Investigation of factors regarding the effects of COVID-19 pandemic on college students' depression by quantum annealer

TL;DR

This study uses quantum annealing algorithms on D-Wave quantum computers to analyze mental health factors affecting college students before and after COVID-19, revealing shifts in factor importance.

Contribution

It introduces QA-based feature selection algorithms for mental health research, demonstrating their effectiveness compared to traditional models.

Findings

QA algorithms have comparable performance to MLR models.

Pandemic-related factors gained importance post-COVID-19.

Psychological factors like decision-making became more significant.

Abstract

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.