The quantification of physical activity energy expenditure (PAEE) offers significant benefits for healthcare monitoring and has the potential to promote healthy and active aging for elderly individuals. With recent advancements in quantum information and computation, quantum machine learning (QML) has emerged as a powerful tool capable of improving upon the measurement of PAEE. In this study, we propose a hybrid machine-learning model to predict PAEE. This model specifically leverages a classical long short-term memory (LSTM) model integrated with a variational quantum circuit (VQC). This model, which we refer to as the enhanced quantum long short-term memory linear (eQLSTML) model, was subsequently trained and tested using the publicly available GOTOV Human Physical Activity and Energy Expenditure Dataset for Older Individuals. Upon performance comparisons between the classical LSTM and proposed eQLSTML models, our findings suggest that the eQLSTML modeling approach demonstrates superior performance compared to classical machine learning methods, thereby holding a promise for personalized healthcare monitoring and promoting healthy aging in the older population.
The quantification of physical activity energy expenditure (PAEE) offers significant benefits for healthcare monitoring and has the potential to promote healthy and active aging for elderly individuals. With recent advancements in quantum information and computation, quantum machine learning (QML) has emerged as a powerful tool capable of improving upon the measurement of PAEE. In this study, we propose a hybrid machine-learning model to predict PAEE. This model specifically leverages a classical long short-term memory (LSTM) model integrated with a variational quantum circuit (VQC). This model, which we refer to as the enhanced quantum long short-term memory linear (eQLSTML) model, was subsequently trained and tested using the publicly available GOTOV Human Physical Activity and Energy Expenditure Dataset for Older Individuals. Upon performance comparisons between the classical LSTM and proposed eQLSTML models, our findings suggest that the eQLSTML modeling approach demonstrates superior performance compared to classical machine learning methods, thereby holding a promise for personalized healthcare monitoring and promoting healthy aging in the older population.
Physical activity energy expenditure (PAEE) offers significant benefits for general healthcare monitoring and has the potential to promote healthy and active aging for elderly individuals. With recent advancements in quantum information and computation, quantum machine learning (QML) has emerged as a tool capable of improving upon the measurement of PAEE. In this paper, we propose a hybrid QML model to predict PAEE which consists of a classical long short-term memory (LSTM) model integrated with a variational quantum circuit (VQC). This model, which we refer to as the enhanced quantum long short-term memory linear (eQLSTML), was subsequently trained and tested using the publicly available GOTOV Human Physical Activity and Energy Expenditure Dataset for Older Individuals. In particular, we study the proposed eQLSTML model with different gate choices in the quantum circuit along with various embedding and layering techniques. Our results indicate our model to be superior in both performance comparisons and prediction when compared to traditional machine learning methods currently employed. Our findings indicate that combining QML approaches with wearable IoT healthcare devices provides a new avenue for personalized healthcare monitoring and an effective method for promoting healthy aging.
The rapid development and integration of wearable technologies or Internet-of-Things (IoT) into healthcare technologies with computational methods holds transformative potential for future healthcare systems, especially for personalized health monitoring and early disease detection. In parallel, recent advancements in quantum computing have o↵ered significant interest in quantum machine learning (QML), an intersection of quantum computing and machine learning, which provides a novel hybrid computational paradigm capable of addressing complex healthcare challenges that remain unsolvable with classical machine learning techniques. This thesis explores the application of quantum-enhanced recurrent neural network architectures, specifically Quantum Long Short-Term Memory (QLSTM) and Quantum-based Gated Recurrent Unit (QGRU), for predictive modeling and activity classification in wearable IoT systems, focusing particularly on their applicability and efficacy to elderly populations. One of the key challenges in modern healthcare intelligence systems is the simple but accurate estimation mechanism of Physical Activity Energy Expenditure (PAEE), which is an essential factor for assessing physical health status, particularly for healthy aging. In order to address this challenge, the first work proposes a model called an enhanced QLSTM with linear layer (eQLSTML) that integrates Variational Quantum Circuits (VQCs) into classical LSTM architectures. This integration notably explores the advantages of quantum computing, including properties such as entanglement and superposition, to enhance the model’s ability to capture complex temporal dependencies and subtle variations in human activity patterns. The modelling experiments were evaluated using the publicly available GOTOV Human Physical Activity dataset, which includes accelerometer data collected from older adults engaged in various daily activities. In addition to modelling architecture analysis, this thesis provides comprehensive analyses concerning the scalability, feasibility, and computational complexity of the proposed quantum-enhanced models compared to the classical backbone. The overall results demonstrate that the proposed quantum-enhanced approach significantly outperforms traditional classical machine learning algorithms, which demonstrates the potential ability for accurate predictions and robustness to noisy data like accelerometer data. In addition, accurate classification of activities of daily living (ADLs) is another critical aspect for early disease detection and e↵ective healthcare intervention. Within the context of this thesis, the second work introduces a novel hybrid model called QGRU-Multiclass Classifier (QGRU-MC). The data preprocessing involves creating a statistical feature extraction and applying oversampling techniques – SMOTE-ENN (Synthetic Minority Oversampling Technique with Edited Nearest Neighbors) for the publicly available dataset called “Dataset for ADL recognition with Wrist-worn Accelerometer”. The preliminary findings suggest that our model has good potential for healthcare applications, particularly in advancing future intelligent systems focused on daily activity monitoring. Specifically, QGRU-MC achieved significant improvements in classification accuracy, sensitivity, and specificity across multiple categories of daily activities, highlighting its potential for practical deployment in intelligent wearable systems aimed at monitoring and supporting healthy aging. Furthermore, this work also discusses the future research directions and practical considerations for transforming from the simulators to modern real quantum computers in the noisy intermediate-scale quantum (NISQ) era for a more comprehensive view on exploiting potential impacts on public health policies and personalized healthcare systems. Overall, simulator-based results indicate promising potential for integrating QML methods into wearable healthcare applications such as predictive analytics, personalized treatments, and health disease intervention to improve healthcare outcomes. Specifically, on PAEE regression, the proposed eQLSTML improved R² by 13.2% and reduced RMSE and MAE by 14.8% and 19.1%, respectively, relative to the classical LSTM; on ADL, QGRU-MC demonstrates good macro-averaged metrics across all eight classes (see Chs. 3–4). This research contributes significantly to the growing body of literature supporting quantum computing technologies in healthcare applications, making QML an incredible tool for addressing complex healthrelated computational challenges in the era of big and complex data in medical fields and smart healthcare technologies, especially in e↵ectively solving data from multiple sources, wearable accelerometry, and multi-class ADL recognition with complicated tasks.
