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REVIEW 3 major objections 6 minor 1 cited by

Quantum Computing and AI: Perspectives on Advanced Automation in Science and Engineering

T0 review · 3 major / 6 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read This perspective argues that Quantum CAE—using quantum algorithms for simulation, optimization, and machine learning inside engineering design—can deliver tangible benefits in discrete-variable design problems at current small scales.

desk verdict A clearly written perspective whose only genuinely new contribution is the label 'Quantum CAE'; the central 'tangible benefits' claim rests on self-cited case studies with no classical baseline. read the letter →

arxiv 2505.10012 v1 pith:5SQVEE4D submitted 2025-05-15 quant-ph cs.AI

Tadashi Kadowaki This is my paper · ORCID
classification quant-phcs.AI
keywords QuantumCAEannealingblack-boxoptimizationQUBOscientificautomationdesigndigitalscientistsAI4Science
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 argues that scientific discovery and engineering design run on the same three-step loop—generate a hypothesis, test it by experiment or simulation, fold the findings into knowledge—and that quantum computing can accelerate each step. It introduces Quantum CAE as the umbrella term for this insertion, with quantum optimization as the most mature ingredient. The central practical claim is that current small-scale quantum annealers and Ising solvers, embedded in black-box optimization methods, already produce useful solutions to discrete-variable design problems that are NP-hard. The paper presents two industrial-style case studies as evidence and maps this capability onto Level 3 scientific automation, meaning full autonomy under well-defined conditions with humans supervising.

What carries the argument

The load-bearing machinery is the black-box optimization cycle woven into CAE: an optimizer proposes candidate designs (hypothesis generation), a simulator evaluates them (experiment), and a machine-learning model absorbs the data and improves the next proposal (knowledge integration). Discrete design variables are encoded as QUBO problems, and quantum annealing or Ising solvers perform the optimization step. BOCS (Bayesian Optimization of Combinatorial Structures, a posterior-sampling approach) and FMQA (Factorization Machine Quantum Annealing, a point-estimate approach) are the two instantiations that carry the case studies. The framework-level object, Quantum CAE, names the full integration of quantum simulation, quantum machine learning, and quantum optimization, with the long-term goal of exchanging quantum states directly between tasks.

What would settle it

Run BOCS and FMQA on the same electronic-board or noise-filter design problems with the quantum annealer replaced by a classical optimizer such as simulated annealing, keeping the surrogate model and evaluation budget identical; if the classical version matches or beats the quantum-in-the-loop version on solution quality and wall-clock time, the paper's claim of tangible near-term benefit is not supported.

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Extended reading notes

Core claim

The central claim is that Quantum CAE can deliver tangible benefits at small scale for discrete-variable engineering design problems by treating optimization as the hypothesis-generation step in an automated design loop. The paper identifies quantum annealing and Ising solvers as practical tools for this step, combined with learned surrogate models such as factorization machines or posterior-sampling models. Two case studies—mounting-point placement on an electronic control board and printed-circuit-pattern design for a noise filter—are offered as demonstrations that the approach finds feasible, near-optimal designs within limited iterations, with the noise-filter solution resembling a topology-optimization result. The paper further claims that this level of integration corresponds to Level 3 automation, and that the remaining path to Levels 4 and 5 requires teams of specialized AI agents, including agents that design quantum circuits for QUBO problems. On the simulation side, it argues quantum algorithms for equations like radiative transfer are emerging, but the main near-term evidence is optimization.

Load-bearing premise

The claimed practical benefit rests on the assumption that quantum annealing embedded in black-box optimization finds design-relevant solutions at least as efficiently as classical optimization; the cited case studies do not include classical baseline comparisons or time-to-solution metrics, so this is asserted rather than demonstrated here.

