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.
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.
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.
Timely clinical intervention at the early stage for detecting widespread psychological health issues such as depression and anxiety is essential. Conventional diagnostic approaches that are generally based on subjective questionnaires face limitations which can result in delays as well as imprecisions. Current data-driven methodologies employ features that are extracted from multiple sources (behavioural surveys, clinical attributes and physiological signals excluding EEG and ECG). Although known classical ML models such as RF and LR are effective, the limitations of these models on capturing extremely complex non-linear issues result in their performance being limited. This performance upper bound is validated by the combined classical benchmark accuracy of 86.59%. This paper introduces a new quantum hybrid support vector machine (QSVM) framework, and shows how it can dramatically improve prediction accuracy on such mental health conditions. The system uses a hybrid design, in which the classical processing part computes and sends out a processed feature map to be quantum mapped into a Q exponentially larger-dimensional Hilbert space through quantum kernel methods (Quantum Feature Maps). By taking advantage of the quantum effects, the QSVM implicitly computes kernels that are difficult for classical machines and allows achieving better classification boundaries. The accuracy of QSVM is critically evaluated against the RF and LR benchmarks. Thus The Hybrid QSVM shows preliminary signs of quantum advantage, particularly in precision and kernel separability, although accuracy remains comparable to classical baselines. Validation on real quantum hardware will be required to establish practical quantum advantage.
Brain imaging is a critical tool in mental health diagnostics, particularly for detecting anomalies associated with behavioral disorders, anxiety, depression, and neuropsychiatric conditions. Traditional deep learning approaches, such as Convolutional Neural Networks (CNNs), have shown promise in analyzing functional Magnetic Resonance Imaging (fMRI), but they often suffer from computational limitations, feature extraction inefficiencies, and generalization issues. Quantum Machine Learning (QML) offers a transformative solution by enhancing feature representation, improving classification accuracy, and exploiting the power of quantum computation for large-scale medical imaging datasets. In this paper, we explored the potential of quantum models for detecting anomalies in brain imaging. Using the UCLA Consortium for Neuropsychiatric Phenomics dataset, we compared quantum models, including the Variational Quantum Classifier (VQC), Quantum Convolutional Neural Network (QCNN), and Quantum Support Vector Machine (QSVM), with classical models like CNNs and the Support Vector Machine (SVM). Our results revealed that QSVM and VQC outperformed classical models, both achieving 92% accuracy and perfect recall (100%), making them highly effective at identifying positive cases-a critical requirement in clinical diagnostics. QCNN, while less accurate at 74%, still showed strong precision (90%) and an F1score of 85%, highlighting its potential where avoiding false positives is key. Classical models, in comparison, performed moderately well: SVM reached 87% accuracy and CNN achieved 83%, but both had lower precision and F 1 -scores, suggesting less consistent performance. These findings suggest that quantum models, especially VQC and QSVM, offer a promising advantage in neuroimaging-based mental health diagnostics.
Mental health disorders represent a growing global healthcare challenge affecting hundreds of millions of people worldwide. Major depressive disorder (MDD), anxiety disorders, post-traumatic stress disorder (PTSD), and Alzheimer’s disease place huge pressure on medical systems. A central challenge is early detection, which remains difficult due to the complexity of symptoms related to these diseases. Delayed diagnosis is also due to limited access to specialists and the multimodal complexity of relevant biomarkers, clinical assessments, neuroimaging, physiological signals, and behavioral data. Recent developments in artificial intelligence (AI) have demonstrated promising results in improving diagnostic support. However, classical AI still struggles to model high-dimensional correlations and inherent uncertainties that are common to cognitive-emotional data. This chapter explores quantum machine learning (QML) as a transformative paradigm to overcome these limitations. We provide a comprehensive overview of foundational QML architectures and evaluate its suitability for processing diverse healthcare data such as neuroimaging, wearable sensor inputs, and electronic health records (EHRs). We propose a new conceptual framework for a quantum-enabled clinical decision support system that integrates multimodal feature extraction. Taking into consideration the sensitivity of psychiatric data, we critically examine security imperatives that include federated learning and post-quantum privacy protection. This chapter aims to provide researchers and practitioners with a comprehensive perspective on how quantum computing (QC) may contribute to future intelligent mental healthcare.
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.
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).