Reuse & Permissions

It is not necessary to obtain permission to reuse this article or its components as it is available under the terms of the Creative Commons Attribution 4.0 International license. This license permits unrestricted use, distribution, and reproduction in any medium, provided attribution to the author(s) and the published article's title, journal citation, and DOI are maintained. Please note that some figures may have been included with permission from other third parties. It is your responsibility to obtain the proper permission from the rights holder directly for these figures.

Export citation

Export citation

Choose format for download:

Download Citation
  • Editors' Suggestion
  • Open Access

Can a controlled-not gate affect the control qubit? Student resources for understanding controlled-not

Soren S. Plueger1, Bethany R. Wilcox1, Steven J. Pollock1, and Gina Passante2,*

  • *Contact author: gpassante@fullerton.edu

Phys. Rev. Phys. Educ. Res. 22, 020145 – Published 9 October, 2026

DOI: https://doi.org/10.1103/6tn7-klxh

Abstract

The controlled-not (cnot) gate is essential to algorithms in quantum computing for its ability to entangle qubits. As such, it is important to understand how students learning quantum computing reason around the function and use of this critical quantum gate. To investigate this, we conducted think-aloud interviews in which students solved problems involving the cnot gate to understand students’ “cnot toolbox”—the strategies and cognitive resources students use when reasoning about the effect of the cnot gate. We identify three cognitive resources related to the cnot gate: (1) the procedural resource of applying cnot to specific states, (2) a qualitative description of cnot’s effect on the target qubit given the control qubit, and (3) the idea that the control qubit is not changed when cnot is applied to computational basis states. We find that students’ use of the first resource is foundational to their understanding of the second and third, that the second and third resources can sometimes lead students to incorrect conclusions, and that students can use each of these resources separately or in tandem. We also explore how students use these resources in conjunction with Dirac notation, superposition states, and entanglement to reason both productively and unproductively about quantum computing problems.

View figure in article

Physics Subject Headings (PhySH)

Article Text

References (39)

