Chronic stress may have detrimental impacts on mental health as well as an elevated risk of various diseases. Stress monitoring might become simple with the help of artificial intelligence and quantum computing. The main goal of this work is to investigate how quantum enhanced machine learning algorithms can be used to forecast the stress levels of knowledge workers in the workplace using many modalities. This paper provides a high-level overview of the most well-known quantum augmented machine learning algorithms., such as Variational Quantum Classifier (VQC)., Quantum K-Nearest Neighbor (QKNN) and Quantum Support Vector Machine (QSVM)., after analysing the literature during the last ten years
Chronic stress may have detrimental impacts on mental health as well as an elevated risk of various diseases. Stress monitoring might become simple with the help of artificial intelligence and quantum computing. The main goal of this work is to investigate how quantum enhanced machine learning algorithms can be used to forecast the stress levels of knowledge workers in the workplace using many modalities. This paper provides a high-level overview of the most well-known quantum augmented machine learning algorithms., such as Variational Quantum Classifier (VQC)., Quantum K-Nearest Neighbor (QKNN) and Quantum Support Vector Machine (QSVM)., after analysing the literature during the last ten years
Prolonged stress can negatively impact a person's mental health leading to multiple diseases. Stress monitoring can be efficiently done with the help of artificial intelligence technology combined with the benefits of quantum computing. The main aim of the paper is to analyze quantum enhanced machine learning techniques in predicting the stress of knowledge workers at office through multiple modalities. A general overview of popular quantum enhanced machine learning methods such as Quantum Support Vector Machine (QSVM), Variational Quantum Classifier (VQC) and Quantum K-Nearest Neighbor (QKNN) methods has been presented after studying the literatures of past 10 years. Besides, these models have been implemented on multimodal SWELL-KW dataset, which contains knowledge worker's computer interaction, facial expressions, body postures, heart rate variability and skin conductance data recorded in various working conditions. Further, the impacts of Quantum Principal Component Analysis based feature reduction on their performances have been analyzed. Experimental results show that for the current dataset, QSVM model with PCA on heart rate variability and skin conductance data results in highest accuracy of 0.8.
Quantum Machine Learning (QML) is fast becoming a powerful instrument in medical diagnostics because it can process high-dimensional data and small samples efficiently. This paper is a detailed analysis of QML models used for disease prediction, with a relative comparison of Variational Quantum Classifiers (VQC), Quantum Convolutional Neural Networks (QCNN), and Quantum Support Vector Machines (QSVM) in clinical environments. We suggest a new multimodal quantumclassical hybrid approach that analyzes electronic health records (EHR), medical imaging, and biosignal data by means of dedicated QML pipelines with late fusion for ultimate decisionmaking. Recent IEEE literature is integrated in experimental performance glimpses to demonstrate the merits, difficulties, and viability of QML in real-world healthcare applications. The envisioned architecture provides the foundation for future applications of quantum-enhanced diagnostics with existing quantum simulators as well as emerging hardware.
Exposure to continuous stress can negatively impact a person's mental health. Stress monitoring plays a significant role to detect the stress level of an individual. The main goal of this work is to develop a quantum augmented deep learning model to support knowledge workers in the self management of their stress levels in the office. Quantum computing is a technological leap that is expected to provide us with the fastest computational power than today's classical processors. In this paper, a parameterized quantum circuit is augmented in the traditional Long Short Term memory (LSTM) model to enhance its learning capability and improve the predictions. The effectiveness of the proposed method is evaluated on various sensor data obtained from SWELL-KW [1] dataset. The time series dataset contains knowledge worker's computer interaction, facial expression, body postures and heart rate (variability) and skin conductance data recorded in varied working conditions. With improved learning ability, the proposed model has shown promising prediction ability on data obtained from all the modalities. For dataset in hand, the model with heart rate variability data resulted in maximum F1-score of 87.67%.
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.
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.
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.