 2019

    2019

  6. [6]

    Ruffino, T.-Y

    A. Ruffino, T.-Y. Yang, J. Michniewicz, Y. Peng, E. Charbon, and M. F. Gonzalez-Zalba, `` en A cryo- CMOS chip that integrates silicon quantum dots and multiplexed dispersive readout electronics ,'' en Nature Electronics , vol. 5, no. 1, pp. 53--59, Jan. 2022, publisher: Nature Publishing Group. [Online]. Available: https://www.nature.com/articles/s41928-...

    2022

  7. [7]

    Hader, S

    F. Hader, S. Fleitmann, J. Vogelbruch, L. Geck, and S. v. Waasen, ``Simulation of charge stability diagrams for automated tuning solutions (simcats),'' IEEE Transactions on Quantum Engineering, vol. 5, pp. 1--14, 2024, doi: 10.1109/TQE.2024.3445967 https://doi.org/10.1109/TQE.2024.3445967

    arXiv 2024

  8. [8]

    J. P. Zwolak and J. M. Taylor, ``Colloquium: Advances in automation of quantum dot devices control,'' Reviews of Modern Physics, vol. 95, no. 1, p. 011006, 2023, publisher: American Physical Society. [Online]. Available: https://link.aps.org/doi/10.1103/RevModPhys.95.011006

    doi:10.1103/revmodphys.95.011006 2023

Show all 104 references
  1. [9]

    Darulová, S

    J. Darulová, S. J. Pauka, N. Wiebe, K. W. Chan, G. C. Gardener, M. J. Manfra, M. C. Cassidy, and M. Troyer, ``Autonomous tuning and charge state detection of gate defined quantum dots,'' 2019. [Online]. Available: http://arxiv.org/abs/1911.10709

    2019 arXiv

  2. [10]

    Severin, D

    B. Severin, D. T. Lennon, L. C. Camenzind, F. Vigneau, F. Fedele, D. Jirovec, A. Ballabio, D. Chrastina, G. Isella, M. de Kruijf, M. J. Carballido, S. Svab, A. V. Kuhlmann, S. Geyer, F. N. M. Froning, H. Moon, M. A. Osborne, D. Sejdinovic, G. Katsaros, D. M. Zumbühl, G. a. D. ...

    2024

  3. [11]

    S. S. Kalantre, J. P. Zwolak, S. Ragole, X. Wu, N. M. Zimmerman, M. D. Stewart, and J. M. Taylor, ``Machine Learning techniques for state recognition and auto-tuning in quantum dots,'' npj Quantum Information, vol. 5, no. 1, p. 6, 2019, doi: 10.1038/s41534-018-0118-7 https://d...

    2019 arXiv

  4. [12]

    J. P. Zwolak, T. McJunkin, S. S. Kalantre, J. P. Dodson, E. R. MacQuarrie, D. E. Savage, M. G. Lagally, S. N. Coppersmith, M. A. Eriksson, and J. M. Taylor, ``Autotuning of double dot devices in situ with machine learning,'' Physical review applied, vol. 13, 2020, doi: 10.1103...

    2020 doi

  5. [13]

    Ziegler, T

    J. Ziegler, T. McJunkin, E. S. Joseph, S. S. Kalantre, B. Harpt, D. E. Savage, M. G. Lagally, M. A. Eriksson, J. M. Taylor, and J. P. Zwolak, ``Toward Robust Autotuning of Noisy Quantum Dot Devices .'' [Online]. Available: http://arxiv.org/abs/2108.00043

    arXiv

  6. [14]

    Y. Muto, T. Nakaso, M. Shinozaki, T. Aizawa, T. Kitada, T. Nakajima, M. Delbecq, J. Yoneda, K. Takeda, A. Noiri, A. Ludwig, A. Wieck, S. Tarucha, A. Kanemura, M. Shiga, and T. Otsuka, ``Visual explanations of machine learning model estimating charge states in quantum dots,'' A...

    2024 doi

  7. [15]

    H. Liu, B. Wang, N. Wang, Z. Sun, H. Yin, H. Li, G. Cao, and G. Guo, ``An automated approach for consecutive tuning of quantum dot arrays,'' Applied Physics Letters, vol. 121, no. 8, p. 084002, 2022, doi: 10.1063/5.0111128 https://doi.org/10.1063/5.0111128. [Online]. Available...

