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REVIEW 4 major objections 5 minor 93 references

The USRA Feynman Quantum Academy: If You Give a Student a Quantum Internship

T0 review · 4 major / 5 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read A review of eight years of the Feynman Quantum Academy argues that graduate internships embedded in a research lab keep over 75% of students in quantum careers and produce papers cited at the same rate as the host group's other work.

desk verdict A transparent, well-contextualized program retrospective whose headline retention figure needs a methods appendix before it can be used as evidence. read the letter →

arxiv 2505.04641 v1 pith:JU5FWO3N submitted 2025-05-05 physics.ed-ph quant-ph

Zoe Gonzalez Izquierdo , Sophie Block , Besart Mujeci , P. Aaron Lott , Davide Venturelli , David Bell This is my paper · ORCID
classification physics.ed-phquant-ph
keywords quantumworkforceinternshipprogramexperientiallearningeducationcareeroutcomescitationimpactFeynmanAcademyNASAAmes
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 reviews eight years of the Feynman Quantum Academy, a graduate-focused internship program run inside a quantum computing research group at NASA Ames, and argues the program is a working model for preparing the quantum workforce. It reports that the program supported 60 internships between 2016 and 2024, that more than 75% of former interns still work in quantum-related roles, and that papers written with intern participation receive citations comparable to the group's papers without interns. A sympathetic reader would take the claim as: structured, research-embedded internships can both advance quantum research and launch durable careers. The paper places this program in the broader landscape of quantum workforce education and compares it with similar initiatives.

What carries the argument

The load-bearing mechanism is the internship structure itself: students join an active research group (QuAIL), work one-on-one with staff research scientists, attend group meetings, and are given access to quantum processors and high-performance computing resources. They are expected to produce code or a publication, often feeding into their doctoral thesis. The program thus functions as an apprenticeship inside a functioning research organization rather than a standalone training course.

What would settle it

An independent career-tracking study of all 60 former interns, using contact information obtained outside the program's own records and achieving a high response rate, that found fewer than half still in quantum-related jobs would refute the 75% retention claim. Similarly, a citation comparison restricted to papers whose intern authorship is verified externally and matched to the group's non-intern papers by year and venue would test the citation-parity claim.

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Extended reading notes

Core claim

The central claim is that the Feynman Quantum Academy has demonstrated a proven model for experiential learning in quantum computing. The evidence offered is longitudinal: 60 internships funded by NASA, NSF, AFRL, DARPA, Fermilab, DLR, and DHS; a cohort that was mostly PhD students (57%) but included undergraduates, master's students, and one associate's student; a current snapshot in which more than three quarters of the alumni remain in quantum-related work; and a citation comparison in which intern-collaboration papers track the same impact as non-intern papers from the same group. The paper also documents that many alumni continued into PhDs or took positions at universities, national labs, startups, and large companies such as Google, Amazon, IBM, JP Morgan, and Boeing.

Load-bearing premise

The retention and citation statistics assume the program's tracking of former interns is complete and unbiased; the paper does not report how this data was collected or how nonrespondents were handled.

Editorial extensions

If this is right

  • Graduate internships embedded in a research lab can feed the quantum workforce: the program reports over 75% of former interns remain in quantum-related positions.
  • Intern-contributed papers receive citations comparable to non-intern papers, suggesting interns produce research of similar visibility rather than peripheral work.
  • A single program funded by multiple agencies shows that mission-oriented funders can pool resources for workforce training.
  • The mix of PhD, master's, undergraduate, and associate's-level interns indicates the model can operate across educational levels.
  • Since many interns continue to PhDs or academic and industry positions, the program functions as a recruiting pipeline for academic and corporate quantum research.

Reading between the lines

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

  • The retention figure's credibility could be tested by other programs publishing comparable longitudinal outcome data; if similar numbers appear, it would strengthen the case that structured lab internships, not selection alone, drive retention.
  • Because the program is embedded in a research lab with pre-existing projects, the model may be most reproducible at institutions that already run mission-driven quantum research, rather than as a standalone training add-on.
  • A testable extension would be comparing intern-paper citation rates against the broader field's citation distribution by year and topic, not just against the host group's other papers.
  • If citation parity holds, program evaluation could shift from counting placements to measuring research output, offering a quantitative template for internship assessment.
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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

4 major / 5 minor

Summary. The paper presents a self-description of the USRA Feynman Quantum Academy, a graduate- and undergraduate-level internship program embedded in the NASA Ames QuAIL group, covering the period 2016–2024. After a broad review of the internship and quantum-workforce literature, the authors describe the program's structure, funding sources, student demographics, and career outcomes, and they compare the citation impact of papers with intern co-authors against papers without intern co-authors. The central quantitative claims are that the program supported 60 student internships, that over 75% of alumni remain in quantum-related work, and that intern-involved papers receive citations comparable to non-intern papers (abstract and Section V; Figures 1–3). The paper also includes a lengthy appendix summarizing the research output produced by the program.

