Accurate state-of-health (SOH) estimation for lithium-ion batteries remains a challenging problem due to complex electrochemical degradation mechanisms and long-range temporal dependencies. In this work, we propose a quantum-enhanced recurrent framework, termed QLSTM, in which variational quantum circuits are directly embedded into the gating mechanisms of long short-term memory networks. By replacing classical affine transformations with parameterized unitary operations, the proposed model introduces structured nonlinear transformations into the recurrent state-transition process. Extensive experiments on multiple benchmark battery datasets demonstrate that QLSTM consistently outperforms classical sequence models in both predictive accuracy and robustness, achieving significant reductions in mean absolute error (MAE), with improvements on the order of 20% compared with classical LSTM baselines. Ablation studies further confirm that these improvements arise primarily from quantum-enhanced gating rather than input-level transformations. Additional analyses on qubit scaling and noise robustness reveal that model performance is governed by a balance between expressive capacity and trainability. These results provide empirical evidence that embedding quantum computational primitives within recurrent architectures offers a structurally grounded approach to improving sequence modeling capability. The proposed framework establishes a new design paradigm for integrating quantum operators into temporal learning models, with potential applications in complex dynamical system prediction tasks.
Accurate state-of-health (SOH) estimation for lithium-ion batteries remains a challenging problem due to complex electrochemical degradation mechanisms and long-range temporal dependencies. In this work, we propose a quantum-enhanced recurrent framework, termed QLSTM, in which variational quantum circuits are directly embedded into the gating mechanisms of long short-term memory networks. By replacing classical affine transformations with parameterized unitary operations, the proposed model introduces structured nonlinear transformations into the recurrent state-transition process. Extensive experiments on multiple benchmark battery datasets demonstrate that QLSTM consistently outperforms classical sequence models in both predictive accuracy and robustness, achieving significant reductions in mean absolute error (MAE), with improvements on the order of 20% compared with classical LSTM baselines. Ablation studies further confirm that these improvements arise primarily from quantum-enhanced gating rather than input-level transformations. Additional analyses on qubit scaling and noise robustness reveal that model performance is governed by a balance between expressive capacity and trainability. These results provide empirical evidence that embedding quantum computational primitives within recurrent architectures offers a structurally grounded approach to improving sequence modeling capability. The proposed framework establishes a new design paradigm for integrating quantum operators into temporal learning models, with potential applications in complex dynamical system prediction tasks.
Accurate prediction of the State of Health (SoH) in Lithium-ion (Li-ion) batteries is critical for increasing Electric Vehicles (EV) performance and lifespan. However, capturing complex patterns remains challenging as traditional Machine Learning (ML) models struggle with non-linear and high-dimensional data. We propose a Variational Quantum Neural Network (VQNN) model that uses the principles of quantum mechanics, such as superposition, entanglement, and parallelism, to predict SoH. QML applications in EVs are scarce, and VQNN addresses this gap by applying quantum computing in battery SoH prediction. Our VQNN model achieves a Root Mean Squared Error (RMSE) of 0.0346 on the NASA B0005 and B0006 battery datasets, outperforming the LSTM model by 0.0017. However, the VQNN requires a longer training time of 8021.81 s compared to the Neural Network (NN) at 4.45 s and the Long Short Term Memory (LSTM) at 9.46 s. We demonstrate that Quantum Machine Learning (QML) can be used to capture complex degradation patterns, paving the way for quantum-driven Battery Management Systems (BMS) in connected EVs. Our findings suggest that despite the computational overheads, advancements in quantum hardware could enable real-time SoH prediction, enhancing EV reliability in the transport ecosystem.
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 machine learning (QML) offers new perspectives for modeling complex sequential data by leveraging the computational advantages of quantum computing. In this work, we explore the application of Quantum Long Short-Term Memory (QLSTM) networks for sequence prediction tasks. By integrating parameterized quantum circuits within the classical LSTM architecture, QLSTM models exploit quantum parallelism and entanglement to learn temporal dependencies in data more efficiently. We evaluate the performance of QLSTM on synthetic and real-world sequence prediction tasks and compare it with its classical counterpart. The results demonstrate that QLSTM models achieve competitive performance with fewer trainable parameters, indicating their potential as lightweight and efficient models for sequence learning and prediction.