    2022 doi

  8. [16]

    T. A. Baart, P. T. Eendebak, C. Reichl, W. Wegscheider, and L. M. K. Vandersypen, `` en Computer-automated tuning of semiconductor double quantum dots into the single-electron regime ,'' en Applied Physics Letters , vol. 108, no. 21, p. 213104, May 2016, arXiv: 1603.02274. [On...

    2016 arXiv

  9. [17]

    Lapointe-Major, O

    M. Lapointe-Major, O. Germain, J. Camirand Lemyre, D. Lachance-Quirion, S. Rochette, F. Camirand Lemyre, and M. Pioro-Ladrière, `` en Algorithm for automated tuning of a quantum dot into the single-electron regime ,'' en Physical Review B , vol. 102, no. 8, p. 085301, Aug. 202...

    2020 doi

  10. [18]

    H. Moon, D. T. Lennon, J. Kirkpatrick, N. M. van Esbroeck, L. C. Camenzind, L. Yu, F. Vigneau, D. M. Zumbühl, G. A. D. Briggs, M. A. Osborne, D. Sejdinovic, E. A. Laird, and N. Ares, ``Machine learning enables completely automatic tuning of a quantum device faster than human e...

    2020 doi

  11. [19]

    van Straaten, F

    B. van Straaten, F. Fedele, F. Vigneau, J. Hickie, D. Jirovec, A. Ballabio, D. Chrastina, G. Isella, G. Katsaros, and N. Ares. All rf-based tuning algorithm for quantum devices using machine learning. arXiv.org. [Online]. Available: https://arxiv.org/abs/2211.04504v1

  12. [20]

    Durrer, B

    R. Durrer, B. Kratochwil, J. V. Koski, A. J. Landig, C. Reichl, W. Wegscheider, T. Ihn, and E. Greplova, `` en Automated tuning of double quantum dots into specific charge states using neural networks ,'' en Physical Review Applied , vol. 13, no. 5, p. 054019, May 2020, arXiv:...

    2020 arXiv

  13. [21]

    Czischek, V

    S. Czischek, V. Yon, M.-A. Genest, M.-A. Roux, S. Rochette, J. Camirand Lemyre, M. Moras, M. Pioro-Ladrière, D. Drouin, Y. Beilliard, and R. G. Melko, `` en Miniaturizing neural networks for charge state autotuning in quantum dots ,'' en Machine Learning: Science and Technolog...

    2022 doi

  14. [22]

    Ziegler, F

    J. Ziegler, F. Luthi, M. Ramsey, F. Borjans, G. Zheng, and J. P. Zwolak, ``Tuning arrays with rays: Physics -informed tuning of quantum dot charge states,'' Physical Review Applied, vol. 20, no. 3, p. 034067, Sep. 2023, arXiv:2209.03837 [cond-mat, physics:quant-ph]. [Online]. ...

    2023 arXiv

  15. [23]

    J. P. Zwolak, T. McJunkin, S. S. Kalantre, S. F. Neyens, E. R. MacQuarrie, M. A. Eriksson, and J. M. Taylor, ``Ray-based framework for state identification in quantum dot devices,'' PRX Quantum, vol. 2, no. 2, p. 020335, 2021, doi: 10.1103/PRXQuantum.2.020335 https://doi.org/1...

    2021 arXiv

  16. [24]

    J. P. Zwolak, S. S. Kalantre, X. Wu, S. Ragole, and J. M. Taylor, `` QFlow lite dataset: A machine-learning approach to the charge states in quantum dot experiments,'' PLOS ONE, vol. 13, no. 10, p. e0205844, 2018, doi: 10.1371/journal.pone.0205844 https://doi.org/10.1371/journ...

    2018 doi

  17. [25]

    C. J. van Diepen, P. T. Eendebak, B. T. Buijtendorp, U. Mukhopadhyay, T. Fujita, C. Reichl, W. Wegscheider, and L. M. K. Vandersypen, ``Automated tuning of inter-dot tunnel coupling in double quantum dots,'' Applied Physics Letters, vol. 113, no. 3, p. 033101, 2018, doi: 10.10...

    2018 doi

  18. [26]

    A. R. Mills, M. M. Feldman, C. Monical, P. J. Lewis, K. W. Larson, A. M. Mounce, and J. R. Petta, ``Computer-automated tuning procedures for semiconductor quantum dot arrays,'' Applied Physics Letters, vol. 115, no. 11, p. 113501, 2019, doi: 10.1063/1.5121444 https://doi.org/1...