Quantum computing combined with machine learning (ML) is a highly promising research area, with numerous studies demonstrating that quantum machine learning (QML) is expected to solve scientific problems more effectively than classical ML. In this work, we present Quantum Long Short-Term Memory (QLSTM), a QML architecture, and demonstrate its effectiveness in drug discovery. We evaluate QLSTM on five benchmark datasets (BBBP, BACE, SIDER, BCAP37, T-47D), and observe consistent performance gains over classical LSTM, with ROC-AUC improvements ranging from 3% to over 6%. Furthermore, QLSTM exhibits improved predictive accuracy as the number of qubits increases, and faster convergence than classical LSTM under the same training conditions. Notably, QLSTM maintains strong robustness against quantum computer noise, outperforming noise-free classical LSTM in certain settings. These findings highlight the potential of QLSTM as a scalable and noise-resilient model for scientific applications, particularly as quantum hardware continues to advance in qubit capacity and fidelity.
Abstract Quantum computing combined with machine learning (ML) is a highly promising research area, with numerous studies demonstrating that quantum machine learning (QML) is expected to solve scientific problems more effectively than classical ML. In this work, we present Quantum Long Short-Term Memory (QLSTM), a QML architecture, and demonstrate its effectiveness in drug discovery. We evaluate QLSTM on five benchmark datasets (BBBP, BACE, SIDER, BCAP37, T-47D), and observe consistent performance gains over classical LSTM, with ROC-AUC improvements ranging from 3% to over 6%. Furthermore, QLSTM exhibits improved predictive accuracy as the number of qubits increases, and faster convergence than classical LSTM under the same training conditions. Notably, QLSTM maintains strong robustness against quantum computer noise, outperforming noise-free classical LSTM in certain settings. These findings highlight the potential of QLSTM as a scalable and noise-resilient model for scientific applications, particularly as quantum hardware continues to advance in qubit capacity and fidelity.
In recent years, the electricity consumption in residential sector has witnessed a significant increase due to the population growth on one hand and the widespread adoption of electrical appliances on the other hand.Hence, finding solutions to decrease electricity consumption has become a matter of great interest for researchers.To address this challenge, we propose a Quantum Cognitive IoT (QCIoT) Framework that integrates quantum deep learning with edge computing to optimize energy use in smart homes.Our key innovation is a hybrid Quantum Long Short-Term Memory (QLSTM) model, which enhances traditional LSTM networks by leveraging quantum circuits for improved timeseries forecasting.Specifically, QLSTM employs parameterized quantum gates to process temporal dependencies more efficiently, enabling higher accuracy than classical approaches.We evaluate quantum-enhanced LSTMs (QLSTMs) against classical LSTM baselines on multivariate time-series forecasting.Experimental results demonstrate that QLSTMs significantly outperform classical counterparts, with the multivariate QLSTM (MQLSTM) achieving a 25.8% reduction in RMSE and improving explanatory power by 78.3%.While QLSTMs exhibit slightly slower convergence, they deliver superior generalization, evidenced by lower test loss and stable training dynamics.These advantages stem from quantum parallelism, entanglement and optimized state representation, which enable superior handling of noisy, high-dimensional smart home data.By integrating quantum-enhanced forecasting with edge-based IoT systems, our framework offers a scalable solution for real-time energy management in smart homes.This work bridges quantum computing and smart infrastructure, demonstrating practical benefits for sustainability and energy savings.
Works in quantum machine learning (QML) over the past few years indicate that QML algorithms can function just as well as their classical counterparts, and even outperform them in some cases. Among the corpus of recent work, many current QML models take advantage of variational quantum algorithm (VQA) circuits, given that their scale is typically small enough to be compatible with NISQ devices and the method of automatic differentiation for optimizing circuit parameters is familiar to machine learning (ML). While the results bare interesting promise for an era when quantum machines are more readily accessible, if one can achieve similar results through non-quantum methods then there may be a more near-term advantage available to practitioners. To this end, the nature of this work is to investigate the utilization of stochastic methods inspired by a variational quantum version of the long short-term memory (LSTM) model in attempt to approach the reported successes in performance and rapid convergence. By analyzing the performance of classical, stochastic, and quantum methods, this work aims to elucidate if it is possible to achieve some of QML’s major reported benefits on classical machines by incorporating aspects of its stochasticity.