Editorial extensions

If this is right

  • Discrete-variable design problems that are NP-hard can in principle be tackled in current engineering practice with quantum annealers or Ising solvers inside a black-box loop, without waiting for fault-tolerant hardware.
  • Reaching Level 3 scientific automation—full autonomy under well-defined conditions—is presented as feasible now for computationally closed fields such as structural and circuit design.
  • If larger annealers arrive, the bottleneck shifts from optimization to building the prediction model, so classical algorithm development remains a priority in the short term.
  • Higher automation levels will depend on specialized AI agents that can design and validate quantum circuits, such as generators for QUBO-solving circuits, rather than on a single general-purpose algorithm.
  • Successful Quantum CAE would reduce prototype and development cycles by replacing physical experiments with quantum-assisted simulation and automated design refinement.

Reading between the lines

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

  • The paper leaves the size of the benefit unquantified: its two case studies do not report classical baselines or time-to-solution, so the fair reading is that the claim is feasibility, not demonstrated superiority; a direct comparison against classical heuristics on the same problems would isolate the quantum contribution.
  • A testable extension would run FMQA and BOCS on the same design problems with the quantum step replaced by simulated annealing, holding the surrogate model fixed; matching performance would suggest the benefit comes from the black-box loop rather than the quantum sampler.
  • The Quantum CAE framing implies that quantum advantage may show up first as better end-to-end design outcomes rather than as a stand-alone algorithmic speedup, which is a different benchmark from the one usually used in quantum computing studies.
  • The same loop could transfer to other discrete domains, such as pharmaceutical candidate selection or materials design, wherever a cheap simulator can evaluate a QUBO-encoded proposal.
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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 / 6 minor

Summary. This perspective paper introduces "Quantum CAE," a framework that applies quantum algorithms for simulation, optimization, and machine learning within computer-aided engineering and scientific automation. The author draws parallels between scientific discovery workflows and CAE design automation, argues that current technology enables Level 3 automation under well-defined conditions, and illustrates the framework with two case studies of discrete-variable design optimization using BOCS and FMQA with quantum annealers and Ising solvers (automotive mounting-point placement and noise-filter design). The paper then discusses AI agents that autonomously generate quantum circuits and sketches a roadmap toward higher automation levels. The central empirical claim, stated in the abstract and Section VI, is that Quantum CAE can deliver tangible benefits at current small scales, supported primarily by the two case studies and references to the author's own prior work.

Significance. If the practical-benefit claim were convincingly demonstrated, this paper would constitute a valuable synthesis for near-term quantum computing in industrial design, giving engineers a concrete framework for applying quantum optimization. The conceptual mapping between scientific automation and CAE is clear and potentially useful, and the paper usefully assembles a broad literature on quantum-assisted design, including work by others. However, the manuscript is a perspective that presents no new technical results, and its load-bearing empirical claims rest on a small set of self-cited, non-benchmarked case studies. The acknowledged exponential worst-case overhead for classical data I/O also sits in tension with the paper's feasibility tone. The significance is therefore primarily programmatic rather than evidentiary; the paper could serve as a roadmap if the claims are appropriately reframed as hypotheses and the limitations are more fully acknowledged.

