Radio frequency fingerprint identification (RFFI) is an emerging physical-layer method for authenticating devices through their unique hardware impairments. Deep learning (DL) is widely used to identify devices based on their signal transmissions. However, training DL models is resource-intensive because it requires repeated updates to numerous parameters, which becomes particularly problematic in resource-constrained environments. In this paper, we propose quantum-assisted training (QAST), a framework that addresses training inefficiencies in RFFI systems. QAST integrates a quantum neural network with a classical neural network to generate parameters for a DL model. Compared to traditional training methods, this indirect training strategy substantially decreases the number of trainable parameters, thereby mitigating the overall computational and resource demands. Experimental results show that QAST enables training an RFFI model with 90% fewer trainable parameters than traditional training approaches, while maintaining comparable classification accuracy.
Radio frequency fingerprint identification (RFFI) is an emerging physical-layer method for authenticating devices through their unique hardware impairments. Deep learning (DL) is widely used to identify devices based on their signal transmissions. However, training DL models is resource-intensive because it requires repeated updates to numerous parameters, which becomes particularly problematic in resource-constrained environments. In this paper, we propose quantum-assisted training (QAST), a framework that addresses training inefficiencies in RFFI systems. QAST integrates a quantum neural network with a classical neural network to generate parameters for a DL model. Compared to traditional training methods, this indirect training strategy substantially decreases the number of trainable parameters, thereby mitigating the overall computational and resource demands. Experimental results show that QAST enables training an RFFI model with 90% fewer trainable parameters than traditional training approaches, while maintaining comparable classification accuracy.
Radio frequency fingerprint identification (RFFI) offers a promising physical-layer method to authenticate devices based on unique hardware impairments. However, existing RFFI systems use deep learning (DL) models that are resource- intensive. Training is particularly demanding, requiring repeated updates to a large number of parameters. In this paper, we introduce a quantum-assisted training (QAST) framework to address training inefficiencies in RFFI. QAST integrates a quantum neural network (QNN) with a mapping network to generate parameters for a classical DL model. This indirect training strategy substantially reduces the number of trainable parameters and the overall memory requirements compared to direct training of the DL model. We achieve this by introducing a multimodal mapping network that effectively learns the QNN output. This network generates multiple classical parameters from a shared quantum representation, thereby reducing qubit requirements and lowering the risk of barren-plateau-related trainability degradation. We also propose a new embedding method that reduces the size of the embedding matrix and yields a 15% to 30% reduction in training time. The tailored QAST framework trains the RFFI model while requiring only 10% of the original number of parameters while maintaining comparable accuracy, thereby substantially reducing memory and computational overhead and enabling efficient training or retraining in resource-limited environments.
Wi-Fi-based human activity recognition (HAR) has become an important part of integrated sensing and communications, paving the way for a range of context-aware services. However, most existing Wi-Fi-based HAR systems rely on deep learning (DL) models that are computationally and memory intensive in both training and inference, which poses significant challenges for real-world deployment. Conventional training requires simultaneous updates of millions of parameters, leading to prohibitive memory consumption. In this paper, we propose a novel quantum-assisted memory-efficient training framework (Q-MET) designed to improve efficiency in both training and inference. Q-MET utilizes a hybrid quantum classical neural network to indirectly generate parameters for HAR models, significantly reducing the trainable parameter count compared to direct optimization. To further support the deployment on resource-constrained devices, we integrate structured pruning during the training phase. Experimental results demonstrate that Q-MET achieves a 90% to 95% reduction in trainable parameters compared with conventional backpropagation-based DL training while maintaining or even exceeding classical classification accuracy. Additionally, Q-MET supports lightweight inference through structured pruning, achieving 75% to 85% model sparsity with less than 2% loss in classification accuracy. To the best of our knowledge, this work represents the first quantum-assisted approach to simultaneously tackle memory inefficiencies in both the training and inference stages of HAR systems.
