Diverse cases regarding the impact, with its related factors, of the COVID-19 pandemic on mental health have been reported in previous studies. In this study, multivariable datasets were collected from 751 college students who could be easily affected by pandemics 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. Although the results should be validated using other mental health variables or national datasets, this study will serve as a reference for researchers regarding the use of the quantum annealing approach in factor analysis with validation through real-world survey dataset analysis.
Diverse cases regarding the impact, with its related factors, of the COVID-19 pandemic on mental health have been reported in previous studies. In this study, multivariable datasets were collected from 751 college students who could be easily affected by pandemics 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. Although the results should be validated using other mental health variables or national datasets, this study will serve as a reference for researchers regarding the use of the quantum annealing approach in factor analysis with validation through real-world survey dataset analysis.
Social media has become a significant resource for evaluating public attitudes regarding mental health, as individuals may express their thoughts through postings, comments, and conversations. Sentiment analysis is a promising method for studying emotional patterns expressed in mental-health-related text. Traditional machine learning for sentiment analysis faces challenges like transparency, scarce labeled data, and language limitations. This paper presents a sentiment classification (positive, negative, neutral) framework applied to mental-health-related Reddit posts. Repeated emotional patterns can offer helpful insights into sentiment trends in mental health discussions, but they shouldn’t be seen as proof of clinical disorders. In this paper, we propose a hybrid framework for sentiment classification from text, termed QBERT-LSTM, which integrates quantum-enhanced bidirectional encoder representations from transformers (QBERT) with long short-term memory (LSTM) networks. In the proposed approach, transformer models excel at capturing global context, while the LSTM enhances sequential patterns and temporal features. The quantum component uses a parameterized circuit with angle encoding and variational gates to perform nonlinear transformations on a quantum simulator (PennyLane). Subsequently, the model is tested on more intricate circuits comprising 2, 4, 6, 8, 10, and 12 qubits to assess its consistency. A novel Reddit dataset of mental health-related subreddits (anxiety, panic attacks, and depression) is constructed, with sentiment labels derived from the VADER sentiment analyzer. We thoroughly assessed the performance of our proposed model against deep learning models and cutting-edge transformer architectures, achieving up to 98% accuracy in the best-performing configuration on the generated dataset (mean accuracy 0.97±0.0059 across five random seeds).
The increasing cases of mental illnesses such as stress, anxiety, and depression, driven by excessive workload, social pressure, and emotional instability, highlight the need for early and accurate detection. Traditional Machine Learning (ML) and Deep Learning (DL) models were initially used for this purpose, as they can analyse psychological datasets like DASS-42 and provide automated mental health assessment. However, these classical models face challenges in capturing complex emotional patterns and high-dimensional feature interactions, which limits their overall diagnostic efficiency. To overcome these limitations, Quantum Machine Learning (QML) was introduced. In this article, we applied the Variational Quantum Classifier (VQC) model that leverages quantum properties such as superposition and entanglement, allowing it to process and distinguish subtle mental health indicators more effectively than ML and DL approaches. The Quantum model (VQC) outperformed all classical approaches, delivering an exceptional accuracy of 99.95%, with precision, recall, and F1-score values nearly reaching 1.0, indicating highly reliable classification consistency. A comparative evaluation revealed that the quantum model demonstrated superior classification capabilities, outperforming classical systems in consistency and predictive reliability. This work establishes QML as a promising direction toward next-generation mental health diagnosis systems.
Disruptions of university campuses caused by COVID-19 have motivated strategies to prevent the spread of infectious diseases while maintaining some level of in person learning. In response, the proposed approach recursively applied a quantum annealing algorithm for Max-Cut optimization on D-Wave Systems, which grouped students into cohorts such that the number of possible infection events via shared classrooms was minimized. To test this approach, available coursework data was used to generate highly clustered course enrollment networks representing students and the classes they share. The algorithm was then recursively called on these networks to group students, and a disease model was applied to forecast disease spread. Simulation results showed that under some assumptions on disease statistics and methods of spread, the quantum grouping method reduced both the total and peak percentage of infected students when compared against random groupings of students. Scaling to larger networks, it is possible that this quantum annealer-assisted grouping approach may provide practical advantage over classical approaches. This paper, however, is strictly a proof-of-concept demonstration of the approach and is not intended to argue for a quantum speedup.