Significance. If the reported outcomes are reliable, the paper would be a useful program-evaluation data point for the quantum-workforce-development literature, a field that largely relies on surveys and curricular proposals rather than longitudinal internship-outcome data. The paper's strengths are that it makes concrete, falsifiable claims (60 internships, >75% retention, citation comparability) and that it attempts to compare intern-involved publications against a non-intern baseline via citation data, an approach rarely seen in physics-education program descriptions. The literature review in Section II is broad and current. However, the evidentiary value of the central claims depends entirely on the unstated data-collection and analysis methodology, and the paper as written does not yet provide the transparency that would make those claims checkable.

major comments (4)
  1. [Section V, Figure 1] The claim that 'over 75% remain in the quantum industry in some capacity' is the paper's headline result, but no methodology is given for how alumni positions were ascertained. The text does not state whether the data come from LinkedIn tracking, alumni surveys, direct contact, or a combination; it does not report a response rate or the treatment of alumni whose status could not be confirmed. If tracking preferentially reaches alumni who stayed in quantum, the 75% figure is biased upward. The authors should provide a data-collection description, a definition of the denominator (unique students vs. internships), and a coding rubric for 'quantum-related work,' including how continuing PhD students and postdocs are classified.
  2. [Section V, paragraph 1] The denominator for the career-outcome percentages is ambiguous. The text states that the program 'supported 60 student internships' and that 'several interns have participated in multiple projects that lasted more than one year (so in some cases the totals in prior sections add up to over 60).' Yet Figure 2 labels the same quantity 60 as the number of students at the time of internship, and the percentages in Figures 1 and 2 are computed as if 60 were the number of unique students. If any student held multiple internships, the percentages in Figures 1 and 2 are over an ill-defined base, and the 75% retention claim is not reproducible. The authors should state the number of unique students explicitly and recompute all percentages over that base.
  3. [Section III, Figure 3] The citation comparison between intern-participating and non-intern papers is presented as evidence that 'papers involving interns have a comparable impact... to papers without intern involvement,' but the construction of the comparison is not described. The caption refers to 'all publications (2014-April 2025) of USRA,' and the text does not specify how the two sets were matched, whether the non-intern set is intended as a control, how citation counts were normalized for publication year and venue, or how papers with both intern and non-intern authors were assigned. Without this information, the figure supports only a descriptive comparison, not the causal or comparative claim stated in the text. A specification of the inclusion criteria, matching procedure, and normalization, or a revised and more modest claim, is needed.
  4. [Section V, final paragraph] The opening sentence of the conclusion generalizes from the program's outcomes to a 'proven model for experiential learning in quantum computing.' Even if the retention and citation claims were fully documented, the paper compares the Feynman Academy to no counterfactual and reports no comparison group of students who did not receive this internship. The claim of a 'proven model' goes beyond what the data can support; this sentence should be softened to describe demonstrated outcomes of the program rather than a validated causal model.
minor comments (5)
  1. [Title and throughout] The informal title 'If You Give a Student a Quantum Internship' is engaging, but the paper would benefit from a brief program-outcome summary in the abstract that states the number of unique students, the data sources, and the period covered, so that the reader does not have to infer these from the body.
  2. [Section V, paragraph 2] The sentence listing employers ('Google, Amazon, IBM, JP Morgan Bank and Boeing') is presented without any counts or denominators. If this list is meant to illustrate placement outcomes, a count of alumni known to hold positions at each type of employer would make the claim more informative.
  3. [Section III, paragraph 6] Typographical errors occur throughout, including 'experential learning opportunities' (twice), 'serves serve as current state-of-the-art baselines' in Section III, and 'the alst decade' and 'denoisinghappen' in Appendix A. A careful proofreading pass is needed.
  4. [Section IV] The related-programs section provides useful context, but it does not compare student outcomes, selection criteria, or cost structures. Adding a short comparison table of program characteristics (duration, level, funding, mentoring structure, reported outcomes) would make the section more useful and strengthen the paper's contribution as a program review.
  5. [Appendix A] The appendix is a long list of research summaries, but the connection between each summary and the claim that a Feynman Academy intern co-authored the work is not always stated. The reader cannot tell from the appendix alone which specific students were involved, and the mapping between the references in Appendix A and the intern/non-intern classification used in Figure 3 is not provided.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the paper is a retrospective program self-review whose central quantitative claims are empirical observations, not derivations from inputs.

full rationale

The paper's central claims—60 internships supported, over 75% of former interns remaining in quantum-related work, and comparable citation counts for intern-collaboration papers—are presented as measured outcomes of a program, not as results derived from a model or from the paper's own assumptions. There is no fitted parameter that is later called a prediction, no equation whose output is equivalent to its input, and no uniqueness theorem imported from the authors' prior work. The authors do cite their own QuAIL/USRA papers and list many USRA-authored publications, but these citations serve as descriptive examples of the program's research output rather than as load-bearing justification for the empirical outcome claims. The main quantitative claims are in principle externally checkable against an alumni roster and citation databases. The concern raised elsewhere about undocumented alumni-tracking methodology is a transparency and measurement-validity issue, not a circularity issue, because the paper does not exhibit any quoted step where a claimed result reduces by construction to its own input. Therefore, under the requirement to cite a specific reduction before flagging circularity, the appropriate finding is no significant circularity.

