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REVIEW 2 major objections 2 minor 43 references

Localized Kernel Methods for Signal Processing

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

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Pith's one-line read A dissertation abstract claims two localized-kernel signal-processing methods that beat MUSIC and ESPRIT at low SNR and separate chirps down to -30 dB, but the attached full text contains none of these methods.

desk verdict The abstract describes a localized-kernel signal processing paper, but the attached full text is an unrelated quantum orchestration paper; there is no manuscript here to review. read the letter →

arxiv 2508.04978 v1 pith:THRPKIBI submitted 2025-08-07 eess.SP

Sippanon Kitimoon This is my paper
classification eess.SP
keywords localizedkernelsfrequencyestimationexponentialsignalmodelschirpseparationOperatorMUSICESPRITlowsignal-to-noiseratio
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 abstract claims a dissertation with two localized-kernel signal processing methods. The first estimates frequencies and amplitudes in multidimensional exponential models, beating MUSIC and ESPRIT at low SNR and requiring fewer samples in the multivariate case. The second separates intersecting and discontinuous linear chirps down to -30 dB SNR without knowing the component count, using a localized-kernel Signal Separation Operator. The supplied full text, however, is an unrelated paper on deep-reinforcement-learning scheduling for quantum cloud computing; it contains none of these methods, experiments, or comparisons. Thus the paper's scientific claims are present only in the abstract and cannot be verified or refuted from the body.

What carries the argument

The central objects are the localized trigonometric polynomial kernel and the localized-kernel variant of the Signal Separation Operator. These act as concentrated filters that, combined with FFT-based computation, are claimed to isolate signal components directly in the frequency or time-frequency domain. The chirp separation pipeline then clusters instantaneous-frequency estimates and applies piecewise linear regression, eliminating the need for subspace decomposition or sparsity constraints. The machinery's intended work is to replace parametric or model-order-dependent estimation (MUSIC/ESPRIT) with a nonlinear, data-driven localization step robust at very low SNR.

What would settle it

A reader who opens the full text and searches for 'MUSIC', 'ESPRIT', 'chirp', 'Signal Separation Operator', 'localized kernel', or '-30 dB' finds none of them; the text instead describes PPO-based orchestration of quantum processors. This complete absence of the advertised methods and experiments is the concrete observation that falsifies the paper's central claim as submitted.

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Extended reading notes

Core claim

The dissertation's central claim is that a specially designed localized trigonometric polynomial kernel, combined with efficient FFT-based processing, can recover parameters of exponential signal models more robustly than classical subspace methods. In the univariate setting the method is said to outperform MUSIC and ESPRIT at low signal-to-noise ratios, and a coordinate-wise projection-and-registration extension is said to achieve high multivariate accuracy with markedly fewer samples. A second claimed contribution replaces the conventional Signal Separation Operator with a localized-kernel variant: the algorithm estimates instantaneous frequencies by FFT-based filtering, clusters the estim

Load-bearing premise

The central claim rests on the assumption that the attached full text is the dissertation the abstract describes; in fact the full text is an unrelated quantum-cloud scheduling paper, so the claim collapses unless the correct text is supplied.

Editorial extensions

If this is right

  • If the univariate method beats MUSIC and ESPRIT at low SNR, it would give practitioners a subspace-free alternative for frequency estimation in noisy exponential models.
  • If the multivariate projection approach needs significantly fewer samples, it could extend exponential parameter recovery to settings where data is scarce.
  • If the chirp separator truly handles intersecting and discontinuous chirps at -30 dB without knowing the component count, it would target a regime where many classical methods collapse.
  • Because both methods are FFT-based and avoid sparsity regularization, any real implementation would be fast and relatively easy to tune.

Reading between the lines

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

  • The full-text mismatch suggests a submission error—either a wrong file upload or a metadata collision—so the real dissertation would need to be retrieved before the claims can be assessed; this is our inference, not something the paper states.
  • If the -30 dB chirp claim is later verified, the natural next stress test is nonlinear chirps, which the abstract itself names as future work; a curved instantaneous-frequency trajectory would challenge the piecewise-linear regression step.
  • Implicitly, the localized-kernel width is a free parameter that controls time-frequency resolution; the abstract does not say how it is chosen, so a robustness study across kernel bandwidths would be a meaningful extension.
  • The claim of recovering discontinuous chirps suggests the method could serve as an unsupervised estimator of the number of components, since no component-count prior is assumed; such an estimator would be a valuable by-product if confirmed.
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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 / 2 minor