major comments (3)
  1. [Section IV, case studies [31] and [32], and Figure 3] The central claim that BOCS and FMQA 'efficiently identify' good solutions and that Quantum CAE delivers 'tangible benefits even at small scales' (Section VI) is not supported by the evidence presented. The two case studies are described only qualitatively: no comparison to classical optimization baselines (e.g., simulated annealing, tabu search, or classical Bayesian optimization), no time-to-solution or wall-clock data, and no optimality gaps are reported. Figure 3 only shows a cost function decreasing with the number of searches, which any reasonable optimization method would exhibit; it does not demonstrate any quantum-specific advantage. Because the abstract and summary assert the benefit as fact rather than as a hypothesis, this missing baseline is load-bearing for the paper's main claim.
  2. [Section III, paragraph on challenges] The paper acknowledges that encoding classical data into quantum states and extracting information back has exponential computational cost in the worst case, yet the abstract and Section VI claim that current technology enables practical, tangible benefits. The manuscript does not reconcile this tension for the specific discrete-variable problems in the case studies, nor does it explain how the black-box optimization loop avoids or mitigates this overhead. Without such an analysis, the practical-feasibility claim is not internally consistent, and the paper should either quantify the overhead for the presented problems or soften the claim to a proposal that still requires this issue to be addressed.
  3. [General, evidence base] The evidence for Quantum CAE's value is heavily dominated by prior papers authored or coauthored by the current author (refs [26], [31], [32], [46], [49], [53], and [55]). These works are cited as demonstrations, but they are not reproduced, not independently validated, and not benchmarked against classical solvers in this manuscript. For a perspective, citing one's own prior work is acceptable if the claims are framed as promising directions with appropriate caveats; however, the current text states the outcomes as established results (e.g., 'These case studies highlight the effectiveness of quantum annealing as well as Ising solvers in enhancing automation frameworks'). The author should reframe these as preliminary or illustrative and explicitly identify the need for independent, systematic benchmarks.
minor comments (6)
  1. [Section I, paragraph on Paul Erdős] The name is typeset as 'Erd˝ os,' which is likely a character-encoding error; it should be 'Erdős'.
  2. [References, ref [12]] Reference [12] contains an anomalous author entry 'T. Google, and A. I. Language,' which appears to be a corrupted or misformatted citation and should be corrected.
  3. [Acknowledgments] The name 'Al´an Aspuru-Guzuk' should be spelled 'Alán Aspuru-Guzik'.
  4. [Figure 3 caption] The y-axis is labeled 'y' while the caption refers to the 'cost function'; the notation should be unified, and the axes should be described consistently.
  5. [Section IV, opening paragraph] The sentence 'These methods rely on discrete design variables and evaluation approaches, such as simulations, to assess product characteristics' is redundant with the preceding discussion and could be removed or rewritten for clarity.
  6. [Section II, JST automation levels] The six-level JST taxonomy is referenced but not fully defined; readers unfamiliar with the original report may not understand what distinguishes Level 3 from Levels 4 and 5. A brief definition or a pointer to the original report would improve accessibility.

Circularity Check

1 steps flagged · score 6.0 of 10

The 'tangible benefits' claim for Quantum CAE rests on self-authored case studies with no independent baseline, so the central empirical assertion reduces to a self-citation chain.

  1. self citation load bearing [Section VI (Summary and Future Perspectives), supported by Section IV case studies [31,32]]
    "Specific examples were presented illustrating how Quantum CAE can deliver tangible benefits even at small scales. ... These case studies highlight the effectiveness of quantum annealing as well as Ising solvers in enhancing automation frameworks in both manufacturing and scientific fields."

    The paper's central empirical claim is not derived from new data in this manuscript; the 'specific examples' are prior papers [31] (Matsumori, Taki, Kadowaki) and [32] (Okada, ..., Kadowaki), both co-authored by the present author. No classical solver comparison, time-to-solution, optimality gap, or independent reproduction is reported here, so the assertion that Quantum CAE yields 'tangible benefits' reduces to trusting the author's own earlier reports. The cited work may be valuable, but it is not independent evidence in this context, making the self-citation load-bearing for the headline claim.

full rationale

The manuscript is a perspective rather than a derivation, so there are no equations whose outputs equal inputs. The central empirical assertion, however, is that Quantum CAE 'can deliver tangible benefits even at small scales' (Section VI). The only support offered for this assertion in the text is the Section IV pair of case studies from Matsumori et al. [31] and Okada et al. [32], both of which list the present author as a co-author. The manuscript does not report wall-clock times, comparisons with classical solvers, optimality gaps, or any independent verification; Figure 3 merely shows a decreasing cost curve with more searches, which does not establish quantum advantage. Therefore the headline claim is load-bearing on a self-citation chain rather than on evidence external to the author's own prior work. This is a genuine circularity concern under the self-citation rule, though it is a perspective paper and the cited papers may themselves be sound. No other pattern (fitted input called prediction, uniqueness imported, ansatz smuggling, or renaming) is present. Score 6: the central claim reduces to the author's own prior reports, with partial independent content in the framework discussion and level-of-automation taxonomy.