Globally, mental health diseases have a substantial influence on people, necessitating the urgent need for novel and precise diagnostic techniques. The power of quantum machine learning (QML) classifiers to accurately predict mental health disorders is examined in this study. QML promises promising improvements over conventional techniques by utilizing the special powers of quantum computing, such as increased processing speed and the capacity to handle complicated and highdimensional information. The study compares the effectiveness of classical algorithms with quantum classifiers, including quantum support vector machines and quantum neural networks. Through the use of several real-world datasets that include demographic, behavioral, and diagnostic variables, the study investigates the classifiers' accuracy, scalability, and noise resistance. The findings show that QML classifiers are particularly good at spotting complex and nuanced patterns in mental health data. Potential solutions are addressed along with hardware limits and quantum noise challenges. This study opens the door for further advancements in the field by demonstrating the potential of quantum technologies to support data-driven, individualized mental health care.
Mental health has a deep effect on human health affecting individuals in physically, emotionally and socially. Poor mental health can lead to numerous physical, emotional and behavioral consequences which can significantly impact overall health and quality of life. So, Mental health disorders (MHD) prediction is essential in the field of research for early identification and intervention, improving treatment efficacy, reducing stigma, improving quality of life, addressing Comorbidities, etc. The Mental Health Disorders (MHD) can be tracked using a range of methods including: Quantum Computing (QC), Federated Learning (FL), Machine Learning (ML), Deep Learning (DL), and Edge Computing. In this study we focus on the use of Quantum ML (QML) to predict MHD as Anxiety (AX), Depression (DP), Loneliness (LL), Stress (ST), or Normal (NR). For this study we will evaluate three models: Quantum Gradient Boosting (QGB); Quantum Support Vector Machine (QSM); and Hybrid Quantum Classical Model (HQM). Each model will be compared based on Accuracy (A in %); Precision (P in %); Recall (R in %); F1 Score(F1S in %); Training time (TT in secs); Memory Usage (M in MB); & Model Size (MS in KB) with 80:20, 70:30, & 60:40 Training Testing Ratios (TTR in \%) before and after applying Grid Search (GS) based Hyperparameter (HP) Tuning (HPT) optimization to identify if there is a statistically significant improvement in performance. Additionally, the receiver operating characteristic (ROC) curve will provide TPR & FPR for each model; while Boxplot & Heatmaps will visually represent these parameters. Analysis of results show that the performance of HQM is higher on average (A, P, R, F1S) than A, P and TT with averages of 94.1, 94.0, 94.1, 94.2 and 4.49 for both HQM before and HQM after HPT testing. However, QSM performs better in terms of M and MS with values 1274.41 and 88.54, and 1269.36 and 80.53 accordingly.
Stress, is a psychological experience representing the body's response to immune system activation and perceived danger, has emerged as a critical issue, particularly amid the global spread of Covid-19. While various techniques such as PET, ECG, EMG, and MRI exist for stress detection, this paper focuses on leveraging electroencephalography (EEG) signals within a quantum computing framework. We present a comparative analysis of stress detection methods utilizing EEG signals, with a particular emphasis on quantum-inspired approaches. Furthermore, we propose a real-time neurofeedback solution based on quantum computing principles to offer a quantitative assessment of stress levels. By integrating quantum computing concepts into stress detection and management, our research not only contributes to personal health but also extends to applications in stress-related mental health and clinical knowledge. Moreover, it advances the field of neuroethology by exploring the intersection of quantum technology and cognitive health.