    2019 doi

  19. [27]

    Monical, P

    C. Monical, P. Lewis, A. Agron, K. Larson, and A. Mounce, ``Image Processing Algorithms for Tuning Quantum Devices and Nitrogen-Vacancy Imaging .'' no. SAND2019-11786, 1662017, 690548, pp. SAND2019--11\,786, 1\,662\,017, 690\,548, 2020, doi: 10.2172/1662017 https://doi.org/10....

    2020

  20. [28]

    J. D. Teske, S. Humpohl, R. Otten, P. Bethke, P. Cerfontaine, J. Dedden, A. Ludwig, A. D. Wieck, and H. Bluhm, ``A Machine Learning Approach for Automated Fine-Tuning of Semiconductor Spin Qubits ,'' Applied Physics Letters, vol. 114, no. 13, p. 133102, 2019, doi: 10.1063/1.50...

    2019 arXiv

  21. [29]

    N. M. van Esbroeck, D. T. Lennon, H. Moon, V. Nguyen, F. Vigneau, L. C. Camenzind, L. Yu, D. M. Zumbühl, G. A. D. Briggs, D. Sejdinovic, and N. Ares, ``Quantum device fine-tuning using unsupervised embedding learning,'' New Journal of Physics, vol. 22, no. 9, p. 095003, 2020, ...

    2020 arXiv

  22. [30]

    Schuff, M

    J. Schuff, M. J. Carballido, M. Kotzagiannidis, J. C. Calvo, M. Caselli, J. Rawling, D. L. Craig, B. van Straaten, B. Severin, F. Fedele, S. Svab, P. C. Kwon, R. S. Eggli, T. Patlatiuk, N. Korda, D. Zumbühl, and N. Ares. Fully autonomous tuning of a spin qubit. doi: 10.48550/a...

    doi

  23. [31]

    D. T. Lennon, H. Moon, L. C. Camenzind, L. Yu, D. M. Zumbühl, G. A. D. Briggs, M. A. Osborne, E. A. Laird, and N. Ares, ``Efficiently measuring a quantum device using machine learning,'' npj Quantum Information, vol. 5, no. 1, p. 79, 2019, doi: 10.1038/s41534-019-0193-4 https:...

    2019 doi

  24. [32]

    Nguyen, S

    V. Nguyen, S. B. Orbell, D. T. Lennon, H. Moon, F. Vigneau, L. C. Camenzind, L. Yu, D. M. Zumbühl, G. a. D. Briggs, M. A. Osborne, D. Sejdinovic, and N. Ares, ``Deep reinforcement learning for efficient measurement of quantum devices,'' npj Quantum Information, vol. 7, no. 1, ...

    2021 doi

  25. [33]

    Matsumoto, T

    Y. Matsumoto, T. Fujita, A. Ludwig, A. D. Wieck, K. Komatani, and A. Oiwa, ``Noise-robust classification of single-shot electron spin readouts using a deep neural network,'' npj Quantum Information, vol. 7, no. 1, pp. 1--7, 2021, doi: 10.1038/s41534-021-00470-7 https://doi.org...

    2021 doi

  26. [34]

    Hader, `` SimCATS-datasets ,'' Dec

    F. Hader, `` SimCATS-datasets ,'' Dec. 2023. [Online]. Available: https://github.com/f-hader/SimCATS-Datasets

    2023

  27. [35]

    Qubyte

    C. Volk, A. M. J. Zwerver, U. Mukhopadhyay, P. T. Eendebak, C. J. van Diepen, J. P. Dehollain, T. Hensgens, T. Fujita, C. Reichl, W. Wegscheider, and L. M. K. Vandersypen, ``Loading a quantum-dot based “ Qubyte ” register,'' npj Quantum Information, vol. 5, no. 1, pp. 1--8, Ap...

    2019

  28. [36]

    Hader, `` SimCATS ,'' Dec

    F. Hader, `` SimCATS ,'' Dec. 2023. [Online]. Available: https://github.com/f-hader/SimCATS

    2023

  29. [37]

    Struck, M

    T. Struck, M. Volmer, L. Visser, T. Offermann, R. Xue, J.-S. Tu, S. Trellenkamp, . Cywiński, H. Bluhm, and L. R. Schreiber, `` en Spin- EPR -pair separation by conveyor-mode single electron shuttling in Si / SiGe ,'' en Nature Communications , vol. 15, no. 1, p. 1325, Feb. 202...

