Today’s interdisciplinary quantum information classroom: Themes from a survey of quantum information science instructors

Interdisciplinary introduction to quantum information science (QIS) courses are proliferating at universities across the US, but the experiences of instructors in these courses have remained largely unexplored in the discipline-based education research (DBER) communities. Here, we address this gap by reporting on the findings of a survey of instructors teaching introduction to QIS courses at institutions across the U.S., primarily at the undergraduate or hybrid undergraduate and graduate level, as well as follow-up focus interviews with six individual instructors. Key themes from this analysis include challenges and opportunities associated with the diversity of instructor and student backgrounds, student difficulties with the mathematical formalism (especially though not exclusively with linear algebra), and the need for better textbooks and curricular materials. We also find that while course topics are ostensibly similar, each course is crafted by its instructor to tell a different story about QIS and to uniquely balance goals such as accessibility and academic rigor, such that no canonical introduction to QIS course emerges from our dataset. We discuss the implications of this finding with regard to the benefits and risks associated with standardization of curricula as QIS coursework matures.

Figure 1

Venn diagram illustrating the academic departments in which the 28 surveyed courses are (or are intended to be, in the case of FR-23) listed or cross listed. Physics includes engineering physics. Where course catalogs differentiate between subfields of engineering, courses classified here as engineering were listed in either electrical engineering or electrical and computer engineering.

Figure 2

Estimated enrollment profile of each of the 27 previously taught courses. Each circle represents 5% of students in the course. Solid circles represent students in a major that corresponds to one of the listed departments for the course. Hollow circles represent students in a major that does not correspond to one of the listed departments for the course. Courses A–F that are the subject of follow-up interviews are labeled as such. Dagger ($†$) indicates that instructor estimates were renormalized by our team to sum to 100%.

Figure 3

Estimated enrollment profile by year for each previously offered course ($N=27$), rounded to the nearest 5% for each level, based on faculty responses. Courses are broken out by level classification based on university course number and show a close correspondence with this classification. Courses that are the subject of follow-up interviews are labeled A–F in red accordingly. Hollow symbols correspond to students outside the target audience listed in the course catalog.

Figure 4

Required prerequisite courses for each introduction to QIS course, as reported by the $N=28$ instructors (we include FR-23 since he has already decided upon prerequisites for his course). Note that (b) and (c) list only the highest required prerequisite for each course. By contrast, (a) lists all required courses above calculus, since math course sequences are not typically standardized across universities beyond calculus. (a) All required math preparation at or beyond calculus level; some courses require more than one math prerequisite. Discrete math includes number theory and/or related topics. Advanced calculus includes multivariable calculus, differential equations, and/or related topics. (b) Highest required physics preparation beyond high school level. Intro physics or quantum mechanics prerequisite may be 1 or 2 terms depending on the course and institutional calendar. (c) Highest required computer science preparation. Basic familiarity indicates informal experience with programming but not necessarily in a classroom setting. Beyond intro courses refers to any courses following the first semester of introductory computer science for technical majors, and can include topics such as algorithms or data structures.

Figure 5

Number of survey respondents expressing interest in implementing various proposed PER curricular instruments in their classrooms ($N=28$, instructors allowed to select any number of responses).