Summary. The submission is titled "Localized Kernel Methods for Signal Processing" and its abstract claims two contributions: (i) a localized-trigonometric-polynomial-kernel method for parameter recovery in multidimensional exponential models that outperforms MUSIC and ESPRIT at low SNR, and (ii) a localized-kernel variant of the Signal Separation Operator (SSO) that separates linear chirps, including intersecting and discontinuous components, at SNR levels as low as -30 dB. However, the full text attached to the submission is not the corresponding dissertation or technical paper. It is the arXiv preprint 2508.04974v1, "QFOR: A Fidelity-aware Orchestrator for Quantum Computing Environments using Deep Reinforcement Learning," by Hoa T. Nguyen, Muhammad Usman, and Rajkumar Buyya. The full text contains no definitions of the claimed localized kernels, no SSO construction, no FFT-based filtering pipeline, no piecewise linear regression, no MUSIC/ESPRIT comparison, and no chirp experiments. The manuscript as submitted therefore has no body supporting the abstract's claims.

Significance. If the abstract's claims were substantiated, the work would be of practical interest to the signal processing community: robust low-SNR frequency/amplitude estimation without subspace decomposition, and chirp separation without knowing the number of components, are useful capabilities. The claimed -30 dB performance for chirp recovery would be noteworthy. However, the submitted manuscript provides none of the evidence needed to assess these claims. There are no equations, no algorithmic descriptions, no experimental protocols, no results tables, no reproducible code, and no machine-checked proofs. The only text is an abstract and an unrelated quantum-computing paper. Consequently, the significance of the claimed contribution cannot currently be evaluated.

major comments (2)
  1. [Full Text (body)] The central claims in the abstract have no evidentiary basis in the manuscript body. The body is an unrelated paper on quantum cloud orchestration (QFOR), with different title, authors, and subject matter. It contains no localized trigonometric polynomial kernels, no SSO construction, no FFT filtering step, no piecewise linear regression, and no experiments on exponential models or chirps. Because the submitted text does not include the claimed methods, the manuscript cannot support the abstract's assertions. This is a load-bearing defect: the claimed contributions are absent from the submitted work.
  2. [Abstract] The abstract makes specific empirical claims: the univariate method "outperforms MUSIC and ESPRIT under low signal-to-noise ratios," and the chirp separator "recovers intersecting and discontinuous chirps at SNR levels as low as -30 dB." These are quantitative performance claims that require experimental comparison and supporting results. No tables, figures, error metrics, or simulation descriptions are present anywhere in the manuscript. The abstract alone is not evidence, so these claims are unsupported.
minor comments (2)
  1. [Title/Author metadata] The title and author list of the abstract do not match the title and author list of the full text. This suggests a mismatch or submission error that should be corrected before any further review.
  2. [References] The reference list in the full text concerns quantum computing and cloud scheduling; none of the cited works relate to signal processing, exponential models, MUSIC, ESPRIT, or chirp separation. No relevant background or prior-work comparison is provided for the claimed methods.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity found: the abstract and the attached full text are different works, so there is no derivation chain to be circular.

full rationale

The manuscript under review consists of an abstract for a signal-processing dissertation on localized kernel methods and a full text that is an unrelated quantum cloud orchestration paper (QFOR). Because the full text contains none of the methods, equations, experiments, or comparisons promised in the abstract, there is no derivation chain in which a prediction is defined in terms of an input, a fitted parameter is renamed as a prediction, a self-citation carries a load-bearing premise, or a known result is repackaged. The abstract's claims about outperforming MUSIC/ESPRIT or recovering chirps at -30 dB are unsupported by the attached text, but unsupportedness is a soundness or integrity problem, not circularity. Under the circularity rubric, which requires exhibiting a specific reduction of a claimed result to its own inputs or to a self-citation chain, no such step can be identified. Therefore the appropriate score is 0, with no circular steps.

Assumptions & free parameters 0 free parameters · 1 assumptions · 0 invented entities

Because the manuscript body is a different paper, no parameters, axioms, or entities for the claimed signal processing methods can be audited. The only assumption that matters is the content-matching premise, which fails.

assumptions (1)
  • ad hoc to paper The attached full text corresponds to the abstract's described methods
    This premise is required to evaluate the abstract's signal processing claims, but the full text is an unrelated quantum orchestration paper, so the premise is false.