    2024

  30. [38]

    Seidlitz, J

    S. Seidlitz, J. Sellner, J. Odenthal, B. Özdemir, A. Studier-Fischer, S. Knödler, L. Ayala, T. J. Adler, H. G. Kenngott, M. Tizabi, M. Wagner, F. Nickel, B. P. Müller-Stich, and L. Maier-Hein, ``Robust deep learning-based semantic organ segmentation in hyperspectral images,'' ...

    2022

  31. [39]

    (2023) calflops: a flops and params calculate tool for neural networks in pytorch framework

    xiaoju ye. (2023) calflops: a flops and params calculate tool for neural networks in pytorch framework. [Online]. Available: https://github.com/MrYxJ/calculate-flops.pytorch

    2023

  32. [40]

    Available: https://github.com/Lavender105/DFF

    `` DFF and CASENet source code.'' [Online]. Available: https://github.com/Lavender105/DFF

  33. [41]

    Available: https://github.com/elharroussomar/Refined-Edge-Detection-With-Cascaded-and-High-Resolution-Convolutional-Network

    `` CHRNet source code.'' [Online]. Available: https://github.com/elharroussomar/Refined-Edge-Detection-With-Cascaded-and-High-Resolution-Convolutional-Network

  34. [42]

    Iakubovskii, `` Segmentation Models Pytorch ,'' https://github.com/qubvel/segmentation_models.pytorch, 2019

    P. Iakubovskii, `` Segmentation Models Pytorch ,'' https://github.com/qubvel/segmentation_models.pytorch, 2019

    2019

  35. [43]

    Available: https://github.com/xavysp/LDC

    `` LDC source code .'' [Online]. Available: https://github.com/xavysp/LDC

  36. [44]

    Available: https://github.com/xavysp/TEED

    `` TEED source code.'' [Online]. Available: https://github.com/xavysp/TEED

  37. [45]

    Available: https://github.com/milesial/Pytorch-UNet

    `` U-Net source code.'' [Online]. Available: https://github.com/milesial/Pytorch-UNet

  38. [46]

    Available: https://github.com/LouisNUST/CrackFormer-II

    `` CrackFormer source code.'' [Online]. Available: https://github.com/LouisNUST/CrackFormer-II

  39. [47]

    Available: https://github.com/MengyangPu/EDTER

    `` EDTER source code.'' [Online]. Available: https://github.com/MengyangPu/EDTER

  40. [48]

    Available: https://github.com/yywbkn/MMViT-Segmentation

    `` MMViT-Seg source code.'' [Online]. Available: https://github.com/yywbkn/MMViT-Segmentation

  41. [49]

    Available: https://github.com/NVlabs/SegFormer

    `` SegFormer source code.'' [Online]. Available: https://github.com/NVlabs/SegFormer

  42. [50]

    Available: https://github.com/rstrudel/segmenter

    `` Segmenter source code.'' [Online]. Available: https://github.com/rstrudel/segmenter

  43. [51]

    Available: https://github.com/HuCaoFighting/Swin-Unet

    `` Swin-Unet source code.'' [Online]. Available: https://github.com/HuCaoFighting/Swin-Unet

  44. [52]

    Available: https://github.com/Beckschen/TransUNet

    `` TransUNet source code.'' [Online]. Available: https://github.com/Beckschen/TransUNet

  45. [53]

    Available: https://github.com/JCruan519/VM-UNet

    `` VM-UNet source code.'' [Online]. Available: https://github.com/JCruan519/VM-UNet

  46. [54]

    Available: https://github.com/GuHuangAI/DiffusionEdge

    `` DiffusionEdge source code.'' [Online]. Available: https://github.com/GuHuangAI/DiffusionEdge

  47. [55]

    Available: https://github.com/MedicineToken/MedSegDiff

    `` MedSegDiff-V2 source code.'' [Online]. Available: https://github.com/MedicineToken/MedSegDiff

  48. [56]

    Available: https://scikit-image.org/docs/stable/api/skimage.feature.html#skimage.feature.canny

    ``Canny edge detector software interface.'' [Online]. Available: https://scikit-image.org/docs/stable/api/skimage.feature.html#skimage.feature.canny

  49. [57]