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

The paper introduces no new equations or fitted parameters. Its central contribution is a conceptual framework whose axioms are (a) the CAE mapping and (b) the practical efficacy of quantum-annealing-based black-box optimization. Both rest on the author's prior work, which is cited rather than reproduced.

assumptions (2)
  • domain assumption Scientific automation maps one-to-one onto CAE design automation: optimization corresponds to hypothesis generation, simulation to experimentation, and machine learning to knowledge integration.
    Section II and Figure 1 assert this mapping; it is the conceptual basis for Quantum CAE but is not formally justified.
  • ad hoc to paper Current quantum annealing and Ising solvers, used within BOCS/FMQA, can efficiently solve practical discrete design optimization problems.
    Section IV relies on the author's prior case studies (refs [31], [32]) to support this premise; no independent data or baseline comparison is included in this paper.

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Cite this review

Pith. "Pith review of Quantum Computing and AI: Perspectives on Advanced Automation in Science and Engineering." pith.science (2026). https://pith.science/paper/5SQVEE4D

@misc{pith2026250510012,
  author       = {Pith},
  title        = {Pith review of: Quantum Computing and AI: Perspectives on Advanced Automation in Science and Engineering},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/5SQVEE4D}},
  note         = {Machine review of arXiv:2505.10012}
}
read the original abstract

Recent advances in artificial intelligence (AI) and quantum computing are accelerating automation in scientific and engineering processes, fundamentally reshaping research methodologies. This perspective highlights parallels between scientific automation and established Computer-Aided Engineering (CAE) practices, introducing Quantum CAE as a framework that leverages quantum algorithms for simulation, optimization, and machine learning within engineering design. Practical implementations of Quantum CAE are illustrated through case studies for combinatorial optimization problems. Further discussions include advancements toward higher automation levels, highlighting the critical role of specialized AI agents proficient in quantum algorithm design. The integration of quantum computing with AI raises significant questions about the collaborative dynamics among human scientists and engineers, AI systems, and quantum computational resources, underscoring a transformative future for automated discovery and innovation.

Figures

Figures reproduced from arXiv: 2505.10012 by the authors.

Figure 1
Figure 1. FIG. 1. Process of automation in science and product de [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. FIG. 2. Optimization results for a noise filter. Similar solu [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. FIG. 3. Relationship between the number of data acquisi [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: FIG. 4. Encoder-decoder model for generating quantum cir [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]

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Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Fourier space readout method for efficiently recovering functions encoded in quantum states

    quant-ph 2025-07 conditional novelty 6.0 of 10

    Measuring a quantum state's Fourier coefficients, rather than its grid-point amplitudes, recovers smooth amplitude-encoded functions with shot count independent of grid size, preserving quantum speedups for CAE readout.

Reference graph

Works this paper leans on

60 extracted references · 47 canonical work pages · cited by 1 Pith paper

  1. [26]

    A. M. Dalzell, S. McArdle, M. Berta, P. Bienias, C.-F. Chen, A. Gily´ en, C. T. Hann, M. J. Kastoryano, E. T. Khabiboulline, A. Kubica, G. Salton, S. Wang, and F. G. S. L. Brand˜ ao,Quantum Algorithms: A Survey of Appli- cations and End-to-end Complexities (Cambridge Uni- versity Press, 2025)

    work page 2025

  2. [31]

    Marsh and J

    S. Marsh and J. B. Wang, Combinatorial optimization via highly efficient quantum walks, Physical Review Re- search 2, 023302 (2020)

    work page 2020

  3. [32]

    Baptista and M

    R. Baptista and M. Poloczek, Bayesian optimization of combinatorial structures, 35th International Conference on Machine Learning, ICML 2018 2, 782 (2018)

    work page 2018

  4. [46]