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

No free parameters or invented entities appear in this review paper. The load-bearing assumptions are about data quality: the alumni tracking methodology, the attribution of intern contributions, and the interpretation of citation counts.

assumptions (3)
  • domain assumption The alumni career-outcome data is complete and unbiased with respect to the 75% retention claim.
    Section V states 'over 75% remain in the quantum industry in some capacity at this time' without reporting the data collection method, response rate, or how lost contacts are counted. If non-respondents are excluded or assumed negative, the figure changes.
  • domain assumption Citation counts are a valid proxy for the 'caliber' or impact of the intern-produced work.
    Figure 3's comparison of citations between intern and non-intern papers implicitly assumes citations measure quality or impact in a way that supports the program's value. The paper does not test this assumption.
  • domain assumption The list of publications in Appendix A accurately attributes intern involvement.
    The narrative claims interns contributed to the listed papers, but the paper does not specify how intern authorship was determined or whether all listed authors were interns.

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Cite this review

Pith. "Pith review of The USRA Feynman Quantum Academy: If You Give a Student a Quantum Internship." pith.science (2026). https://pith.science/paper/JU5FWO3N

@misc{pith2026250504641,
  author       = {Pith},
  title        = {Pith review of: The USRA Feynman Quantum Academy: If You Give a Student a Quantum Internship},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/JU5FWO3N}},
  note         = {Machine review of arXiv:2505.04641}
}
read the original abstract

In the rapidly expanding field of quantum computing, one key aspect to maintain ongoing progress is ensuring that early career scientists interested in the field get appropriate guidance and opportunity to advance their work, and in return that institutions and enterprises with a stake in quantum computing have access to a qualified pool of talent. Internship programs at the graduate level are the perfect vehicle to achieve this. In this paper, we review the trajectory of the USRA Feynman Quantum Academy Internship Program over the last 8 years, placing it in the context of the current push to prepare the quantum workforce of the future, and highlighting the caliber of the work it produced.

Figures

Figures reproduced from arXiv: 2505.04641 by the authors.

Figure 1
Figure 1. FIG. 1: Breakdown of the current positions held by the Feynman Quantum Academy interns. A large portion work [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. FIG. 2: At the time of their internship, most students (34, or 57%) were pursuing their PhD, with the next largest [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. FIG. 3: Citations by year of all publications (2014-April 2025) of USRA, highlighting the split between papers in [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: FIG. 4: Each paper where interns collaborated is categorized by main topic (represented by color), then by [PITH_FULL_IMAGE:figures/full_fig_p008_4.png]

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Works this paper leans on

93 extracted references · 54 canonical work pages

  1. [1]

    E. G. Rieffel, S. Hadfield, T. Hogg, S. Mandr` a, J. Mar- shall, G. Mossi, B. O’Gorman, E. Plamadeala, N. M. Tubman, D. Venturelli, W. Vinci, Z. Wang, M. Wil- son, F. Wudarski, and R. Biswas, From Ans¨ atze to Z- gates: a NASA View of Quantum Computing, IOS Press 10.3233/APC190010 (2019)

    work page doi:10.3233/apc190010 2019

  2. [2]

    E. G. Rieffel, A. A. Asanjan, M. S. Alam, N. Anand, D. E. Bernal Neira, S. Block, L. T. Brady, S. Cotton, Z. Gon- zalez Izquierdo, S. Grabbe, E. Gustafson, S. Hadfield, P. A. Lott, F. B. Maciejewski, S. Mandr` a, J. Marshall, G. Mossi, H. M. Bauza, J. Saied, N. Suri, D. Venturelli, Z. Wang, and R. Biswas, Assessing and advancing the po- tential of quantum...

    2024

  3. [3]

    J. F. Binder, T. Baguley, C. Crook, and F. Miller, The academic value of internships: Benefits across disci- plines and student backgrounds, Contemporary Educa- tional Psychology 41, 73 (2015)

    2015

  4. [4]

    Lincoln, S

    D. Lincoln, S. Pasero, and R. Thompson, Mak- ing future researchers: Internship opportunities for physics students, The Physics Teacher 57, 642 (2019), https://pubs.aip.org/aapt/pte/article- pdf/57/9/642/9871783/642 1 online.pdf

    2019

  5. [5]

    T. Bose, S. Malik, and M. Narain, U.S. CMS - PUR- SUE (Program for Undergraduate Research SUmmer Ex- perience), in Snowmass 2021 (2022) arXiv:2209.10109 [physics.ed-ph]

    work page Pith review arXiv 2022

  6. [6]

    A novel internship program in HEP

    S. Banerjee, T. Bose, U. Heintz, and S. Malik, A novel internship program in HEP, arXiv (2024), arXiv:2401.16217 [physics.ed-ph]

    work page Pith review arXiv 2024

  7. [7]

