Investigation of factors regarding the effects of COVID-19 pandemic on
  college students' depression by quantum annealer
Junggu Choi Kion Kim Soohyun Park Juyoen Hur Hyunjung Yang Younghoon Kim Hakbae Lee Sanghoon Han
Diverse cases regarding the impact, with its related factors, of the COVID-19
pandemic on mental health have been reported in previous studies. College
student groups have been frequently selected as the target population in
previous studies because they are easily affected by pandemics. In this study,
multivariable datasets were collected from 751 college students based on the
complex relationships between various mental health factors. We utilized
quantum annealing (QA)-based feature selection algorithms that were executed by
commercial D-Wave quantum computers to determine the changes in the relative
importance of the associated factors before and after the pandemic.
Multivariable linear regression (MLR) and XGBoost models were also applied to
validate the QA-based algorithms. Based on the experimental results, we confirm
that QA-based algorithms have comparable capabilities in factor analysis
research to the MLR models that have been widely used in previous studies.
Furthermore, the performance of the QA-based algorithms was validated through
the important factor results from the algorithms. Pandemic-related factors
(e.g., confidence in the social system) and psychological factors (e.g.,
decision-making in uncertain situations) were more important in post-pandemic
conditions. We believe that our study will serve as a reference for researchers
studying similar topics.