Disparities in access to U.S. quantum information education

Driven in large part by the National Quantum Initiative Act of 2018, quantum information science (QIS) coursework and degree programs are rapidly spreading across U.S. institutions. Yet prior work suggests that access to quantum workforce education is unequally distributed, disproportionately benefiting students at private research-focused institutions whose student bodies are unrepresentative of U.S. higher education as a whole. We use regression analysis to analyze the distribution of QIS coursework across 456 institutions of higher learning as of Fall 2022, identifying statistically significant disparities across institutions in particular along the axes of institution classification, funding, and geographic distribution suggesting today’s QIS education programs are largely failing to reach low-income and rural students. We also conduct a brief analysis of the distribution of emerging dedicated QIS degree programs, discovering much the same trends. We conclude with a discussion of implications for educators, policymakers, and education researchers including specific policy recommendations to direct investments in QIS education to schools serving low-income and rural students, leverage existing grassroots diversity and inclusion initiatives that have arisen within the quantum community, and update and modernize procedures for collecting QIS educational data to better track these trends.

Figure 1

(a) Percentage of enrolled undergraduates from top 1% of family income distribution ($\ge \$630\mathrm{k}/\mathrm{yr}$) among institutions investigated in our study, by Carnegie classification and funding (public vs private). (b) Percentage of enrolled undergraduate student body in bottom 60% of family income distribution ($<\$65\mathrm{k}/\mathrm{yr}$) among institutions investigated in our study. Numbers taken from a 2017 study of anonymized tax data [60, 61] and are for domestic students in the age 18–22 bracket (typically first-time undergraduates). Data is for 1992 birth cohort (approximately class of 2013). Data available for 387 (85%) of the $N=456$ institutions investigated as part of this study. Red dots represent possible outlier institutions (percentage of students from respective income bracket differs from the median by more than 1.5 times the interquartile range, represented by the height of the blue rectangle).

Figure 2

Low- to moderate-income students attending private universities pay higher annual out of pocket costs. While private R1 institutions at first appear an exception—indeed, many are known for giving generous financial aid to the few low- to moderate-income students they do admit [62]—these institutions’ high selectivity makes them unreachable for all but the most academically accomplished low- and moderate-income students. (a) Annual net costs of attendance for low- to moderate-income undergraduates at median institution in classification (USD/yr) and (b) admissions selectivity (percent of undergraduate applicants admitted, 2021 cycle*) by funding and Carnegie classification. ^*Percent admitted is of the applicant pool, not the entire U.S. population; the application process itself—typically involving fees and extensive essays—already limits applicant pools at selective institutions only to students believing they have a reasonable probability of admission to begin with. ^†As represented by the green dots in (b), a few outlier private R1 institutions with relatively high acceptance rates do exist; however, such institutions generally have costs of attendance similar to those of private non-R1s. Notably, a student with a family income below $\$30\mathrm{k}/\mathrm{yr}$ will pay a median of $\$17500/\mathrm{yr}$ at private R1s with an admit rate above 20% vs. $\$5600/\mathrm{yr}$ at private R1s with an admit rate below 20%.

Figure 3

Mean number of courses offered at institution, by Carnegie classification, funding (private/public), and status as an URM-serving institution. The mean number of courses per institution across the entire dataset is $\mu =0.63$ (median 0), with a standard deviation of $\sigma =1.39$.

Figure 4

Flowchart illustrating the details of the regression methodology discussed in Sec. 3d of the main document. Here, $\overrightarrow{\theta }$ is a vector of independent variables representing institutional factors and $a_0$, ${\beta }_i$, ${\gamma }_{ij}$, and (for negative binomial models) $k_q$ are undetermined regression coefficients. Both the Poisson and negative binomial models model the expected mean number of counts $\mu$ and conditional variance ${\sigma }^2$ as a function of the independent variables $\overrightarrow{\theta }$. The simpler Poisson model is used unless a $t$ test indicates overdispersion.

Figure 5

Correlation matrix between independent variables considered in the regression model.