  1. J. C. Meyer, G. Passante, S. J. Pollock, and B. R. Wilcox, Introductory quantum information science coursework at US institutions: Content coverage, Eur. Phys. J. Quantum Technol. 11, 16 (2024).
  2. B. Cervantes, G. Passante, B. R. Wilcox, and S. J. Pollock, An overview of quantum information science courses at US institutions, in Physics Education Research Conference Proceedings (2021).
  3. T. Plunkett, T. L. Frantz, H. Khatri, P. Rajendran, and S. Midha, A survey of educational efforts to accelerate a growing quantum workforce, in 2020 IEEE International Conference on Quantum Computing and Engineering (QCE) (IEEE, New York, 2020), pp. 330–336.
  4. J. C. Meyer, G. Passante, S. J. Pollock, and B. R. Wilcox, Today’s interdisciplinary quantum information classroom: Themes from a survey of quantum information science instructors, Phys. Rev. Phys. Educ. Res. 18, 010150 (2022).
  5. 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 et al., Building a quantum engineering undergraduate program, IEEE Trans. Ed. 65, 220 (2022).
  6. A. S. Dzurak, J. Epps, A. Laucht, R. Malaney, A. Morello, H. I. Nurdin, J. J. Pla, A. Saraiva, and C. H. Yang, Development of an undergraduate quantum engineering degree, IEEE Trans. Quantum Eng. 3, 1 (2022).
  7. E. Marshman and C. Singh, Framework for understanding the patterns of student difficulties in quantum mechanics, Phys. Rev. ST Phys. Educ. Res. 11, 020119 (2015).
  8. P. J. Emigh, E. Gire, C. A. Manogue, G. Passante, and P. S. Shaffer, Research-based quantum instruction: Paradigms and tutorials, Phys. Rev. Phys. Educ. Res. 16, 020156 (2020).
  9. G. Passante and A. Kohnle, Interactive homework to support student learning of measurement uncertainty in quantum mechanics, Phys. Rev. Phys. Educ. Res. 20, 020131 (2024).
  10. E. Gire and E. Price, Structural features of algebraic quantum notations, Phys. Rev. ST Phys. Educ. Res. 11, 020109 (2015).
  11. J. Meyer, G. Passante, S. J. Pollock, M. Vignal, and B. R. Wilcox, Investigating students’ strategies for interpreting quantum states in an upper-division quantum computing course, in Physics Education Research Conference Proceedings (2021).
  12. T. Kushimo and B. Thacker, Investigating students’ strengths and difficulties in quantum computing, in 2023 IEEE International Conference on Quantum Computing and Engineering (QCE) (IEEE, New York, 2023), Vol. 3, pp. 33–39.
  13. T. Bouchée, L. de Putter - Smits, M. Thurlings, and B. Pepin, Towards a better understanding of conceptual difficulties in introductory quantum physics courses, Stud. Sci. Educ. 58, 183 (2022).
  14. D. Hammer, Student resources for learning introductory physics, Am. J. Phys. 68, S52 (2000).
  15. G. Corsiglia, S. Pollock, and G. Passante, Intuition in quantum mechanics: Student perspectives and expectations, Phys. Rev. Phys. Educ. Res. 19, 010109 (2023).
  16. K. Krijtenburg-Lewerissa, H. J. Pol, A. Brinkman, and W. R. van Joolingen, Insights into teaching quantum mechanics in secondary and lower undergraduate education, Phys. Rev. Phys. Educ. Res. 13, 010109 (2017).
  17. G. Corsiglia, S. Pollock, and B. Wilcox, Effectiveness of an online homework tutorial about changing basis in quantum mechanics, in Physics Education Research Conference (AAPT, Grand Rapids, MI, 2022).
  18. G. Corsiglia, Designing Online Activities for Teaching Quantum Mechanics: A Research-Based Approach, Ph.D. thesis, University of Colorado - Boulder, 2023.
  19. H. R. Sadaghiani and S. J. Pollock, Quantum mechanics concept assessment: Development and validation study, Phys. Rev. ST Phys. Educ. Res. 11, 010110 (2015).
  20. E. Cataloglu and R. W. Robinett, Testing the development of student conceptual and visualization understanding in quantum mechanics through the undergraduate career, Am. J. Phys. 70, 238 (2002).
  21. A. Kohnle and E. Deffebach, Investigating student understanding of quantum entanglement, in Physics Education Research Conference Proceedings (2021).
  22. H. J. Hersom and B. M. Zwickl, Investigating students’ understanding of entanglement, in Physics Education Research Conference Proceedings (AAPT, Boston, MA, 2024).
  23. G. Passante, P. J. Emigh, and P. S. Shaffer, Student ability to distinguish between superposition states and mixed states in quantum mechanics, Phys. Rev. ST Phys. Educ. Res. 11, 020135 (2015).
  24. C. Singh and E. Marshman, Student difficulties with determining expectation values in quantum mechanics, in Physics Education Research Conference Proceedings (AAPT, Sacramento, CA, 2016).
  25. J. Meyer, New Paradigms in Quantum Education for the Second Quantum Revolution, Ph.D. thesis, University of Colorado - Boulder, 2025.
  26. M. Griston, J. Meyer, and B. Wilcox, Measuring student understanding in quantum computing: Development and validation of the quantum computing conceptual survey, to be published.
  27. R. E. Scherr, Modeling student thinking: An example from special relativity, Am. J. Phys. 75, 272 (2007).
  28. M. C. Wittmann and K. E. Black, Mathematical actions as procedural resources: An example from the separation of variables, Phys. Rev. ST Phys. Educ. Res. 11, 020114 (2015).
  29. J. P. Smith III, A. A. diSessa, and J. Roschelle, Misconceptions reconceived: A constructivist analysis of knowledge in transition, J. Learn. Sci. 3, 115 (1994).
  30. L. A. Barth-Cohen, H. Swanson, and J. Arnell, Methods of research design and analysis for identifying knowledge resources, Phys. Rev. Phys. Educ. Res. 19, 020119 (2023).
  31. A. D. Robertson, L. M. Goodhew, L. C. Bauman, B. Hansen, and A. T. Alesandrini, Identifying student conceptual resources for understanding physics: A practical guide for researchers, Phys. Rev. Phys. Educ. Res. 19, 020138 (2023).
  32. E. Kuo, M. M. Hull, A. Gupta, and A. Elby, How students blend conceptual and formal mathematical reasoning in solving physics problems, Sci. Educ. 97, 32 (2013).
  33. M. Eichenlaub and E. F. Redish, Blending physical knowledge with mathematical form in physics problem solving, in Mathematics in Physics Education (Springer, New York, 2019), pp. 127–151.
  34. J. C. Meyer, G. Passante, and B. Wilcox, Disparities in access to US quantum information education, Phys. Rev. Phys. Educ. Res. 20, 010131 (2024).
  35. M. Brang, H. Franke, F. Greinert, M. S. Ubben, F. Hennig, and P. Bitzenbauer, Spooky action at a distance? a two-phase study into learners’ views of quantum entanglement, Eur. Phys. J. Quantum Technol. 11, 33 (2024).
  36. F. Seifollahi and C. Singh, Preparing students for the quantum information revolution: Interdisciplinary teaching, curriculum development, and advising in quantum information science and engineering, Eur. J. Phys. 46, 055709 (2025).
  37. L. Doyle, F. Seifollahi, and C. Singh, Building bridges in quantum information science education: Expert insights to guide framework development for interdisciplinary teaching and evolution of common language, Eur. Phys. J. Quantum Technol. 13, 2 (2026).
  38. A. Buzzell, T. J. Atherton, and R. Barthelemy, Quantum mechanics curriculum in the U.S.: Quantifying the instructional time, content taught, and paradigms used, Phys. Rev. Phys. Educ. Res. 21, 010102 (2025).
  39. J. Kruse, M. Griston, and B. R. Wilcox, Characterization of upper-level undergraduate quantum mechanics courses in the U.S., Phys. Rev. Phys. Educ. Res. 21, 020162 (2025).

Outline

Information

Sign In to Your Journals Account

Filter

Filter

Article Lookup

Enter a citation