    Available: https://cvrs.whu.edu.cn/cannylines/#ct1

    `` CannyPF software interface.'' [Online]. Available: https://cvrs.whu.edu.cn/cannylines/#ct1

  50. [58]

    Available: https://github.com/shaojunluo/EDLinePython

    `` Edge Drawing software interface.'' [Online]. Available: https://github.com/shaojunluo/EDLinePython

  51. [59]

    Available: https://github.com/peterkovesi/ImagePhaseCongruency.jl

    `` Phase Congruency software interface.'' [Online]. Available: https://github.com/peterkovesi/ImagePhaseCongruency.jl

  52. [60]

    Available: https://github.com/HiDiYANG/gPb-GSoC

    `` gPb software interface.'' [Online]. Available: https://github.com/HiDiYANG/gPb-GSoC

  53. [61]

    Canny, ``A computational approach to edge detection,'' Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol

    J. Canny, ``A computational approach to edge detection,'' Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. PAMI-8, pp. 679 -- 698, 12 1986

    1986

  54. [62]

    X. Lu, J. Yao, K. Li, and L. Li, ``Cannylines: A parameter-free line segment detector,'' 09 2015, pp. 507--511

    2015

  55. [63]

    Maire, P

    M. Maire, P. Arbelaez, C. Fowlkes, and J. Malik, ``Using contours to detect and localize junctions in natural images,'' Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on, 06 2008

    2008

  56. [64]

    Morrone and R

    M. Morrone and R. Owens, ``Feature detection from local energy,'' Pattern Recognition Letters, vol. 6, no. 5, pp. 303--313, 1987. [Online]. Available: https://www.sciencedirect.com/science/article/pii/0167865587900134

    1987

  57. [65]

    Kovesi, ``Image features from phase congruency,'' Videre: Journal of Computer Vision Research, vol

    P. Kovesi, ``Image features from phase congruency,'' Videre: Journal of Computer Vision Research, vol. 1, no. 3, pp. 1--26, 1999

    1999

  58. [66]

    Akinlar and C

    C. Akinlar and C. Topal, ``Edlines: A real-time line segment detector with a false detection control,'' Pattern Recognition Letters, vol. 32, pp. 1633--1642, 10 2011

    2011

  59. [67]

    Elharrouss, Y

    O. Elharrouss, Y. Hmamouche, A. K. Idrissi, B. El Khamlichi , and A. El Fallah-Seghrouchni , ``Refined edge detection with cascaded and high-resolution convolutional network,'' Pattern Recognition, vol. 138, p. 109361, 2023. [Online]. Available: https://www.sciencedirect.com/s...

    2023

  60. [68]

    Soria, G

    X. Soria, G. Pomboza-Junez, and A. D. Sappa, `` LDC : Lightweight Dense CNN for Edge Detection ,'' IEEE Access, vol. 10, pp. 68\,281--68\,290, 2022. [Online]. Available: https://ieeexplore.ieee.org/document/9807316/

    2022

  61. [69]

    Soria, A

    X. Soria, A. Sappa, P. Humanante, and A. Akbarinia, ``Dense extreme inception network for edge detection,'' Pattern Recognition, vol. 139, p. 109461, 2023. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0031320323001619

    2023

  62. [70]

    L. Huan, N. Xue, X. Zheng, W. He, J. Gong, and G.-S. Xia, ``Unmixing Convolutional Features for Crisp Edge Detection ,'' Jun. 2021. [Online]. Available: http://arxiv.org/abs/2011.09808

    2021 arXiv

  63. [71]

    Soria, Y

    X. Soria, Y. Li, M. Rouhani, and A. D. Sappa, ``Tiny and Efficient Model for the Edge Detection Generalization ,'' Aug. 2023. [Online]. Available: http://arxiv.org/abs/2308.06468

    2023 arXiv

  64. [72]

    Z. Yu, C. Feng, M.-Y. Liu, and S. Ramalingam, ``Casenet: Deep category-aware semantic edge detection,'' in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 1761--1770

    2017

  65. [73]

    K. He, X. Zhang, S. Ren, and J. Sun, ``Deep residual learning for image recognition,'' 2015. [Online]. Available: https://arxiv.org/abs/1512.03385

    2015 arXiv

  66. [74]

    Y. Hu, Y. Chen, X. Li, and J. Feng, ``Dynamic feature fusion for semantic edge detection,'' 2019

    2019

  67. [75]

    Ronneberger, P

    O. Ronneberger, P. Fischer, and T. Brox, ``U- Net : Convolutional Networks for Biomedical Image Segmentation ,'' in Medical Image Computing and Computer - Assisted Intervention – MICCAI 2015 , ser. Lecture Notes in Computer Science , N. Navab, J. Hornegger, W. M. Wells, and A....