    Z. F. Murgu´ ıa Burton and X. E. Cao, Let graduate stu- dents do internships, Matter 5, 4100 (2022)

    2022

  8. [8]

    McNeil and P

    L. McNeil and P. Heron, Preparing physics stu- dents for 21st-century careers, Physics Today 70, 38 (2017), https://pubs.aip.org/physicstoday/article- pdf/70/11/38/10118369/38 1 online.pdf

    2017

Show all 93 references
  1. [9]

    Withheld, J

    N. Withheld, J. Luhmann, N. C., and J. Cor- rell, D. L., Altering the academy in in- dustry’s interests, Physics Today 48, 13 (1995), https://pubs.aip.org/physicstoday/article- pdf/48/6/13/8308512/13 1 online.pdf

    1995

  2. [10]

    United Nations, https://quantum2025.org/

  3. [11]

    Committee on Science and Committee on Homeland and National Security (National Science and Technology Council, Interagency Working Group on Quantum In- formation Science of the Subcommittee on Physical Sci- ences, Advancing quantum information science: National challenges and ...

    2016

  4. [12]

    6227 (2018)

    National Quantum Initiative Act, 115th congress, h.r. 6227 (2018)

    2018

  5. [13]

    M. F. J. Fox, B. M. Zwickl, and H. J. Lewandowski, Preparing for the quantum revolution: What is the role of higher education?, Phys. Rev. Phys. Educ. Res. 16, 020131 (2020)

    2020

  6. [14]

    J. K. Perron, C. DeLeone, S. Sharif, T. Carter, J. M. Grossman, G. Passante, and J. Sack, Quantum Un- dergraduate Education and Scientific Training, arXiv e-prints , arXiv:2109.13850 (2021), arXiv:2109.13850 [physics.ed-ph]

    2021 arXiv

  7. [15]

    C. D. Aiello, D. D. Awschalom, H. Bernien, T. Brower, K. R. Brown, T. A. Brun, J. R. Caram, E. Chitambar, R. D. Felice, K. M. Edmonds, M. F. J. Fox, S. Haas, A. W. Holleitner, E. R. Hudson, J. H. Hunt, R. Joynt, S. Koziol, M. Larsen, H. J. Lewandowski, D. T. McClure, J. Palsbe...

    2021

  8. [16]

    Hughes, D

    C. Hughes, D. Finke, D.-A. German, C. Merzbacher, P. M. Vora, and H. J. Lewandowski, Assessing the needs of the quantum industry, IEEE Transactions on Educa- tion 65, 592 (2022)

    2022

  9. [17]

    Asfaw, A

    A. Asfaw, A. Blais, K. R. Brown, J. Candelaria, C. Cantwell, L. D. Carr, J. Combes, D. M. Debroy, J. M. Donohue, S. E. Economou, E. Edwards, M. F. J. Fox, S. M. Girvin, A. Ho, H. M. Hurst, Z. Jacob, B. R. John- son, E. Johnston-Halperin, R. Joynt, E. Kapit, J. Klein- Seetharam...

    2022 arXiv

  10. [18]

    Kaur and A

    M. Kaur and A. Venegas-Gomez, Defining the quantum workforce landscape: a review of global quantum educa- tion initiatives, Optical Engineering 61, 081806 (2022)

    2022

  11. [19]

    Raghunathan, V

    R. Raghunathan, V. A. Chekuri, M. Fuhrer, I. Wilde- mann, C. Wu, B. Sarabi, W. Scales, L. F. Lester, and V. Kovanis, Toward a quantum-enabled workforce: cur- riculum design, project based learning, and institutional partnerships as integrated methodologies, in Seventeenth Conf...

    2023

  12. [20]

    Darienzo, A

    M. Darienzo, A. M. Kelly, D. Schneble, and T.-C. Wei, Student attitudes toward quantum information science and technology in a high school outreach program, Phys. Rev. Phys. Educ. Res. 20, 020126 (2024)

    2024

  13. [21]

    QLCI - Quantum Leap Challenge Institutes, https://www.nsf.gov/funding/opportunities/qlci- quantum-leap-challenge-institutes

  14. [22]

    Q-Sense - Quantum Sensing through Entangled Science and Engineering., https://www.colorado.edu/research/qsense/

  15. [23]

    M. B. Bennett, J. ´E. Arrow, S. Novack, and N. D. Finkel- stein, Investigating Student Participation in Quantum Workforce Initiatives, arXiv e-prints , arXiv:2407.14698 (2024), arXiv:2407.14698 [physics.ed-ph]

    2024 arXiv

  16. [24]

    EdQuantum - Hybrid Curriculum in Advenced Op- tics, Spectroscopy, and Quantum Technologies., https://edquantum.org/

  17. [25]

    Hasanovic, C

    M. Hasanovic, C. A. Panayiotou, D. M. Silberman, P. Stimers, and C. I. Merzbacher, Quantum technician skills and competencies for the emerging Quantum 2.0 industry, Optical Engineering 61, 081803 (2022)

