Q-ACE : Smart Control for Quantum Technologies

Quantum technology

  1. A. S. Rao, D. Buterakos, B. van Straaten, V. John, C. X. Yu, S. D. Oosterhout, L. Stehouwer, G. Scappucci, M. Veldhorst, F. Borsoi, and J. P. Zwolak. MAViS: Modular Autonomous Virtualization System for Two-Dimensional Semiconductor Quantum Dot Arrays. arXiv:2411.12516 [to appear in Physical Review X] (2024).
  2. A. Zubchenko, D. Middlebrooks, T. Rasmussen, L. Lausen, F. Kuemmeth, A. Chatterjee, and J. P. Zwolak. Autonomous bootstrapping of quantum dot devices. Phys. Rev. Appl. 23 (1), 014072 (2025).
  3. D. Schug, T. J. Kovach, M. A. Wolfe, J. Benson, S. Park, J. P. Dodson, J. Corrigan, M. A. Eriksson, and J. P. Zwolak. Automation of quantum dot measurement analysis via explainable machine learning. Mach. Learn.: Sci. Technol. 6 (1): 015006 (2025)
  4. J. P. Zwolak, J. M. Taylor, et al. Data needs and challenges for quantum dot devices automation. npj Quantum Inf. 10 (1): 105 (2024).
  5. D. Schug, T. J. Kovach, M. A. Wolfe, J. Benson, S. Park, J. P. Dodson, J. Corrigan, M. A. Eriksson, and J. P. Zwolak. Explainable Classification Techniques for Quantum Dot Device Measurements. In: Proceedings of the XAI4Sci: Explainable machine learning for sciences workshop (AAAI 2024), Vancouver, Canada [February 26, 2024] (2024).
  6. D. Schug, S. Yerramreddy, R. Caruana, C. Greenberg, and J. P. Zwolak. Extending Explainable Boosting Machines to Scientific Image Data. In: Proceedings of the Machine Learning and the Physical Sciences Workshop, (NeurIPS 2023), New Orleans, LA, USA [December 15, 2023] (2023).
  7. J. Ziegler, F. Luthi, M. Ramsey, F. Borjans, G. Zheng, and J. P. Zwolak. Tuning arrays with rays: Physics-informed tuning of quantum dot charge states. Phys. Rev. Appl. 20 (3), 034067 (2023).
    • Featured as Editor’s Suggestion in Physical Review Applied.
  8. J. Ziegler, F. Luthi, M. Ramsey, F. Borjans, G. Zheng, and J. P. Zwolak. Automated extraction of capacitive coupling for quantum dot systems. Phys. Rev. Appl. 19 (5), 054077 (2023).
    • Featured as Editor’s Suggestion in Physical Review Applied.
  9. J. P. Zwolak and J. M. Taylor. Colloquium: Advances in automation of quantum dot devices control. Rev. Mod. Phys. 95(1), 011006 (2023).
    • Best Journal Paper of the Year award by NIST/Information Technology Laboratory (2024).
  10. A. R. Fritsch, S. Guo, S. M. Koh, I. B. Spielman, and J. P. Zwolak. Dark Solitons in Bose-Einstein Condensates: A Dataset for Many-body Physics Research. Mach. Learn.: Sci. Technol. 3 (4): 047001 (2022).
  11. S. Guo, S. M. Koh, A. R. Fritsch, I. B. Spielman, and J. P. Zwolak. Combining machine learning with physics: A framework for tracking and sorting multiple dark solitons. Phys. Rev. Res. 4(2): 023163 (2022).
  12. J. Ziegler, T. McJunkin, E. S. Joseph, S. S. Kalantre, B. Harpt, D. E. Savage, M. G. Lagally, M. A. Eriksson, J. M. Taylor, and J. P. Zwolak. Toward Robust Autotuning of Noisy Quantum Dot Devices. Phys. Rev. Appl. 17 (2): 024069 (2022).
  13. B. J. Weber, S. S. Kalantre, T. McJunkin, J. M. Taylor, and J. P. Zwolak. Theoretical bounds on data requirements for the ray-based classification. SN Comput. Sci. 3 (1): 57 (2022).
  14. J. P. Zwolak, T. McJunkin, S. S. Kalantre, S. F. Neyens, E. R. MacQuarrie, M. A. Eriksson, and J. M. Taylor. Ray-based framework for state identification in quantum dot devices. PRX Quantum 2 (2): 020335 (2021).
  15. S. Guo, A. R. Fritsch, C. Greenberg, I. Spielman, and J. P. Zwolak. Machine-learning enhanced dark soliton detection in Bose-Einstein condensates. Mach. Learn.: Sci. Technol. 2 (3): 035020 (2021).
  16. J. P. Zwolak, S. S. Kalantre, T. McJunkin, B. J. Weber, and J. M. Taylor. Ray-based classification framework for high-dimensional data. In: Proceedings of the Machine Learning and the Physical Sciences Workshop (NeurIPS 2020), Vancouver, Canada [December 11, 2020] (2020).
  17. J. P. Zwolak, T. McJunkin, S. S. Kalantre, J. P. Dodson, E. R. MacQuarrie, D. E. Savage, M. G. Lagally, S. N. Coppersmith, M. A. Eriksson, and J. M. Taylor. Autotuning of double-dot devices in situ with machine learning. Phys. Rev. Appl. 13 (3): 034075 (2020).
  18. S. S. Kalantre, J. P. Zwolak, S. Ragole, X. Wu, N. M. Zimmerman, M. D. Stewart, and J. M. Taylor. Machine Learning techniques for state recognition and auto-tuning in quantum dots. npj Quantum Inf. 5 (1): 6 (2019).
  19. J. P. Zwolak, S. S. Kalantre, X. Wu, S. Ragole, and J. M. Taylor. QFlow lite dataset: A machine-learning approach to the charge states in quantum dot experiments. PLoS ONE 13 (10): e0205844 (2018).
  20. J. P. Zwolak and D. Chruściński. Recurrent construction of optimal entanglement witnesses for 2N-qubit systems. Phys. Rev. A 89 (5): 052314 (2014).
  21. J. P. Zwolak and D. Chruściński. New tools for investigating positive maps in matrix algebras. Rep. Math. Phys. 71 (2): 163–175 (2013).
  22. S. Michalakis and J. P. Zwolak. Stability of frustration-free systems. Comm. Math. Phys. 322 (2): 277–302 (2013).
  23. D. Chruściński and J. Pytel*. Optimal entanglement witnesses from generalized reduction and Robertson maps. J. Phys. A: Math. Theor. 44 (16): 165304 (2011).
  24. D. Chruściński and J. Pytel*. Constructing optimal entanglement witnesses. II. Witnessing entanglement in 4N × 4N systems. Phys. Rev. A 82 (5): 052310 (2010).
  25. D. Chruściński, J. Pytel*, and G. Sarbicki. Constructing optimal entanglement witnesses. Phys. Rev. A 80 (6): 062314 (2009).