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Cite this review

Pith. "Pith review of Localized Kernel Methods for Signal Processing." pith.science (2026). https://pith.science/paper/THRPKIBI

@misc{pith2026250804978,
  author       = {Pith},
  title        = {Pith review of: Localized Kernel Methods for Signal Processing},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/THRPKIBI}},
  note         = {Machine review of arXiv:2508.04978}
}
read the original abstract

This dissertation presents two signal processing methods using specially designed localized kernels for parameter recovery under noisy condition. The first method addresses the estimation of frequencies and amplitudes in multidimensional exponential models. It utilizes localized trigonometric polynomial kernels to detect the multivariate frequencies, followed by a more detailed parameter estimation. We compare our method with MUSIC and ESPRIT, which are classical subspace-based algorithms widely used for estimating the parameters of exponential signals. In the univariate case, the method outperforms MUSIC and ESPRIT under low signal-to-noise ratios. For the multivariate case, we develop a coordinate-wise projection and registration approach that achieves high recovery accuracy using significantly fewer samples than other methods. The second method focuses on separating linear chirp components from time-localized signal segments. A variant of the Signal Separation Operator (SSO) is constructed using a localized kernel. Instantaneous frequency estimates are obtained via FFT-based filtering, then clustered and fitted with piecewise linear regression. The method operates without prior knowledge of the number of components and is shown to recover intersecting and discontinuous chirps at SNR levels as low as -30 dB. Both methods share an idea based on localized kernels and efficient FFT-based implementation, and neither requires subspace decomposition or sparsity regularization. Experimental results confirm the robustness and tractability of the proposed approaches across a range of simulated data conditions. Potential extensions include application to nonlinear chirps, adaptive kernel design, and signal classification using extracted features.

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Works this paper leans on

43 extracted references · 16 canonical work pages

  1. [1]

    Humble, Ryan Landfield, Ketan Maheshwari, Sarp Oral, Michael A

    Thomas Beck, Alessandro Baroni, Ryan Bennink, Gilles Buchs, Eduardo Antonio Coello Pérez, Markus Eisenbach, Rafael Ferreira Da Silva, Muralikrishnan Gopalakrishnan Meena, Kalyan Gottiparthi, Peter Groszkowski, Travis S. Humble, Ryan Landfield, Ketan Maheshwari, Sarp Oral, Michael A. Sandoval, Amir Shehata, In-Saeng Suh, and Christopher Zimmer. 2024. Integ...

    doi:10.1016/j.future 2024

  2. [2]

    Jacob Biamonte, Peter Wittek, Nicola Pancotti, Patrick Rebentrost, Nathan Wiebe, and Seth Lloyd. 2017. Quantum machine learning. Nature549, 7671 (Sept. 2017), 195–202. doi:10.1038/nature23474

    doi:10.1038/nature23474 2017

  3. [3]

    Dario Bruneo. 2014. A stochastic model to investigate data center performance and QoS in IaaS cloud computing systems. IEEETransactionson ParallelandDistributedSystems25, 3 (March 2014), 560–569. doi:10.1109/tpds.2013.67

    work page doi:10.1109/tpds.2013.67 2014

  4. [4]

    Jwo-Sy Chen, Erik Nielsen, Matthew Ebert, Volkan Inlek, Kenneth Wright, Vandiver Chaplin, Andrii Maksymov, Eduardo Páez, Amrit Poudel, Peter Maunz, and John Gamble. 2023. Benchmarking a trapped-ion quantum computer with 29 algorithmic qubits. http://arxiv.org/abs/2308.05071 arXiv:2308.05071 [quant-ph]

    arXiv 2023

  5. [5]

    Zheyi Chen, Jia Hu, Geyong Min, Chunbo Luo, and Tarek El-Ghazawi. 2022. Adaptive and Efficient Resource Allocation in Cloud Datacenters Using Actor-Critic Deep Reinforcement Learning. IEEETransactionsonParallelandDistributedSystems33, 8 (Aug. 2022), 1911–1923. doi:10.1109/TPDS. 2021.3132422

    arXiv 2022

  6. [6]

    Bishop, Steven Heidel, Colm A

    Andrew Cross, Ali Javadi-Abhari, Thomas Alexander, Niel De Beaudrap, Lev S. Bishop, Steven Heidel, Colm A. Ryan, Prasahnt Sivarajah, John Smolin, Jay M. Gambetta, and Blake R. Johnson. 2022. OpenQASM 3: A Broader and Deeper Quantum Assembly Language. ACMTransactionson QuantumComputing3, 3 (Sept. 2022), 1–50. doi:10.1145/3505636

    doi:10.1145/3505636 2022

  7. [7]