    2022

  18. [26]

    K. A. Oliver, V. Borish, B. R. Wilcox, and H. J. Lewandowski, Education for expanding the quantum workforce: Student perceptions of the quantum indus- try in an upper-division physics capstone course, arXiv e-prints , arXiv:2407.07902 (2024), arXiv:2407.07902 [physics.ed-ph]

    2024 arXiv

  19. [27]

    Quantum Technology Education., https://qtedu.eu/

  20. [28]

    Gerke, R

    F. Gerke, R. M¨ uller, P. Bitzenbauer, M. Ubben, and K.- A. Weber, Requirements for future quantum workforce – a delphi study, Journal of Physics: Conference Series 2297, 012017 (2022)

    2022

  21. [29]

    QUCATS - Coordination and support action of the Quantum Flagship., https://qt.eu/projects/csa- projects/qucats

  22. [30]

    Greinert, R

    F. Greinert, R. M¨ uller, P. Bitzenbauer, M. S. Ubben, and K.-A. Weber, Future quantum workforce: Competences, requirements, and forecasts, Phys. Rev. Phys. Educ. Res. 19, 010137 (2023)

    2023

  23. [31]

    G. F, M. R, G. S, S. J, and U. MS, Towards a quan- tum ready workforce: the updated european competence framework for quantum technologies, Front. Quantum Sci. Technol. 10.3389/frqst.2023.1225733 (2023)

    2023

  24. [32]

    DigiQ - Digitally Enhanced Quantum Technology Mas- ter., https://www.digiq.eu/

  25. [33]

    Quantum Technologies Courses for Industry., https://qtindu.eu/

  26. [34]

    Greinert, M

    F. Greinert, M. S. Ubben, I. N. Dogan, D. Hilfert- R¨ uppell, and R. M¨ uller, Advancing quantum tech- nology workforce: industry insights into qualification and training needs, EPJ Quantum Technology 11, 10.1140/epjqt/s40507-024-00294-2 (2024)

    2024 doi

  27. [35]

    Biswas, Z

    R. Biswas, Z. Jiang, K. Kechezhi, S. Knysh, S. Mandr` a, B. O’Gorman, A. Perdomo-Ortiz, A. Petukhov, J. Realpe-G´ omez, E. Rieffel, D. Venturelli, F. Vasko, and Z. Wang, A nasa perspective on quantum comput- ing: Opportunities and challenges, Parallel Computing 64, 81 (2017), ...

    2017

  28. [36]

    lanl.gov/engage/collaboration/internships/ summer-schools/quantumschool

    Los Alamos National Laboratory, https://www. lanl.gov/engage/collaboration/internships/ summer-schools/quantumschool

  29. [37]

    Fermilab, https://sqmscenter.fnal.gov/ opportunities/internships-and-fellowships/

  30. [38]

    M. S. Alam, S. Hadfield, H. Lamm, and A. C. Li, Primi- tive quantum gates for dihedral gauge theories, Physical Review D 105, 10.1103/physrevd.105.114501 (2022)

    2022 doi

  31. [39]

    Bassman, K

    L. Bassman, K. Klymko, D. Liu, N. M. Tubman, and W. A. de Jong, Computing free energies with fluctuation relations on quantum computers (2021), arXiv:2103.09846 [quant-ph]

    2021 arXiv

  32. [40]

    Kerger and R

    P. Kerger and R. Miyazaki, Quantum image denois- ing: a framework via boltzmann machines, qubo, and quantum annealing, Frontiers in Computer Science 5, 10.3389/fcomp.2023.1281100 (2023)

    2023

  33. [41]

    Juenger, E

    M. Juenger, E. Lobe, P. Mutzel, G. Reinelt, F. Rendl, G. Rinaldi, and T. Stollenwerk, Performance of a quan- tum annealer for ising ground state computations on chimera graphs (2019), arXiv:1904.11965 [cs.DS]

    2019 arXiv

  34. [42]

    Gonzalez Izquierdo, S

    Z. Gonzalez Izquierdo, S. Grabbe, S. Hadfield, J. Mar- shall, Z. Wang, and E. Rieffel, Ferromagnetically shift- ing the power of pausing, Physical Review Applied 15, 10.1103/physrevapplied.15.044013 (2021)

    2021 doi

  35. [43]

    Z. G. Izquierdo, I. Hen, and T. Albash, Testing a quan- tum annealer as a quantum thermal sampler, ACM Transactions on Quantum Computing 2, 1–20 (2021)

    2021

  36. [44]

    Pokharel, Z

    B. Pokharel, Z. Gonzalez Izquierdo, and P. e. a. Lott, Inter-generational comparison of quantum annealers in solving hard scheduling problems, Quantum Information Processing 22 (2023)

    2023

  37. [45]

    D. E. Bernal, K. E. C. Booth, R. Dridi, H. Al- ghassi, S. Tayur, and D. Venturelli, Integer programming techniques for minor-embedding in quantum annealers (2019), arXiv:1912.08314 [quant-ph]

    2019 arXiv

  38. [46]

    R. Levy, Z. Gonzalez Izquierdo, Z. Wang, J. Marshall, J. Barreto, L. Fry-Bouriaux, D. T. O’Connor, P. A. Warburton, N. Wiebe, E. Rieffel, and F. A. Wudarski, Towards solving the Fermi-Hubbard model via tailored quantum annealers, arXiv e-prints , arXiv:2207.14374 (2022), arXiv...