* Papers published under my maiden name. The convention in my PhD group was alphabetical ordering of authors, but I was the primary author of these articles. After I graduated, we no longer adhered to this convention.

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Media and Impact

  1. J. P. Zwolak (March 12, 2025). Marie Skłodowska-Curie: A Legacy of Innovation and Empowerment for Women in Science. Taking Measure: Just a Standard Blog, https://www.nist.gov/blogs/taking-measure/marie-sklodowska-curie-legacy-innovation-and-empowerment-women-science.
  2. J. P. Zwolak (March 22, 2023). Ada Lovelace: The World’s First Computer Programmer Who Predicted Artificial Intelligence. Taking Measure: Just a Standard Blog, https://www.nist.gov/blogs/taking-measure/ada-lovelace-worlds-first-computer-programmer-who-predicted-artificial.
  3. Mary Theofanos and J. P. Zwolak (May 16, 2021). Diversity and Inclusivity at NIST. Women in Standards News, https://womeninstandards.org/diversity-and-inclusivity-at-nist/.
  4. J. P. Zwolak (June 2, 2020). Ebb and Flow: Creating Quantum Dots Automatically With AI. Taking Measure: Just a Standard Blog, https://www.nist.gov/blogs/taking-measure/ebb-and-flow-creating-quantum-dots-automatically-ai.
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Social studies