Accurate prediction of the remaining useful life (RUL) of lithium-ion batteries is critical for enhancing the reliability and safety of battery-powered systems, especially in electric vehicles. While recent machine learning approaches such as Transformers and recurrent neural networks have shown promising results, their deployment in resource-constrained environments is limited due to high computational costs and large model sizes. In this paper, we propose a novel hybrid quantum-classical denoising Transformer (HQDeTransformer) architecture that integrates variational quantum circuits (VQCs) and quantum-enhanced attention mechanisms into a Transformer-based framework. The proposed HQDeTransformer employs a hybrid quantum denoising autoencoder to mitigate noise in capacity degradation sequences and utilizes hybrid quantum-classical neural networks to construct lightweight multi-head attention and feedforward blocks. Experimental evaluations on the NASA and CALCE benchmark datasets demonstrate that HQDeTransformer achieves superior performance compared to conventional machine learning models and quantum machine learning models, while reducing the number of trainable parameters. Furthermore, the use of angle-amplitude quantum embeddings and deeper quantum layers contributes to enhanced generalization and expressivity. These results highlight the potential of hybrid quantum-classical models for lightweight, accurate, and noise-resilient RUL prediction in practical battery management systems.
Energy storage devices such as lithium-ion batteries and supercapacitors are essential for portable electronics, electric vehicles, and renewable energy systems, yet their long-term reliability is limited by capacity degradation. Accurate prediction of state-of-health (SOH), state-of-charge (SOC), and capacity retention are therefore critical for extending device lifetimes and improving safety. Classical machine learning (ML) methods including LSTMs, CNNs, and ensemble approaches have achieved success in forecasting degradation trends, but they face limitations in scalability, data requirements, and capturing complex electrochemical behaviors. Quantum computing, and in particular quantum machine learning (QML), offers new opportunities by exploiting superposition and entanglement to process information more efficiently and compactly. This review surveys recent advances in applying QML to energy storage, with a focus on CQ approaches (classical data–quantum processing), distinguishing Hybrid-CQ architectures. Comparative analyses highlight trade-offs: CQ and Hybrid-CQ models achieve higher accuracy and parameter efficiency but are constrained by noise, limited qubits, and slower runtimes on real devices. Looking forward, integrating error-mitigation strategies, benchmarking on actual quantum hardware, and embedding physics-informed modeling are critical to closing the gap between theoretical promise and practical deployment. Beyond prediction tasks, quantum approaches hold potential for dynamic charging optimization, materials discovery, and quantum battery concepts. Collectively, these developments underscore how QML can complement and eventually surpass classical ML, paving the way toward more accurate, sustainable, and efficient energy storage systems.
Abstract Sarcasm detection is a challenging task in Natural Language Processing (NLP) due to its reliance on subtle and implicit linguistic cues that are often not explicitly expressed in text. Designing models capable of capturing such nuanced patterns remains an open problem. In this work, we investigate hybrid quantum–classical recurrent architectures for sarcasm detection by conducting a comparative study of classical and quantum-enhanced sequence models, including Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and their quantum counterparts, Quantum LSTM (QLSTM) and Quantum GRU (QGRU).We employ a pre-trained BERT model to extract contextualized token embeddings, which are subsequently compressed through classical neural layers to ensure compatibility with Noisy Intermediate-Scale Quantum (NISQ) devices. In the proposed quantum-enhanced models, classical linear transformations within recurrent gates are replaced by Variational Quantum Circuits (VQCs), enabling the exploitation of quantum properties such as superposition and entanglement within a hybrid learning framework.Experimental results on a balanced sarcasm-labeled dataset indicate that both QLSTM and QGRU achieve performance levels comparable to their classical counterparts, despite utilizing significantly fewer trainable parameters. Notably, the quantum-enhanced models require approximately 10% of the parameters used by their classical equivalents while maintaining similar macro F1 scores. These findings suggest that hybrid quantum recurrent architectures can serve as compact and parameter-efficient alternatives to classical sequence models, highlighting their potential applicability in quantum natural language processing under near-term hardware constraints.