    2022 arXiv

  39. [47]

    J. Sud, S. Hadfield, E. Rieffel, N. Tubman, and T. Hogg, A parameter setting heuristic for the quantum alternat- ing operator ansatz (2022), arXiv:2211.09270 [quant-ph]

    2022 arXiv

  40. [48]

    Streif, M

    M. Streif, M. Leib, F. Wudarski, E. Rieffel, and Z. Wang, Quantum algorithms with local particle-number conser- vation: Noise effects and error correction, Physical Re- view A 103, 10.1103/physreva.103.042412 (2021)

    2021 doi

  41. [49]

    Kremenetski, A

    V. Kremenetski, A. Apte, T. Hogg, S. Hadfield, and N. M. Tubman, Quantum alternating operator ansatz (qaoa) beyond low depth with gradually changing uni- taries (2023), arXiv:2305.04455 [quant-ph]

    2023 arXiv

  42. [50]

    LaRose, E

    R. LaRose, E. Rieffel, and D. Venturelli, Mixer-phaser ans¨ atze for quantum optimization with hard constraints, Quantum Machine Intelligence 4, 10.1007/s42484-022- 11 00069-x (2022)

    2022 doi

  43. [51]

    Taassob, D

    A. Taassob, D. Venturelli, and P. A. Lott, Neural deep operator networks representation of coherent ising ma- chine dynamics, in Machine Learning with New Compute Paradigms (2023)

    2023

  44. [52]

    E. J. Gustafson, A. C. Y. Li, A. Khan, J. Kim, D. M. Kurkcuoglu, M. S. Alam, P. P. Orth, A. Rahmani, and T. Iadecola, Preparing quantum many-body scar states on quantum computers (2023), arXiv:2301.08226 [quant- ph]

    2023 arXiv

  45. [53]

    M. R. Hirsbrunner, D. Chamaki, J. W. Mulli- nax, and N. M. Tubman, Beyond mp2 initialization for unitary coupled cluster quantum circuits (2023), arXiv:2301.05666 [quant-ph]

    2023 arXiv

  46. [54]

    Sorourifar, D

    F. Sorourifar, D. Chamaki, N. M. Tubman, J. Paul- son, and D. E. Bernal Neira, Bayesian optimization pri- ors for efficient variational quantum algorithms, in 34th European Symposium on Computer Aided Process Engi- neering / 15th International Symposium on Process Sys- tems En...

    2024

  47. [55]

    Parolini and G

    T. Parolini and G. Mossi, Multifractal dynamics of the qrem (2020), arXiv:2007.00315 [cond-mat.dis-nn]

    2020 arXiv

  48. [56]

    N. Suri, J. Barreto, S. Hadfield, N. Wiebe, F. Wudarski, and J. Marshall, Two-Unitary Decomposition Algorithm and Open Quantum System Simulation, Quantum 7, 1002 (2023)

    2023

  49. [57]

    D. B. Chamaki, S. Hadfield, K. Klymko, B. O’Gorman, and N. M. Tubman, Self-consistent quantum iteratively sparsified hamiltonian method (squish): A new algo- rithm for efficient hamiltonian simulation and compres- sion (2022), arXiv:2211.16522 [quant-ph]

    2022 arXiv

  50. [58]

    Verdon, J

    G. Verdon, J. Marks, S. Nanda, S. Leichenauer, and J. Hidary, Quantum hamiltonian-based models and the variational quantum thermalizer algorithm (2019), arXiv:1910.02071 [quant-ph]

    2019 arXiv

  51. [59]

    Templin, M

    T. Templin, M. Memarzadeh, W. Vinci, P. A. Lott, A. Akbari Asanjan, A. Alexiades Armenakas, and E. Rieffel, Anomaly detection in aeronautics data with quantum-compatible discrete deep generative model, Ma- chine Learning: Science and Technology 4, 035018 (2023)

    2023

  52. [60]

    Brown, D

    R. Brown, D. E. B. Neira, D. Venturelli, and M. Pavone, A copositive framework for analysis of hybrid ising- classical algorithms (2023), arXiv:2207.13630 [math.OC]

    2023 arXiv

  53. [61]

    A. Khan, P. Vaish, Y. Pang, N. Kowshik, M. S. Chen, C. H. Batton, G. M. Rotskoff, J. W. Mullinax, B. K. Clark, B. M. Rubenstein, and N. M. Tubman, Quan- tum Hardware-Enabled Molecular Dynamics via Trans- fer Learning, arXiv e-prints , arXiv:2406.08554 (2024), arXiv:2406.08554 ...

