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.
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.
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.
The increasing prevalences of sleep disorders such as insomnia and sleep apnea has become most significant global health concern. Conventional machine learning (ML) models often struggle to deliver very high predictive accuracy or to capture complex and non-linear relationships embedded within multifactorial sleep and lifestyle datasets. This study explores a novel solution by applying the Quantum Machine Learning techniques, specifically the Quantum Support Vector Machine (QSVM) and the Variational Quantum Classifier (VQC), to the Sleep Health and Lifestyle Dataset. The dataset incorporates variables such as sleep duration, physical activity level, stress level, and cardiovascular metrics. Results reveal a clear quantum advantage, with overall accuracy improved by +0.0827 (an 8.27% increase), as the QSVM achieved 0.8100 compared to the traditional classical SVM’s 0.7273. Notably, the QSVM demonstrated superior performance in classifying ambiguous cases, raising the F1-score for the "None" class by 15.4%. These findings highlight Quantum Machine Learning as a robust, data driven avenues for enhancing early diagnosis and personalised intervention strategie.
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.
In this chapter the possibility of Quantum Deep Learning as a tool to strengthen the diagnosis of mental disorders, with a particular focus on anxiety and depression, based on EEG data analysis is constructed. The conventional approach of diagnosing mental health disorders is considerably driven by structured interviews and is often very constrained due to variance in symptoms in individuals. As there are advances in mental health studies, EEG has proved useful in recording brain such activity which coincides with emotional and cognitive processes in the study subjects. The problem lies in the fact that EEG signals are complex, high dimensional data and as such they do not temporal and signal-to-noise ratio required for machine learning paradigm to database features. This work applies quantitative deep learning (QDL) to the DEAP dataset to overcome these hurdles, which includes EEG and raw physiological responses of participants watching video clips with emotional content. It utilizes QDL algorithms with emotional states that are classified with the aid of EEG.
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.