The study introduces an advanced quantum learning framework for biosignal processing that combines Quantum Reservoir Computing (QRC), Quantum Graph Neural Networks (QGNNs), quantum autoencoders and transfer learning. Previous models such as quEEGNet utilized variational quantum circuits in hybrid frameworks but struggled with complex training procedures and poor adaptability across different subjects. To solve existing problems the model utilizes QRC for temporal biosignal dependencies while QGNNs model spatial correlations between EEG/EMG electrodes and transfer learning addresses subject variability. The new quantum autoencoder model is designed to reduce data dimensionality and remove noise from biosignal datasets while making generalization more robust and inference more stable. The new architecture surpasses traditional CNN-LSTM and SVM models as well as hybrid quEEGNet models and achieves performance metrics of 89.3% EEG accuracy, 87.9% EMG accuracy, and 85.7% ECoG accuracy. The system showed strong performance in noisy environments at 5 dB SNR and needed just 10% of additional subject data to achieve good transfer learning adaptation. Quantum machine learning for biosignal analytics now achieves unprecedented standards in real-time performance while providing interpretability and scalability.
The study introduces an advanced quantum learning framework for biosignal processing that combines Quantum Reservoir Computing (QRC), Quantum Graph Neural Networks (QGNNs), quantum autoencoders and transfer learning. Previous models such as quEEGNet utilized variational quantum circuits in hybrid frameworks but struggled with complex training procedures and poor adaptability across different subjects. To solve existing problems the model utilizes QRC for temporal biosignal dependencies while QGNNs model spatial correlations between EEG/EMG electrodes and transfer learning addresses subject variability. The new quantum autoencoder model is designed to reduce data dimensionality and remove noise from biosignal datasets while making generalization more robust and inference more stable. The new architecture surpasses traditional CNN-LSTM and SVM models as well as hybrid quEEGNet models and achieves performance metrics of 89.3% EEG accuracy, 87.9% EMG accuracy, and 85.7% ECoG accuracy. The system showed strong performance in noisy environments at 5 dB SNR and needed just 10% of additional subject data to achieve good transfer learning adaptation. Quantum machine learning for biosignal analytics now achieves unprecedented standards in real-time performance while providing interpretability and scalability.
In this paper, we introduce an emerging quantum machine learning (QML) framework to assist classical deep learning methods for biosignal processing applications. Specifically, we propose a hybrid quantum-classical neural network model that integrates a variational quantum circuit (VQC) into a deep neural network (DNN) for electroencephalogram (EEG), electromyogram (EMG), and electrocorticogram (ECoG) analysis. We demonstrate that the proposed quantum neural network (QNN) achieves state-of-the-art performance while the number of trainable parameters is kept small for VQC.
The growing need for intelligent systems, which can operate in heterogeneous areas, indicates the limitations of traditional transfer learning methods, especially with regard to processing high-dimensional data and operating in real-time conditions. This paper presents a new cross-domain transfer learning system based on Quantum Neural Networks (QNNs) to be used in multi-task applications with real-time adaptation, to facilitate scalable and efficient knowledge transfer across domains, including, but not limited to, healthcare, IoT, and cybersecurity. The suggested architecture is based on a hybrid quantum-classical architecture, where parameterized quantum circuits are used to represent the features, domain-invariant learning and multi-task optimization strategies are applied, and a reinforcement learning-based module is used to provide continuous real-time adaptation by using streaming data. Experimental findings show that the model has 94.8% accuracy, which is significantly higher than the current classical and transformer-based methods, and that the adaptation latency of the model is 95 ms, and the transfer efficiency is more than 10 times higher, which means that generalization and robustness are higher. The results prove that quantum-enhanced transfer learning is a powerful and scalable solution in next-generation intelligent systems, with significant practical impact in real-time systems, such as smart healthcare, cybersecurity, and IoT systems.