    Cross, Lev S

    Andrew W. Cross, Lev S. Bishop, Sarah Sheldon, Paul D. Nation, and Jay M. Gambetta. 2019. Validating quantum computers using randomized model circuits. PhysicalReviewA100, 3 (Sept. 2019), 032328. doi:10.1103/PhysRevA.100.032328

    doi:10.1103/physreva.100.032328 2019

  8. [8]

    Jones, Sebastien Cabaniols, and David Brayford

    Aniello Esposito, Jessica R. Jones, Sebastien Cabaniols, and David Brayford. 2023. A Hybrid Classical-Quantum HPC Workload. In 2023IEEE InternationalConferenceonQuantumComputingandEngineering↼QCE↽. IEEE, Bellevue, WA, USA, 117–121. doi:10.1109/QCE57702.2023.10194

    arXiv 2023

Show all 43 references
  1. [9]

    Papka, and Zhiling Lan

    Yuping Fan, Boyang Li, Dustin Favorite, Naunidh Singh, Taylor Childers, Paul Rich, William Allcock, Michael E. Papka, and Zhiling Lan. 2022. DRAS: Deep Reinforcement Learning for Cluster Scheduling in High Performance Computing.IEEETransactionsonParallelandDistributedSystems 3...

    2022

  2. [10]

    Emmanouil Giortamis, Francisco Romão, Nathaniel Tornow, Dmitry Lugovoy, and Pramod Bhatotia. 2024. Orchestrating Quantum Cloud Environments with Qonductor. http://arxiv.org/abs/2408.04312 arXiv:2408.04312 [quant-ph]

    2024

  3. [11]

    Mohammad Goudarzi, Marimuthu S Palaniswami, and Rajkumar Buyya. 2021. A Distributed Deep Reinforcement Learning Technique for Application Placement in Edge and Fog Computing Environments. IEEETransactionsonMobileComputing(2021), 1–1. doi:10.1109/TMC.2021.3123165

    2021

  4. [12]

    Matteo Hessel, Joseph Modayil, Hado van Hasselt, Tom Schaul, Georg Ostrovski, Will Dabney, Dan Horgan, Bilal Piot, Mohammad Azar, and David Silver. 2017. Rainbow: Combining Improvements in Deep Reinforcement Learning. http://arxiv.org/abs/1710.02298 arXiv:1710.02298 [cs]

    2017 arXiv

  5. [13]

    Mohammad Kordzanganeh, Markus Buchberger, Basil Kyriacou, Maxim Povolotskii, Wilhelm Fischer, Andrii Kurkin, Wilfrid Somogyi, Asel Sagingalieva, Markus Pflitsch, and Alexey Melnikov. 2023. Benchmarking Simulated and Physical Quantum Processing Units Using Quantum and Hybrid Al...

    2023 doi

  6. [14]

    Arthur Kurlej, Sam Alterman, and Kevin Obenland. 2024. Performance of algorithms for emerging ion-trap quantum hardware. FutureGeneration ComputerSystems160 (Nov. 2024), 654–665. doi:10.1016/j.future.2024.06.005

    2024 doi

  7. [15]

    Jinyang Li, Yuhong Song, Yipei Liu, Jianli Pan, Lei Yang, Travis Humble, and Weiwen Jiang. 2025. QuSplit: Achieving Both High Fidelity and Throughput via Job Splitting on Noisy Quantum Computers. doi:10.48550/arXiv.2501.12492 arXiv:2501.12492 [quant-ph]

    2025 doi arXiv

  8. [16]

    Tingting Li and Ziming Zhao. 2024. Moirai: optimizing quantum serverless function orchestration via device allocation and circuit deployment. In 2024IEEEInternationalConferenceonWebServices↼ICWS↽. 707–717. doi:10.1109/ICWS62655.2024.00090

    2024

  9. [17]

    Eric Liang, Richard Liaw, Philipp Moritz, Robert Nishihara, Roy Fox, Ken Goldberg, Joseph E Gonzalez, Michael I Jordan, and Ion Stoica. 2018. RLlib: abstractions for distributed reinforcement learning. InProceedingsofthe35thInternationalConferenceonMachineLearning, Vol. 80. PM...