Rydberg-atom sensors have emerged as a promising quantum technology for broadband radio-frequency detection owing to their intrinsic calibration, wide frequency coverage, and high sensitivity. However, their potential for intelligent identification of real-world wireless signals remains largely unexplored. Here, we demonstrate a quantum-enabled framework for mobile signal classification based on Rydberg electromagnetically induced transparency spectroscopy and deep learning. Three types of wireless emissions, including cellular voice call, Wi-Fi, and 5G signals from three commercial smartphone brands, are measured to construct a spectral-fingerprint dataset. A convolutional neural network trained on this dataset achieves an initial baseline classification accuracy of 93.44\%. To compensate for spectral drifts induced by temporal and environmental variations, a progressive fine-tuning strategy is employed to continuously adapt the pretrained model, enabling robust classification across diverse deployment scenarios. Furthermore, continuous real-time monitoring is demonstrated under dynamically varying wireless conditions, including cellular voice call, Wi-Fi, and their coexistence. The proposed system achieves an overall frame-level recognition accuracy of 87.67\% over a 60-s measurement. These results establish a pathway toward intelligent quantum radio-frequency sensing and highlight the potential of Rydberg-atom platforms for real-time wireless signal awareness and spectrum monitoring.
This paper presents an approach for enhancing indoor localization accuracy using a hybrid quantum deep neural network model (H-QDNN). To improve the accuracy of indoor localization based on contemporary techniques, we employ the combined strengths of quantum computing (QC) and deep neural networks (DNN). The strengths of QC, which accelerates the training process and enables efficient handling of complex data representations through quantum superposition and entanglement, were combined with DNN, known for its ability to extract meaningful features and learn complex patterns from data. The proposed model can be trained using small datasets, reducing the need for extensive data, particularly useful in indoor localization, where data collection can be time-consuming and resource-intensive. To evaluate the effectiveness of our proposed approach, we conduct extensive experiments and comparisons with existing state-of-the-art methods. The results demonstrate that the H-QDNN model significantly improves indoor localization accuracy compared to traditional techniques. Additionally, we provide insights into the factors contributing to enhanced performance, such as the quantum-inspired algorithms utilized and the integration of mixed fingerprints.
The advent of deep-learning technology promises major leaps forward in addressing the ever-enduring problems of wireless resource control and optimization, and improving key network performances, such as energy efficiency, spectral efficiency, transmission latency, etc. Therefore, a common understanding for quantum deep-learning algorithms is that they exploit advantages of quantum hardware, enabling massive optimization speed ups, which cannot be achieved by using classical computer hardware. In this respect, this paper investigates the possibility of resolving the energy efficiency problem in wireless communications by developing a quantum neural network (QNN) algorithm of deep-learning that can be tested on a classical computer setting by using any popular numerical simulation tool, such as Python. The computed results show that our QNN algorithm can be indeed trainable and that it can lead to solution convergence during the training phase. We also show that the proposed QNN algorithm exhibits slightly faster convergence speed than its classical ANN counterpart, which was considered in our previous work. Finally, we conclude that our solution can accurately resolve the energy efficiency problem and that it can be extended to optimize other communications problems, such as the global optimal power control problem, with promising trainability and generalization ability.
The rise of deepfake technologies has posed significant challenges to privacy, security, and information integrity, particularly in audio and multimedia content. This paper introduces a Quantum-Trained Convolutional Neural Network (QT-CNN) framework designed to enhance the detection of deepfake audio, leveraging the computational power of quantum machine learning (QML). The QT-CNN employs a hybrid quantum-classical approach, integrating Quantum Neural Networks (QNNs) with classical neural architectures to optimize training efficiency while reducing the number of trainable parameters. Our method incorporates a novel quantum-to-classical parameter mapping that effectively utilizes quantum states to enhance the expressive power of the model, achieving up to 70% parameter reduction compared to classical models without compromising accuracy. Data pre-processing involved extracting essential audio features, label encoding, feature scaling, and constructing sequential datasets for robust model evaluation. Experimental results demonstrate that the QT-CNN achieves comparable performance to traditional CNNs, maintaining high accuracy during training and testing phases across varying configurations of QNN blocks. The QT framework’s ability to reduce computational overhead while maintaining performance underscores its potential for real-world applications in deepfake detection and other resource-constrained scenarios. This work highlights the practical benefits of integrating quantum computing into artificial intelligence, offering a scalable and efficient approach to advancing deepfake detection technologies.