    2024 arXiv

  54. [62]

    A. Khan, B. K. Clark, and N. M. Tubman, Pre- optimizing variational quantum eigensolvers with ten- sor networks, arXiv e-prints , arXiv:2310.12965 (2023), arXiv:2310.12965 [quant-ph]

    2023 arXiv

  55. [63]

    E. J. Gustafson, J. Tiihonen, D. Chamaki, F. Sorourifar, J. W. Mullinax, A. C. Y. Li, F. B. Maciejewski, N. P. Sawaya, J. T. Krogel, D. E. Bernal Neira, and N. M. Tubman, Surrogate optimization of variational quan- tum circuits, arXiv e-prints , arXiv:2404.02951 (2024), arXiv:...

    2024 arXiv

  56. [64]

    Broughton, G

    M. Broughton, G. Verdon, T. McCourt, A. J. Martinez, J. H. Yoo, S. V. Isakov, P. Massey, R. Halavati, M. Y. Niu, A. Zlokapa, E. Peters, O. Lockwood, A. Skolik, S. Jerbi, V. Dunjko, M. Leib, M. Streif, D. V. Dollen, H. Chen, S. Cao, R. Wiersema, H.-Y. Huang, J. R. McClean, R. B...

    2021 arXiv

  57. [65]

    Villalonga, S

    B. Villalonga, S. Boixo, B. Nelson, C. Henze, E. Rieffel, R. Biswas, and S. Mandr` a, A flexible high-performance simulator for verifying and benchmarking quantum cir- cuits implemented on real hardware, npj Quantum Infor- mation 5, 10.1038/s41534-019-0196-1 (2019)

    2019 doi

  58. [66]

    Chowdhury, N

    S. Chowdhury, N. A. Aadit, A. Grimaldi, E. Raimondo, A. Raut, P. A. Lott, J. H. Mentink, M. M. Rams, F. Ricci-Tersenghi, M. Chiappini, L. S. Theogarajan, T. Srimani, G. Finocchio, M. Mohseni, and K. Y. Cam- sari, Pushing the boundary of quantum advantage in hard combinatorial ...

    2025

  59. [67]

    Aadit, P

    N. Aadit, P. A. Lott, and M. Mohseni, Nonlocal Monte Carlor (2023)

    2023

  60. [68]

    Aadit, P

    N. Aadit, P. A. Lott, and M. Mohseni, APT-solver: An Adaptive Parallel Tempering Solver (2023)

    2023

  61. [69]

    D. E. Bernal Neira, R. Brown, P. Sathe, and D. Ven- turelli, Stochastic Benchmark: toolkit for performance evaluation and parameter tuning of stochastic parame- terized stochastic optimization solvers (2023)

    2023

  62. [70]

    D. E. Bernal Neira, R. Brown, P. Sathe, F. Wudarski, M. Pavone, E. G. Rieffel, and D. Venturelli, Benchmark- ing the Operation of Quantum Heuristics and Ising Ma- chines: Scoring Parameter Setting Strategies on Opti- mization Applications, arXiv e-prints , arXiv:2402.10255 (20...

    2024 arXiv

  63. [71]

    Claes, E

    J. Claes, E. Rieffel, and Z. Wang, Character randomized benchmarking for non-multiplicity-free groups with ap- plications to subspace, leakage, and matchgate random- ized benchmarking, PRX Quantum 2, 010351 (2021)

    2021

  64. [72]

    H.-Y. Hu, R. LaRose, Y.-Z. You, E. Rieffel, and Z. Wang, Logical shadow tomography: Efficient estimation of error-mitigated observables (2022), arXiv:2203.07263 [quant-ph]

    2022 arXiv

  65. [73]

    Lubinski, C

    T. Lubinski, C. Coffrin, C. McGeoch, P. Sathe, J. Apanavicius, D. Bernal Neira, and Q. E. D. C.-C. Col- laboration, Optimization applications as quantum per- formance benchmarks, ACM Transactions on Quantum Computing 5, 10.1145/3678184 (2024)

    2024 doi

  66. [74]

    Leipold, F

    H. Leipold, F. M. Spedalieri, and E. Rieffel, Tailored quantum alternating operator ansatzes for circuit fault diagnostics, Algorithms 15, 10.3390/a15100356 (2022)

    2022 doi

  67. [75]

    N. Gao, M. Wilson, T. Vandal, W. Vinci, R. Nemani, and E. Rieffel, High-dimensional similarity search with quantum-assisted variational autoencoder, inProceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining , KDD ’20 (Asso- ciation for ...