    Y. Dou, D. Jiao, J. Yan, and J. Zhu, Method for ana- lyzing bit error rates (bers) of nonlinear circuits and sys- tems for high-performance signaling, IEEE Transactions on Microwave Theory and Techniques 70, 732 (2022)

    work page 2022

  5. [49]

    Kadowaki and M

    T. Kadowaki and M. Ambai, Lossy compression of matri- ces by black box optimisation of mixed integer nonlinear programming, Scientific Reports 12, 1 (2022)

    work page 2022

  6. [53]

    J. P. Liu, H. Øie Kolden, H. K. Krovi, N. F. Loureiro, K. Trivisa, and A. M. Childs, Efficient quantum algo- rithm for dissipative nonlinear differential equations, Pro- ceedings of the National Academy of Sciences of the United States of America 118, e2026805118 (2021)

    work page 2021

  7. [55]

    Layden, G

    D. Layden, G. Mazzola, R. V. Mishmash, M. Motta, P. Wocjan, J. S. Kim, and S. Sheldon, Quantum- enhanced markov chain monte carlo, Nature 2023 619:7969 619, 282 (2023)

    work page 2023

  8. [1]

    Rosenblatt, The perceptron: A probabilistic model for information storage and organization in the brain., Psychological Review 65, 386 (1958)

    F. Rosenblatt, The perceptron: A probabilistic model for information storage and organization in the brain., Psychological Review 65, 386 (1958)

    work page 1958

Show all 60 references
  1. [2]

    D. E. Rumelhart, G. E. Hinton, and R. J. Williams, Learning representations by back-propagating errors, Na- ture 323, 533 (1986)

    1986

  2. [3]

    Cortes and V

    C. Cortes and V. Vapnik, Support-vector networks, Ma- chine Learning 20, 273 (1995)

    1995

  3. [4]

    J. R. Quinlan, Induction of decision trees, Machine Learning 1, 81 (1986)

    1986

  4. [5]

    digital scientists,

    will require tackling complex, uncontrollable societal challenges and investigating scientific frontiers beyond current human cognitive capacities. Quantum comput- ing could become indispensable, propelled by Quantum CAE-derived knowledge and technologies. IV. QUANTUM CAE FOR ...

  5. [6]

    J. H. Friedman, Greedy function approximation: A gra- dient boosting machine, The Annals of Statistics29, 1189 (2001)

    2001

  6. [7]

    Lecun, Y

    Y. Lecun, Y. Bengio, and G. Hinton, Deep learning, Na- ture 521, 436 (2015)

    2015

  7. [8]

    Breiman, Random forests, Machine Learning 45, 5 (2001)

    L. Breiman, Random forests, Machine Learning 45, 5 (2001)

    2001

  8. [9]

    Hochreiter and J

    S. Hochreiter and J. Schmidhuber, Long short-term mem- ory, Neural Computation 9, 1735 (1997)

    1997

  9. [10]

    Vaswani, G

    A. Vaswani, G. Brain, N. Shazeer, N. Parmar, J. Uszko- reit, L. Jones, A. N. Gomez, Lukasz Kaiser, and I. Polo- sukhin, Attention is all you need, Advances in Neural Information Processing Systems 30 (2017)

    2017

  10. [11]

    Krizhevsky, I

    A. Krizhevsky, I. Sutskever, and G. E. Hinton, Imagenet classification with deep convolutional neural networks, Advances in Neural Information Processing Systems 25 (2012)

    2012

  11. [12]

    Devlin, M.-W

    J. Devlin, M.-W. Chang, K. Lee, K. T. Google, and A. I. Language, Bert: Pre-training of deep bidirectional trans- formers for language understanding, Proceedings of the 2019 Conference of the North , 4171 (2019)

    2019

  12. [13]

    Silver, A

    D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. V. D. Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, S. Dieleman, D. Grewe, J. Nham, N. Kalchbrenner, I. Sutskever, T. Lillicrap, M. Leach, K. Kavukcuoglu, T. Graepel, and D. Hassabis, Maste...

