Brain-computer interface (BCI) technology, as a key means of human-computer interaction, has a wide range of applications in the fields of neurorehabilitation, brain-control devices, and intelligent medicine. However, existing BCI systems suffer from high computational complexity, low decoding accuracy, and limited real-time performance during neural signal processing, which restricts their practical applications. In this study, a quantum-enhanced BCI optimization algorithm is proposed to address this technical bottleneck by combining the parallel computing capability and nonlinear optimization characteristics of quantum computing. The study constructs a quantum-classical hybrid computing architecture, uses quantum Fourier transform (QFT) to enhance the efficiency of time-frequency analysis of EEG signals, introduces quantum principal component analysis (QPCA) to optimize the dimensionality reduction of high-dimensional data, and enhances the generalization ability of motion intent classification by combining with quantum support vector machines (QSVMs), and verifies the effectiveness of the method in experiments. The results show that the method outperforms the traditional BCI algorithm in terms of decoding accuracy, computational efficiency and real-time performance, and the system end-to-end latency is controlled within 200ms, which meets the demands of online brain-computer interaction. This study provides theoretical support and experimental validation for the application of quantum computing in the field of brain-computer interface, which can be further expanded to closed-loop neuromodulation, intelligent prosthesis control and other application scenarios in the future, providing new ideas for the development of high-efficiency brain-computer interaction technology.
Brain-computer interface (BCI) technology, as a key means of human-computer interaction, has a wide range of applications in the fields of neurorehabilitation, brain-control devices, and intelligent medicine. However, existing BCI systems suffer from high computational complexity, low decoding accuracy, and limited real-time performance during neural signal processing, which restricts their practical applications. In this study, a quantum-enhanced BCI optimization algorithm is proposed to address this technical bottleneck by combining the parallel computing capability and nonlinear optimization characteristics of quantum computing. The study constructs a quantum-classical hybrid computing architecture, uses quantum Fourier transform (QFT) to enhance the efficiency of time-frequency analysis of EEG signals, introduces quantum principal component analysis (QPCA) to optimize the dimensionality reduction of high-dimensional data, and enhances the generalization ability of motion intent classification by combining with quantum support vector machines (QSVMs), and verifies the effectiveness of the method in experiments. The results show that the method outperforms the traditional BCI algorithm in terms of decoding accuracy, computational efficiency and real-time performance, and the system end-to-end latency is controlled within 200ms, which meets the demands of online brain-computer interaction. This study provides theoretical support and experimental validation for the application of quantum computing in the field of brain-computer interface, which can be further expanded to closed-loop neuromodulation, intelligent prosthesis control and other application scenarios in the future, providing new ideas for the development of high-efficiency brain-computer interaction technology.
A brain-computer interface (BCI) system enables direct communication between the brain and external devices, offering significant potential for assistive technologies and advanced human-computer interaction. Despite progress, BCI systems face persistent challenges, including signal variability, classification inefficiency, and difficulty adapting to individual users in real time. In this study, we propose a novel hybrid quantum learning model, termed QSVM-QNN, which integrates a Quantum Support Vector Machine (QSVM) with a Quantum Neural Network (QNN), to improve classification accuracy and robustness in EEG-based BCI tasks. Unlike existing models, QSVM-QNN combines the decision boundary capabilities of QSVM with the expressive learning power of QNN, leading to superior generalization performance. The proposed model is evaluated on two benchmark EEG datasets, achieving high accuracies of 0.990 and 0.950, outperforming both classical and standalone quantum models. To demonstrate real-world viability, we further validated the robustness of QNN, QSVM, and QSVM-QNN against six realistic quantum noise models, including bit flip and phase damping. These experiments reveal that QSVM-QNN maintains stable performance under noisy conditions, establishing its applicability for deployment in practical, noisy quantum environments. Beyond BCI, the proposed hybrid quantum architecture is generalizable to other biomedical and time-series classification tasks, offering a scalable and noise-resilient solution for next-generation neurotechnological systems.