    2015

  68. [76]

    Z. Zhou, M. M. Rahman Siddiquee, N. Tajbakhsh, and J. Liang, `` UNet ++: A Nested U - Net Architecture for Medical Image Segmentation ,'' in Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support , ser. Lecture Notes in Computer Science ,...

    2018

  69. [77]

    T.-Y. Lin, P. Dollar, R. Girshick, K. He, B. Hariharan, and S. Belongie, ``Feature Pyramid Networks for Object Detection ,'' in 2017 IEEE Conference on Computer Vision and Pattern Recognition ( CVPR ) . 1em plus 0.5em minus 0.4em Honolulu, HI: IEEE, 2017, pp. 936--944. [Online...

    2017

  70. [78]

    L.-C. Chen, Y. Zhu, G. Papandreou, F. Schroff, and H. Adam, ``Encoder-decoder with atrous separable convolution for semantic image segmentation,'' 2018

    2018

  71. [79]

    L.-C. Chen, G. Papandreou, F. Schroff, and H. Adam, ``Rethinking atrous convolution for semantic image segmentation,'' 2017. [Online]. Available: https://arxiv.org/abs/1706.05587

    2017 arXiv

  72. [80]

    L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille, ``Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs,'' 2017. [Online]. Available: https://arxiv.org/abs/1606.00915

    2017 arXiv

  73. [81]

    Chaurasia and E

    A. Chaurasia and E. Culurciello, ``Linknet: Exploiting encoder representations for efficient semantic segmentation,'' in 2017 IEEE Visual Communications and Image Processing (VCIP). 1em plus 0.5em minus 0.4em IEEE, 12 2017. [Online]. Available: http://dx.doi.org/10.1109/VCIP.2...

    2017

  74. [82]

    Vaswani, N

    A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, ``Attention is all you need,'' in Proceedings of the 31st International Conference on Neural Information Processing Systems, ser. NIPS'17. 1em plus 0.5em minus 0.4em Red Hook,...

    2017

  75. [83]

    Dosovitskiy, L

    A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, and N. Houlsby, ``An image is worth 16x16 words: Transformers for image recognition at scale,'' ICLR, 2021

    2021

  76. [84]

    T. Fan, G. Wang, Y. Li, and H. Wang, ``Ma-net: A multi-scale attention network for liver and tumor segmentation,'' IEEE Access, vol. 8, pp. 179\,656--179\,665, 2020

    2020

  77. [85]

    Strudel, R

    R. Strudel, R. Garcia, I. Laptev, and C. Schmid, ``Segmenter: Transformer for Semantic Segmentation ,'' in 2021 IEEE / CVF International Conference on Computer Vision ( ICCV ) . 1em plus 0.5em minus 0.4em Montreal, QC, Canada: IEEE, Oct. 2021, pp. 7242--7252. [Online]. Availab...

    2021

  78. [86]

    E. Xie, W. Wang, Z. Yu, A. Anandkumar, J. M. Alvarez, and P. Luo, `` SegFormer : Simple and Efficient Design for Semantic Segmentation with Transformers ,'' in Advances in Neural Information Processing Systems , 2021

    2021

  79. [87]

    M. Pu, Y. Huang, Y. Liu, Q. Guan, and H. Ling, `` EDTER : Edge Detection with Transformer ,'' 2022. [Online]. Available: http://arxiv.org/abs/2203.08566

    2022 arXiv

  80. [88]

    Y. Yang, L. Zhang, L. Ren, and X. Wang, ``Mmvit-seg: A lightweight transformer and cnn fusion network for covid-19 segmentation,'' Computer Methods and Programs in Biomedicine, vol. 230, p. 107348, 2023. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0...