    2016

  13. [14]

    T. B. Brown, B. Mann, N. Ryder, M. Subbiah, J. Ka- plan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sas- try, A. Askell, S. Agarwal, A. Herbert-Voss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D. M. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S. Gray,...

    2020

  14. [15]

    Center for Research and Development Strategy, Artifi- cial Intelligence and Science - Toward discovery and un- derstanding by AI-driven science - (Japan Science and Technology Agency, 2021)

    2021

  15. [16]

    National Academies of Sciences, Engineering, and Medicine, Automated Research Workflows for Accelerated Discovery (National Academies Press, 2022)

    2022

  16. [17]

    P. R. Wurman, S. Barrett, K. Kawamoto, J. Mac- Glashan, K. Subramanian, T. J. Walsh, R. Capobianco, A. Devlic, F. Eckert, F. Fuchs, L. Gilpin, P. Khandel- wal, V. Kompella, H. C. Lin, P. MacAlpine, D. Oller, T. Seno, C. Sherstan, M. D. Thomure, H. Aghabozorgi, L. Barrett, R. D...

    2022

  17. [18]

    Tezuka, Astro boy: The greatest robot on earth, Shonen (1964)

    O. Tezuka, Astro boy: The greatest robot on earth, Shonen (1964)

    1964

  18. [19]

    Jumper, R

    J. Jumper, R. Evans, A. Pritzel, T. Green, M. Fig- urnov, O. Ronneberger, K. Tunyasuvunakool, R. Bates, A. ˇZ´ ıdek, A. Potapenko, A. Bridgland, C. Meyer, S. A. Kohl, A. J. Ballard, A. Cowie, B. Romera-Paredes, S. Nikolov, R. Jain, J. Adler, T. Back, S. Petersen, D. Reiman, E....

    2021

  19. [20]

    OECD, Artificial Intelligence in Science: Challenges, Opportunities and the Future of Research (OECD Pub- lishing, 2023). 7

    2023

  20. [21]

    R. D. King, K. E. Whelan, F. M. Jones, P. G. Reiser, C. H. Bryant, S. H. Muggleton, D. B. Kell, and S. G. Oliver, Functional genomic hypothesis generation and experimentation by a robot scientist, Nature 427, 247 (2004)

    2004

  21. [22]

    C. Lu, C. Lu, R. T. Lange, J. Foerster, J. Clune, and D. Ha, The ai scientist: Towards fully automated open- ended scientific discovery (2024), arXiv:2408.06292

    2024 arXiv

  22. [23]

    in simulation, machine learning, and optimization, which together are termed Quantum CAE. Integrat- ing quantum computing into product design automation could significantly reduce development lead times, result- ing in improved productivity, cost-efficiency, and market respons...

  23. [24]

    Hoffman, The Man Who Loved Only Numbers (Hype- rion Books, 1998)

    P. Hoffman, The Man Who Loved Only Numbers (Hype- rion Books, 1998)

    1998

  24. [25]

    H. Y. Huang, M. Broughton, J. Cotler, S. Chen, J. Li, M. Mohseni, H. Neven, R. Babbush, R. Kueng, J. Preskill, and J. R. McClean, Quantum advantage in learning from experiments, Science 376, 1182 (2022)

    2022

  25. [27]

    and quantum walks [28]. Unlike abstract quantum oracle-based algorithms, Quantum CAE emphasizes di- rect integration of concrete physical simulations and eval- uation of product characteristics, creating practical, ef- ficient quantum circuits suitable for real-world applica- ...