  1. A. M. Andrews and J. P. Zwolak. NIST Scientific Integrity Program. Annual Report. Natl. Inst. Stand. Technol. Sp. Rep. 1313e2024 (2024).
  2. R. P. Dalka and J. P. Zwolak. Network analysis of graduate program support structures through experiences of various demographic groups. Phys. Rev. Phys. Educ. Res. 20 (2): 020106 (2024).
  3. A. M. Andrews and J. P. Zwolak. NIST Scientific Integrity Program. Annual Report. Natl. Inst. Stand. Technol. Sp. Rep. 1313 (2024).
  4. R. P. Dalka, Diana Sachmpazidi, Charles Henderson, and J. P. Zwolak. Network analysis approach to Likert-style surveys. Phys. Rev. Phys. Educ. Res. 18(2): 020113 (2022).
  5. E. A. Williams, J. P. Zwolak, R. Dou, and E. Brewe. Linking engagement and performance: The social network analysis perspective. Phys. Rev. Phys. Educ. Res. 15 (2): 020150 (2019).
  6. R. Dou, and J. P. Zwolak. Practitioner’s guide to social network analysis: Examining physics anxiety. Phys. Rev. Phys. Educ. Res. 15 (2): 020105 (2019).
    • An invited article in the Quantitative Methods in PER: A Critical Examination Focused Collection.
  7. E. M. Smith, J. P. Zwolak, and C. A. Manogue. Isolating approaches: How middle-division physics students coordinate forms and representations in complex algebra. Phys. Rev. Phys. Educ. Res. 15 (1): 010138 (2019).
  8. C. A. Hass, F. Genz, M. B. Kustusch, P.-P. A. Ouimet, K. Pomian, E. C. Sayre, and J. P. Zwolak. Studying community development: A network analytical approach. In: Proceedings of the Physics Education Research Conference 2018, Washington, DC [August 1-2, 2018], pp. 1–4 (2019).
  9. R. Dou, E. Brewe, G. Potvin, J. P. Zwolak, and Z. Hazari. Understanding the development of interest and self-efficacy in active-learning undergraduate physics courses. Int. J. Sci. Educ. 40 (13): 1587–1605 (2018).
  10. J. P. Zwolak, M. Zwolak, and E. Brewe. Educational commitment and social networking: The power of informal networks. Phys. Rev. Phys. Educ. Res. 14 (1): 010131 (2018).
    • Featured as Editor’s Suggestion in Physical Review Physics Education Research.
    • Featured as a Research Highlight in Nature Physics: “Friendly persistence”, 14: 528 (2018).
  11. J. P. Zwolak, R. Dou, and E. Brewe. Student perceptions of the value of out-of-class interactions: Attitudes vs. Practice. In: Proceedings of the Physics Education Research Conference 2017, Cincinnati, OH [July 26-27, 2017], pp. 480–483 (2018).
  12. E. Williams, J. P. Zwolak, and E. Brewe. Physics major engagement and persistence: A phenomenography interview study. In: Proceedings of the Physics Education Research Conference 2017, Cincinnati, OH [July 26-27, 2017], pp. 436–439 (2018).
  13. K. Pomian, J. P. Zwolak, E. Sayre, S. Franklin, and M. B. Kustusch. Using Social Network Analysis on classroom video data. In: Proceedings of the Physics Education Research Conference 2017, Cincinnati, OH [July 26-27, 2017], pp. 316–319 (2018).
  14. J. P. Zwolak, R. Dou, E. A. Williams, and E. Brewe. Students’ network integration as a predictor of persistence in introductory physics courses. Phys. Rev. Phys. Educ. Res. 13 (1): 010113 (2017).
  15. R. Dou, E. Brewe, J. P. Zwolak, G. Potvin, E. Williams, and L. H. Kramer. Beyond performance metrics: Examining a drop in students’ physics self-efficacy through a social networks lens. Phys. Rev. Phys. Educ. Res. 12 (2): 020124 (2016).
  16. J. P. Zwolak and E. Brewe. The impact of network embeddedness on student persistence in introductory Modeling Instruction courses. In: Proceedings of the Physics Education Research Conference 2015, College Park, MD [July 29-30, 2015], pp. 395–398 (2015).
  17. E. Williams, E. Brewe, J. P. Zwolak, and R. Dou. Understanding centrality: Investigating student outcomes within a classroom social network. In: Proceedings of the Physics Education Research Conference 2015, College Park, MD [July 29-30, 2015], pp. 375–378 (2015).
  18. E. M. Smith, J. P. Zwolak, and C. A. Manogue. Student difficulties with complex numbers. In: Proceedings of the Physics Education Research Conference 2015, College Park, MD [July 29-30, 2015], pp. 311–314 (2015).
  19. J. P. Zwolak and C. A. Manogue. Assessing student reasoning in upper-division electricity and magnetism at Oregon State University. Phys. Rev. ST Phys. Educ. Res. 11 (2): 020125 (2015).
    • An invited article within the PER in Upper Division Physics Courses Focused Collection.
  20. J. P. Zwolak and C. A. Manogue. Revealing differences Between Curricula Using the Colorado Upper-Division Electrostatics Diagnostic. In: Proceedings of the Physics Education Research Conference 2014, Minneapolis, MN [July 30-31, 2014], pp. 295–298 (2015).
  21. J. P. Zwolak, M. B. Kustusch, and C. A. Manogue. Re-thinking the Rubric for Grading the CUE: The Superposition Principle. In: Proceedings of the Physics Education Research Conference 2013, Portland, OR [July 17-18, 2013], pp. 385–388 (2014).
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