This paper explores the potential of quantum computing to revolutionize brain-computer interfaces (BCIs) and accelerate complex computations within the brain. We propose a novel architecture for a quantum-enhanced BCI, leveraging quantum algorithms to efficiently decode brain signals and translate them into optimized quantum operations. The core mechanism centers around a quantum-accelerated signal processing pipeline, aiming to significantly increase the speed and accuracy of neural control tasks. The paper details the design of the BCI system, outlining the quantum algorithms employed, the hardware components involved, and a preliminary analysis of the expected performance improvements. We conclude by highlighting the novelty and potential impact of this approach, emphasizing its promise for advancements in neuroscience, cognitive computing, and control systems.
Abstract In recent years, quantum computing (QC) and artificial intelligence (AI) have achieved breakthroughs across various scientific and engineering domains, including radio communication, radar, and navigation systems. These applications involve complex computations, high precision requirements, and robustness against noise—challenges that can benefit from the combined power of quantum computing and AI. This study explores the synergistic potential of QC, AI, and brain-computer interfaces (BCIs) to address practical transport and navigation tasks using quantum parallelism. By leveraging IBM Q for quantum computation and GPT Chat for AI-assisted modeling, the research demonstrates methods for optimizing signal processing, error mitigation, and decision-making in radio-based systems. The integration of these technologies enables: Efficient simulation and processing of high-dimensional data in real time. Enhanced robustness to interference and environmental noise. Adaptive algorithms guided by AI insights for optimal performance. This approach highlights the practical applicability of hybrid quantum-classical systems in transport and navigation, paving the way for next-generation solutions that combine quantum computational speed, AI intelligence, and human-computer interfacing.
In the Brain-Computer Interfaces where Electroencephalogram (EEG) signals are used, fast and accurate analysis of data is one of the important steps. For this aim, various machine learning methods are being developed. In this study, we compared the performance of the Quantum Powered Support Vector Machine (QSVM) with classical machine learning algorithms for classifying EEG data. Analyzes were performed in the quantum simulation environment and due to the inadequacies in the quantum environment, the number of samples in the data set was reduced by using Principal Component Analysis. The maximum performance of QSVM was found to be 99.47% in the experiments. When all the results in the analyzes are examined, it is seen that the QSVM has achieved good performance. By removing the limitations of quantum computers and making changes in the hyperparameters of quantum-based algorithms, the use of quantum-based machine learning methods in the classification of EEG data can provide better results in terms of accuracy and runtime.
In neuroscience and therapeutic practice, electroencephalography (EEG) is a vital instrument for tracking and analysing brain activity. While traditional neural network models, like EEG-Net, have made significant progress in interpreting EEG signals, they frequently encounter difficulties due to the great dimensionality and complexity of the data. Quantum machine learning (QML) techniques offer new ways to improve machine learning models, thanks to recent developments in quantum computing. As a forward-looking approach, we present Quantum-EEG Net (QEEG Net), a novel hybrid neural network that combines quantum computing with the classical EEG Net architecture to improve EEG encoding and analysis. While the results may not always outperform conventional methods, it demonstrates its potential. In order to capture more complex patterns in EEG data and maybe provide computational benefits, QEEG Net integrates quantum layers into the neural network. Using the benchmark EEG dataset, BCI Competition IV 2a, we test QEEG Net and show that it consistently performs better than standard EEG-Net on the majority of participants and has other robustness to noise.
Quantum algorithms have emerged as a transformative approach to address the computational challenges of high-dimensional image processing tasks. This research investigates the integration of quantum computing principles, such as Quantum Fourier Transform (QFT) and Quantum Principal Component Analysis (QPCA), to optimize processes like edge detection, noise reduction, and image segmentation. These quantum-enhanced techniques promise significant improvements in computational efficiency and accuracy compared to classical methods, especially when handling complex visual data in real-time applications like medical imaging, satellite imagery, and autonomous systems. Additionally, the study explores hybrid quantum-classical architectures to bridge current limitations in noisy intermediate-scale quantum (NISQ) devices, ensuring resource optimization and scalability. The potential of quantum algorithms to provide faster, more precise solutions is evaluated alongside challenges like error mitigation and algorithmic stability. This research highlights the future role of quantum computing in revolutionizing image processing, paving the way for breakthroughs in computer vision and artificial intelligence.