  26. [28]

    Tang, A quantum-inspired classical algorithm for rec- ommendation systems, Proceedings of the Annual ACM Symposium on Theory of Computing , 217 (2019)

    E. Tang, A quantum-inspired classical algorithm for rec- ommendation systems, Proceedings of the Annual ACM Symposium on Theory of Computing , 217 (2019)

    2019

  27. [29]

    Kadowaki, Enhancing quantum annealing in digi- tal–analog quantum computing, APL Quantum 1, 26101 (2024)

    T. Kadowaki, Enhancing quantum annealing in digi- tal–analog quantum computing, APL Quantum 1, 26101 (2024)

    2024

  28. [30]

    Gilliam, S

    A. Gilliam, S. Woerner, and C. Gonciulea, Grover adap- tive search for constrained polynomial binary optimiza- tion, Quantum 5, 428 (2021)

    2021

  29. [33]

    Kitai, J

    K. Kitai, J. Guo, S. Ju, S. Tanaka, K. Tsuda, J. Shiomi, and R. Tamura, Designing metamaterials with quantum annealing and factorization machines, Physical Review Research 2, 013319 (2020)

    2020

  30. [34]

    Matsumori, M

    T. Matsumori, M. Taki, and T. Kadowaki, Application of qubo solver using black-box optimization to structural design for resonance avoidance, Scientific Reports 12, 1 (2022)

    2022

  31. [35]

    Okada, H

    A. Okada, H. Yoshida, K. Kidono, T. Matsumori, T. Takeno, and T. Kadowaki, Design optimization of noise filter using quantum annealer, IEEE Access 11, 44343 (2023)

    2023

  32. [36]

    Maruyama, S

    S. Maruyama, S. Yamasaki, K. Nomura, K. Yaji, and K. Fujita, Layout design of components and conduc- tors in noise filter by integrative optimization of struc- tural topology and external variables, Transactions of the Japan Society for Computational Engineering and Science 20...

    2021

  33. [37]

    A. Khan, A. I. Cowen-Rivers, A. Grosnit, D.-G.-X. Deik, P. A. Robert, V. Greiff, E. Smorodina, P. Rawat, R. Akbar, K. Dreczkowski, R. Tutunov, D. Bou-Ammar, J. Wang, A. Storkey, and H. Bou-Ammar, Toward real- world automated antibody design with combinatorial bayesian optimiza...

    2023

  34. [38]

    Tuˇ cs, F

    A. Tuˇ cs, F. Berenger, A. Yumoto, R. Tamura, T. Uzawa, and K. Tsuda, Quantum annealing designs nonhemolytic antimicrobial peptides in a discrete latent space, ACS Medicinal Chemistry Letters 14, 577 (2023)

    2023

  35. [39]

    Z. Mao, Y. Matsuda, R. Tamura, and K. Tsuda, Chem- ical design with gpu-based ising machines, Digital Dis- covery 2, 1098 (2023)

    2023

  36. [40]

    Q. Gao, G. O. Jones, T. Kobayashi, M. Sugawara, H. Yamashita, H. Kawaguchi, S. Tanaka, and N. Ya- mamoto, Quantum-classical computational molecular de- sign of deuterated high-efficiency oled emitters, Intelli- gent Computing 2, 10.34133/icomputing.0037 (2023)

    2023 doi

  37. [41]

    S. Kim, W. Shang, S. Moon, T. Pastega, E. Lee, and T. Luo, High-performance transparent radiative cooler designed by quantum computing, ACS Energy Letters 7, 4134 (2022)

    2022

  38. [42]

    D. Zhu, J. Guo, G. Yu, C. Y. Zhao, H. Wang, S. Ju, and S. Ju, Designing thermal radiation metamaterials via a hybrid adversarial autoencoder and bayesian optimiza- tion, Optics Letters, Vol. 47, Issue 14, pp. 3395-3398 47, 3395 (2022)

    2022

  39. [43]

    Inoue, Y

    T. Inoue, Y. Seki, K. Ishizaki, S. Noda, N. Togawa, and S. Tanaka, Towards optimization of photonic-crystal surface-emitting lasers via quantum annealing, Optics Express, Vol. 30, Issue 24, pp. 43503-43512 30, 43503 (2022)