    2018

  10. [18]

    Gonzalez, and Ion Stoica

    Richard Liaw, Eric Liang, Robert Nishihara, Philipp Moritz, Joseph E. Gonzalez, and Ion Stoica. 2018. Tune: a research platform for distributed model selection and training. InICML2018AutoMLWorkshop. PMLR, Stockholm, Sweden. doi:10.48550/arXiv.1807.05118 arXiv:1807.05118 [cs]

    2018 doi arXiv

  11. [19]

    Lotshaw, Thien Nguyen, Anthony Santana, Alexander McCaskey, Rebekah Herrman, James Ostrowski, George Siopsis, and Travis S

    Phillip C. Lotshaw, Thien Nguyen, Anthony Santana, Alexander McCaskey, Rebekah Herrman, James Ostrowski, George Siopsis, and Travis S. Humble

  12. [20]

    Waylon Luo, Jiapeng Zhao, Tong Zhan, and Qiang Guan. 2025. Adaptive Job Scheduling in Quantum Clouds Using Reinforcement Learning. doi:10.48550/arXiv.2506.10889 arXiv:2506.10889 [cs]. 20 H. Nguyen, M. Usman, and R. Buyya

    2025 doi arXiv

  13. [21]

    Nikolaj Moll, Panagiotis Barkoutsos, Lev S Bishop, Jerry M Chow, Andrew Cross, Daniel J Egger, Stefan Filipp, Andreas Fuhrer, Jay M Gambetta, Marc Ganzhorn, Abhinav Kandala, Antonio Mezzacapo, Peter Müller, Walter Riess, Gian Salis, John Smolin, Ivano Tavernelli, and Kristan Temme

  14. [22]

    Nguyen, Prabhakar Krishnan, Dilip Krishnaswamy, Muhammad Usman, and Rajkumar Buyya

    Hoa T. Nguyen, Prabhakar Krishnan, Dilip Krishnaswamy, Muhammad Usman, and Rajkumar Buyya. 2024. Quantum Cloud Computing: A Review, Open Problems, and Future Directions. http://arxiv.org/abs/2404.11420 arXiv:2404.11420 [cs]

    2024 arXiv

  15. [23]

    Nguyen, Muhammad Usman, and Rajkumar Buyya

    Hoa T. Nguyen, Muhammad Usman, and Rajkumar Buyya. 2024. DRLQ: A Deep Reinforcement Learning-based Task Placement for Quantum Cloud Computing. In2024IEEE17thInternationalConferenceonCloudComputing↼CLOUD↽. IEEE, Shenzhen, China, 475–481. doi:10.1109/CLOUD62652. 2024.00060

    2024

  16. [24]

    Nguyen, Muhammad Usman, and Rajkumar Buyya

    Hoa T. Nguyen, Muhammad Usman, and Rajkumar Buyya. 2024. iQuantum: a toolkit for modeling and simulation of quantum computing environments. Software.PracticeandExperience54, 6 (June 2024), 1141–1171. doi:10.1002/spe.3331

    2024 doi

  17. [25]

    Nguyen, Muhammad Usman, and Rajkumar Buyya

    Hoa T. Nguyen, Muhammad Usman, and Rajkumar Buyya. 2024. QFaaS: A Serverless Function-as-a-Service framework for Quantum computing. FutureGenerationComputerSystems154 (May 2024), 281–300. doi:10.1016/j.future.2024.01.018

    2024 doi

  18. [26]

    Nguyen, Muhammad Usman, and Rajkumar Buyya

    Hoa T. Nguyen, Muhammad Usman, and Rajkumar Buyya. 2025. QSimPy: a learning-centric simulation framework for quantum cloud resource management. In QuantumComputing. Morgan Kaufmann, 165–183. doi:10.1016/B978-0-443-29096-1.00012-X

    2025 doi

  19. [27]

    Christophe Piveteau and David Sutter. 2024. Circuit Knitting With Classical Communication. IEEETransactionsonInformationTheory70, 4 (April 2024), 2734–2745. doi:10.1109/TIT.2023.3310797

    2024

  20. [28]

    Christopher Portmann and Renato Renner. 2022. Security in quantum cryptography. ReviewsofModernPhysics94, 2 (June 2022). doi:10.1103/ revmodphys.94.025008

    2022

  21. [29]

    John Preskill. 2018. Quantum computing in the NISQ era and beyond. Quantum2 (Aug. 2018), 79. doi:10.22331/q-2018-08-06-79

    2018 doi

  22. [30]