Quantum Machine Learning (QML) has emerged as a transformative paradigm that integrates the computational advantages of quantum computing with the adaptive intelligence of machine learning to solve complex optimization problems in smart computing systems. Traditional optimization techniques often struggle with high-dimensional search spaces, combinatorial complexity, computational overhead, and scalability limitations in applications such as intelligent transportation, healthcare analytics, smart manufacturing, cybersecurity, cloud computing, and Internet of Things (IoT) infrastructures. QML approaches exploit quantum superposition, entanglement, and quantum parallelism to accelerate optimization tasks and enhance predictive performance beyond classical machine learning capabilities. This research proposes a hybrid Quantum Machine Learning optimization framework that combines Variational Quantum Circuits (VQCs), Quantum Neural Networks (QNNs), and classical deep learning models to address resource allocation, task scheduling, energy optimization, and intelligent decision-making in smart computing environments. The proposed framework integrates quantum-enhanced feature extraction, adaptive optimization layers, and reinforcement-based learning mechanisms to improve convergence speed and computational efficiency. Experimental analysis is performed using benchmark optimization datasets and simulated quantum environments to evaluate accuracy, execution time, energy efficiency, and scalability. Comparative results demonstrate that the proposed QML-based framework achieves superior optimization accuracy, reduced computational latency, and improved resource utilization when compared with traditional machine learning and classical metaheuristic optimization methods. Furthermore, the study highlights the practical significance of hybrid quantum-classical architectures in next-generation intelligent systems where large-scale optimization and real-time decision support are critical. The findings suggest that QML can significantly contribute to the advancement of sustainable, adaptive, and high-performance smart computing ecosystems while opening new directions for future quantum-aware intelligent infrastructures.
The pharmaceutical industry faces mounting challenges in drug discovery, where exploring a chemical space exceeding 10 60 possible compounds remains computationally prohibitive. Traditional in-silico screening approaches struggle to capture the quantum mechanical interactions that determine molecular behavior. To overcome these limitations, this study introduces a hybrid quantum–classical machine learning framework that uniquely integrates molecular graph theory with quantum-enhanced learning for accelerated drug discovery. Using the QM9 benchmark dataset containing 133,885 molecules of low-to-medium structural complexity, we extract Laplacian eigenvalue spectra as topological descriptors that preserve both connectivity and electronic information. These features are jointly processed by Random Forest (RF), Support Vector Machine (SVM), and a Variational Quantum Classifier (VQC) in a weighted ensemble architecture designed for balanced generalization. The Quantum Graph Neural Network (QGNN) component achieves 97.5% accuracy, while the hybrid ensemble attains 100.0% accuracy, confirming strong predictive reliability. Although quantum training requires roughly 3.2× longer computation, it captures high-dimensional correlations that classical methods overlook. Overall, the proposed framework bridges the gap between theoretical quantum computing and practical pharmaceutical modeling, providing a scalable, physics-inspired pathway toward intelligent molecular screening and next-generation drug discovery systems.
Forecasting neural activity from short recordings remains a fundamental challenge. Reservoir computing may offer an efficient paradigm for temporal prediction, however classical reservoirs typically underperform in small-data regimes. Here we investigate whether quantum reservoir computing (QRC) can help overcome this limitation. Building on recent advances, we introduce a quantum reservoir based on a transverse-field Ising model, combined with heterogeneous quantum measurements and polynomial ridge regression. On a standard benchmark task, results show that the quantum reservoir outperforms a classical counterpart overall, with prediction accuracy strongly dependent on reservoir parameters. We further demonstrate feasibility by running the same task on quantum hardware. To assess performance on biological signals, we evaluate QRC on simulated human electroencephalography (EEG) data with a parallel reservoir architecture. On this challenging task, the tested quantum reservoir did not match the performance of the classical one, but it produced stable, convergent predictions. This is a meaningful first step toward forecasting of biologically realistic neural data using a quantum reservoir. Overall, our findings indicate that although current quantum hardware and parallel reservoir architectures do not yet surpass classical methods on complex neural signals, QRC can be executed on near-term devices and does converge with realistic EEG-like data. This work establishes a practical baseline for future algorithmic and hardware developments aimed at clinical time-series forecasting with quantum systems.
Electroencephalography (EEG) is widely used in neuroscience and clinical research for analyzing brain activity. While deep learning models such as EEGNet have shown success in decoding EEG signals, they often struggle with data complexity, inter-subject variability, and noise robustness. Recent advancements in quantum machine learning (QML) offer new opportunities to enhance EEG analysis by leveraging quantum computing’s unique properties. In this study, we extend the previously proposed Quantum-EEGNet (QEEGNet), a hybrid neural network incorporating quantum layers into EEGNet, to investigate its generalization ability across multiple EEG datasets. Our evaluation spans a diverse set of cognitive and motor task datasets, assessing QEEGNet’s performance in different learning scenarios. Experimental results reveal that while QEEGNet demonstrates competitive performance and maintains robustness in certain datasets, its improvements over traditional deep learning methods remain inconsistent. These findings suggest that hybrid quantum-classical architectures require further optimization to fully leverage quantum advantages in EEG processing. Despite these limitations, our study provides new insights into the applicability of QML in EEG research and highlights challenges that must be addressed for future advancements.