    2022

  40. [44]

    K. Nawa, T. Suzuki, K. Masuda, S. Tanaka, and Y. Miura, Quantum annealing optimization method for the design of barrier materials in magnetic tunnel junc- tions, Physical Review Applied 20, 024044 (2023)

    2023

  41. [45]

    Maruo, T

    A. Maruo, T. Soeda, and H. Igarashi, Topology optimization of electromagnetic devices using digi- tal annealer, IEEE Transactions on Magnetics 58, 10.1109/TMAG.2022.3184325 (2022)

    2022

  42. [47]

    C. Oh, R. Bondesan, D. Kianfar, R. Ahmed, R. Khu- rana, P. Agarwal, R. Lepert, M. Sriram, and M. Welling, Bayesian optimization for macro placement (2022)

    2022

  43. [48]

    Drouet, S

    V. Drouet, S. Verel, and J. M. Do, Surrogate-assisted asynchronous multiobjective algorithm for nuclear power plant operations, GECCO 2020 - Proceedings of the 2020 Genetic and Evolutionary Computation Conference , 1073 (2020)

    2020

  44. [50]

    Minamoto and Y

    Y. Minamoto and Y. Sakamoto, A black-box op- timization method with polynomial-based ker- nels and quadratic-optimization annealing (2025), arXiv:2501.04225

    2025

  45. [51]

    Budinski, Quantum algorithm for the advec- tion–diffusion equation simulated with the lattice boltz- mann method, Quantum Information Processing 20, 1 8 (2021)

    L. Budinski, Quantum algorithm for the advec- tion–diffusion equation simulated with the lattice boltz- mann method, Quantum Information Processing 20, 1 8 (2021)

    2021

  46. [52]

    Igarashi, T

    A. Igarashi, T. Kadowaki, and S. Kawabata, Quantum al- gorithm for the radiative-transfer equation, Physical Re- view Applied 21, 034010 (2024)

    2024

  47. [54]

    Joseph, Koopman-von neumann approach to quantum simulation of nonlinear classical dynamics, Physical Re- view Research 2, 043102 (2020)

    I. Joseph, Koopman-von neumann approach to quantum simulation of nonlinear classical dynamics, Physical Re- view Research 2, 043102 (2020)

    2020

  48. [56]

    Arai and T

    S. Arai and T. Kadowaki, Quantum annealing enhanced markov-chain monte carlo (2025), arXiv:2502.08060

    2025 arXiv

  49. [57]

    Nakaji, L

    K. Nakaji, L. B. Kristensen, J. A. Campos-Gonzalez- Angulo, M. G. Vakili, H. Huang, M. Bagherimehrab, C. Gorgulla, F. Wong, A. McCaskey, J.-S. Kim, T. Nguyen, P. Rao, and A. Aspuru-Guzik, The gener- ative quantum eigensolver (gqe) and its application for ground state search (2...

    2024

  50. [58]

    Minami, K

    S. Minami, K. Nakaji, Y. Suzuki, A. Aspuru-Guzik, and T. Kadowaki, Generative quantum combinatorial op- timization by means of a novel conditional generative quantum eigensolver (2025), arXiv:2501.16986

    2025 arXiv

  51. [59]

    Sakka, K

    K. Sakka, K. Mitarai, and K. Fujii, Automating quan- tum feature map design via large language models (2025), arXiv:2504.07396

    2025

  52. [60]

    Alexeev, M

    Y. Alexeev, M. H. Farag, T. L. Patti, M. E. Wolf, N. Ares, A. Aspuru-Guzik, S. C. Benjamin, Z. Cai, Z. Chandani, F. Fedele, N. Harrigan, J.-S. Kim, E. Kyoseva, J. G. Li- etz, T. Lubowe, A. McCaskey, R. G. Melko, K. Nakaji, A. Peruzzo, S. Stanwyck, N. M. Tubman, H. Wang, and T....

    2024

Pith tools

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