    Xiaoyu Qiu, Weikun Zhang, Wuhui Chen, and Zibin Zheng. 2021. Distributed and Collective Deep Reinforcement Learning for Computation Offloading: A Practical Perspective.IEEETransactionsonParallelandDistributedSystems32, 5 (May 2021), 1085–1101. doi:10.1109/TPDS.2020.3042599

    2021

  23. [31]

    Nils Quetschlich, Lukas Burgholzer, and Robert Wille. 2023. MQT Bench: Benchmarking Software and Design Automation Tools for Quantum Computing. Quantum7 (July 2023), 1062. doi:10.22331/q-2023-07-20-1062 arXiv:2204.13719 [quant-ph]

    2023 arXiv

  24. [32]

    Smith, Prakash Murali, and Frederic T

    Gokul Subramanian Ravi, Kaitlin N. Smith, Prakash Murali, and Frederic T. Chong. 2021. Adaptive job and resource management for the growing quantum cloud. In 2021 IEEE International Conference on Quantum Computing and Engineering ↼QCE↽. IEEE, Broomfield, CO, USA, 301–312. doi:...

    2021

  25. [33]

    Karpuzcu

    Salonik Resch and Ulya R. Karpuzcu. 2022. Benchmarking Quantum Computers and the Impact of Quantum Noise. Comput▷Surveys54, 7 (2022). doi:10.1145/3464420 arXiv: 1912.00546

    2022 arXiv

  26. [34]

    Nishant Saurabh, Shantenu Jha, and Andre Luckow. 2023. A Conceptual Architecture for a Quantum-HPC Middleware. In2023IEEEInternational ConferenceonQuantumSoftware↼QSW↽. IEEE, Chicago, IL, USA, 116–127. doi:10.1109/QSW59989.2023.00023

    2023

  27. [35]

    John Schulman, Philipp Moritz, Sergey Levine, Michael Jordan, and Pieter Abbeel. 2018. High-dimensional continuous control using generalized advantage estimation. doi:10.48550/arXiv.1506.02438 arXiv:1506.02438 [cs]

    2018 doi arXiv

  28. [36]

    John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. 2017. Proximal Policy Optimization Algorithms. http://arxiv.org/ abs/1707.06347 arXiv:1707.06347 [cs]

    2017 arXiv

  29. [37]

    Gambetta, and Blake R

    Andrew Wack, Hanhee Paik, Ali Javadi-Abhari, Petar Jurcevic, Ismael Faro, Jay M. Gambetta, and Blake R. Johnson. 2021. Quality, Speed, and Scale: three key attributes to measure the performance of near-term quantum computers. http://arxiv.org/abs/2110.14108 arXiv:2110.14108

    2021 arXiv

  30. [38]

    Deliang Yi, Xin Zhou, Yonggang Wen, and Rui Tan. 2020. Efficient Compute-Intensive Job Allocation in Data Centers via Deep Reinforcement Learning. IEEETransactionsonParallelandDistributedSystems31, 6 (June 2020), 1474–1485. doi:10.1109/TPDS.2020.2968427

    2020

  31. [39]

    Kamal, and Zhengyuan Zhu

    Min Sang Yoon, Ahmed E. Kamal, and Zhengyuan Zhu. 2017. Adaptive data center activation with user request prediction. ComputerNetworks122 (July 2017), 191–204. doi:10.1016/j.comnet.2017.04.047

    2017 doi

  32. [40]

    Shaojun Zhang, Chen Wang, and Albert Y. Zomaya. 2022. Robustness Analysis and Enhancement of Deep Reinforcement Learning-based Schedulers. IEEETransactionsonParallelandDistributedSystems(2022), 1–12. doi:10.1109/TPDS.2022.3218649

    2022

  33. [41]

    Maximillian Zinner, Florian Dahlhausen, Philip Boehme, Jan Ehlers, Linn Bieske, and Leonard Fehring. 2021. Quantum computing’s potential for drug discovery: Early stage industry dynamics. DrugDiscoveryToday26, 7 (July 2021), 1680–1688. doi:10.1016/j.drudis.2021.06.003

    2021 doi

  34. [2018]

    QuantumScienceandTechnology3, 3 (July 2018), 030503

    Quantum optimization using variational algorithms on near-term quantum devices. QuantumScienceandTechnology3, 3 (July 2018), 030503. doi:10.1088/2058-9565/aab822

    2018 doi

  35. [2022]

    doi:10.1038/s41598-022-14767-w

    Scaling quantum approximate optimization on near-term hardware.ScientiσcReports12, 1 (July 2022), 12388. doi:10.1038/s41598-022-14767-w

    2022 doi

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