  68. [76]

    Wilson, T

    M. Wilson, T. Vandal, and T. e. a. Hogg, Quantum- assisted associative adversarial network: applying quan- tum annealing in deep learning, Quantum Machine Intel- ligence 3, 10.1007/s42484-021-00047-9 (2021)

    2021 doi

  69. [77]

    Wilson, R

    M. Wilson, R. Stromswold, and F. e. a. Wudarski, Op- timizing quantum heuristics with meta-learning, Quan- tum Machine Intelligence 3, 10.1007/s42484-020-00022-w (2021). 12

    2021 doi

  70. [78]

    Akbari Asanjan, M

    A. Akbari Asanjan, M. Memarzadeh, P. A. Lott, E. Ri- effel, and S. Grabbe, Probabilistic wildfire segmentation using supervised deep generative model from satellite im- agery, Remote Sensing 15, 10.3390/rs15112718 (2023)

    2023 doi

  71. [79]

    O’Connor and W

    D. O’Connor and W. Vinci, RBM-Flow and D-Flow: In- vertible Flows with Discrete Energy Base Spaces, arXiv e-prints , arXiv:2012.13196 (2020), arXiv:2012.13196 [cs.LG]. Appendix A: Research Areas and Output of the F eynman Quantum Academy

    2020 arXiv

  72. [81]

    Quantum Computing Architectures and Experimental Designs a. Programmable Quantum Processor Applications A study focused on the dihedral group DN as an ap- proximation forU(1)×Z2 lattice gauge theory, showcased the development of efficient quantum circuits for key op- erations ...

  73. [82]

    Formalizing concepts such as Perfect Homo- geneity and the Classical Homogeneous Proxy for QAOA marks a significant step in this direction

    Combinatorial Optimization Problems Advancements in combinatorial optimization problems within quantum computing are further enriched by inno- vative strategies in parameter setting for quantum al- gorithms. Formalizing concepts such as Perfect Homo- geneity and the Classical ...

  74. [83]

    Quantum State Preparation and Analysis a. Quantum Many-Body Scar (QMBS) States Quantum many-body scar states, known for their unique entanglement and correlation properties, play a pivotal role in sustaining long-lived coherent dynamics. The preparation of the superposition st...

  75. [84]

    Quantum-Enhanced Deep Learning Models Ref

    Advanced Quantum Algorithms and Applications a. Quantum-Enhanced Deep Learning Models Ref. [59] explores the development of unsupervised Variational Autoencoder (VAE) models with discrete la- tent variables for anomaly detection in commercial aero- nautics data. It focuses on ...

  76. [85]

    Software Tools & Development TensorFlow Quantum remains a key platform for quan- tum machine learning models and algorithms [64]

    Software Development a. Software Tools & Development TensorFlow Quantum remains a key platform for quan- tum machine learning models and algorithms [64]. It supports a range of advanced quantum learning tasks, including meta-learning, layerwise learning, Hamiltonian learning, ...

  77. [86]

    Enhanced Techniques for Quantum Benchmarking Quantum gate fidelity benchmarking is essential for evaluating quantum algorithms’ performance

    Benchmarking quantum algorithms and systems a. Enhanced Techniques for Quantum Benchmarking Quantum gate fidelity benchmarking is essential for evaluating quantum algorithms’ performance. While quantum state tomography is a traditional approach, it can be confounded by state p...

  78. [87]

    Quantum-compatible Machine Learning Algorithms Over the past few years, there have been significant developments in machine learning methods, both in dis- criminative modeling such as classification and generative modeling to support analysis based on an understanding of the p...

  79. [88]

    The Classical-Classical Approach (CC), seen in the use of quantum-inspired methods in discrete latent space models, showcases the potential for quantum influence in enhancing traditional algorithms

  80. [89]

    The Classical-Quantum Approach (CQ) is exem- plified by models like the Quantum-assisted Varia- tional Autoencoder (QVAE), where quantum com- puting elements are utilized to process and inter- pret classical data

  81. [90]

    The Quantum-Classical (QC) paradigm, observed in models optimizing quantum heuristics with clas- sical machine learning techniques, reflects the ap- plication of traditional methods to data generated by quantum processors

  82. [91]

    Lastly, the Quantum-Quantum (QQ) approach, though not explored in this section, represents the full fusion of quantum data and quantum compu- tational methods. a. Quantum-assisted Variational Autoencoder In their groundbreaking work, N. Gao et al. introduced a novel approach i...

  83. [92]

    The study presented two distinct DVAE models: one with a factorized Bernoulli prior and another that integrated a Restricted Boltzmann Machine (RBM) as its prior

    investigated the capabilities of unsupervised deep generative models, particularly focusing on VAEs) with discrete latent variables (DVAE). The study presented two distinct DVAE models: one with a factorized Bernoulli prior and another that integrated a Restricted Boltzmann Ma...

  84. [93]

    assesses the efficacy of a Long Short Term Mem- ory (LSTM) recurrent neural network model (the meta- learner) in optimizing two quantum heuristics, compar- ing its performance traditional optimizers (Bayesian opti- mization, evolutionary strategies, L-BFGS-B and Nelder- Mead)....

  85. [2025]

    The first two weeks are exclusively dedicated to lectures given by different subject matter experts. Then each student is paired with an LANL mentor to guide them through their research project over the remainder 8 weeks, which includes hands-on programming of one of the avail...

    2004

Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.