    2023

  81. [89]

    H. Liu, J. Yang, X. Miao, C. Mertz, and H. Kong, ``Crackformer network for pavement crack segmentation,'' IEEE Transactions on Intelligent Transportation Systems, vol. 24, no. 9, pp. 9240--9252, 2023

    2023

  82. [90]

    Badrinarayanan, A

    V. Badrinarayanan, A. Kendall, and R. Cipolla, ``Segnet: A deep convolutional encoder-decoder architecture for image segmentation,'' 2016

    2016

  83. [91]

    J. Chen, Y. Lu, Q. Yu, X. Luo, E. Adeli, Y. Wang, L. Lu, A. L. Yuille, and Y. Zhou, `` TransUNet : Transformers Make Strong Encoders for Medical Image Segmentation ,'' Feb. 2021. [Online]. Available: http://arxiv.org/abs/2102.04306

    2021 arXiv

  84. [92]

    H. Cao, Y. Wang, J. Chen, D. Jiang, X. Zhang, Q. Tian, and M. Wang, ``Swin-unet: Unet-like pure transformer for medical image segmentation,'' in Proceedings of the European Conference on Computer Vision Workshops(ECCVW), 2022

    2022

  85. [93]

    Z. Liu, Y. Lin, Y. Cao, H. Hu, Y. Wei, Z. Zhang, S. Lin, and B. Guo, ``Swin transformer: Hierarchical vision transformer using shifted windows,'' in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021

    2021

  86. [94]

    A. Gu, K. Goel, and C. Ré, ``Efficiently modeling long sequences with structured state spaces,'' arXiv, 2022. [Online]. Available: https://arxiv.org/abs/2111.00396

    2022 arXiv

  87. [95]

    Ruan and S

    J. Ruan and S. Xiang, `` VM - UNet : Vision Mamba UNet for Medical Image Segmentation ,'' Feb. 2024. [Online]. Available: http://arxiv.org/abs/2402.02491

    2024 arXiv

  88. [96]

    Y. Ye, K. Xu, Y. Huang, R. Yi, and Z. Cai, `` DiffusionEdge : Diffusion Probabilistic Model for Crisp Edge Detection ,'' 2024. [Online]. Available: http://arxiv.org/abs/2401.02032

    2024 arXiv

  89. [97]

    J. Wu, R. FU, H. Fang, Y. Zhang, Y. Yang, H. Xiong, H. Liu, and Y. Xu, ``Medsegdiff: Medical image segmentation with diffusion probabilistic model,'' in Medical Imaging with Deep Learning, 2023

    2023

  90. [98]

    J. Wu, W. Ji, H. Fu, M. Xu, Y. Jin, and Y. Xu, ``Medsegdiff-v2: Diffusion based medical image segmentation with transformer,'' arXiv preprint arXiv:2301.11798, 2023

    2023 arXiv

  91. [99]

    Paszke, S

    A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. K\" o pf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala, PyTorch: an imperative style, high...

    2019

  92. [100]

    L. N. Smith and N. Topin, ``Super- Convergence : Very Fast Training of Neural Networks Using Large Learning Rates ,'' May 2018, arXiv:1708.07120 [cs, stat]. [Online]. Available: http://arxiv.org/abs/1708.07120

    2018 arXiv

  93. [101]

    P. Ren, Y. Xiao, X. Chang, P.-Y. Huang, Z. Li, X. Chen, and X. Wang, ``A Comprehensive Survey of Neural Architecture Search : Challenges and Solutions ,'' Mar. 2021. [Online]. Available: http://arxiv.org/abs/2006.02903

    2021 arXiv

  94. [102]

    Benmeziane, K

    H. Benmeziane, K. E. Maghraoui, H. Ouarnoughi, S. Niar, M. Wistuba, and N. Wang, ``A Comprehensive Survey on Hardware - Aware Neural Architecture Search ,'' Jan. 2021, arXiv:2101.09336 [cs]. [Online]. Available: http://arxiv.org/abs/2101.09336

    2021 arXiv

  95. [103]

    Shekhar, A

    S. Shekhar, A. Bansode, and A. Salim, `` en A Comparative study of Hyper - Parameter Optimization Tools ,'' Jan. 2022. [Online]. Available: http://arxiv.org/abs/2201.06433

    2022 arXiv

  96. [104]

    Hader, `` SimCATS\_GaAs\_v1\_random\_variations\_v2 ,'' Oct

    F. Hader, `` SimCATS\_GaAs\_v1\_random\_variations\_v2 ,'' Oct. 2024. [Online]. Available: https://doi.org/10.5281/zenodo.13903285

    2024 doi

Pith tools

Reviewed August 5, 2026 · model on record in the stance chip above.