In this paper, we propose a novel framework for multimodal contrastive learning utilizing a quantum encoder to integrate EEG (electroencephalogram) and image data. This groundbreaking attempt explores the integration of quantum encoders within the traditional multimodal learning framework. By leveraging the unique properties of quantum computing, our method enhances the representation learning capabilities, providing a robust framework for analyzing time series and visual information concurrently. We demonstrate that the quantum encoder effectively captures intricate patterns within EEG signals and image features, facilitating improved contrastive learning across modalities. This work opens new avenues for integrating quantum computing with multimodal data analysis, particularly in applications requiring simultaneous interpretation of temporal and visual data.
In this paper, we propose a novel framework for multimodal contrastive learning utilizing a quantum encoder to integrate EEG (electroencephalogram) and image data. This groundbreaking attempt explores the integration of quantum encoders within the traditional multimodal learning framework. By leveraging the unique properties of quantum computing, our method enhances the representation learning capabilities, providing a robust framework for analyzing time series and visual information concurrently. We demonstrate that the quantum encoder effectively captures intricate patterns within EEG signals and image features, facilitating improved contrastive learning across modalities. This work opens new avenues for integrating quantum computing with multimodal data analysis, particularly in applications requiring simultaneous interpretation of temporal and visual data.
Advances in neuroimaging and genomics have enabled unprecedented insights into the biological underpinnings of neuropsychiatric disorders; however, integrating heterogeneous, high-dimensional datasets remains a significant analytical challenge. This paper explores quantum neuroinformatics for multi-modal brain imaging, focusing on the integration of functional magnetic resonance imaging (fMRI), electroencephalography (EEG), and genomic data to model complex neuropsychiatric phenotypes. By leveraging hybrid quantum–classical machine learning frameworks, the study examines how quantum-enhanced feature mapping, probabilistic modeling, and high-dimensional optimization can improve the fusion and interpretation of multimodal datasets. The analysis evaluates the potential of quantum algorithms to capture nonlinear interactions across neural, temporal, and genetic domains, while also addressing computational and hardware limitations associated with the Noisy Intermediate-Scale Quantum (NISQ) era. The findings suggest that quantum neuroinformatics may offer a novel pathway for advancing precision psychiatry by enabling more accurate phenotypic classification, biomarker discovery, and personalized treatment strategies.
Advances in neuroimaging and genomics have enabled unprecedented insights into the biological underpinnings of neuropsychiatric disorders; however, integrating heterogeneous, high-dimensional datasets remains a significant analytical challenge. This paper explores quantum neuroinformatics for multi-modal brain imaging, focusing on the integration of functional magnetic resonance imaging (fMRI), electroencephalography (EEG), and genomic data to model complex neuropsychiatric phenotypes. By leveraging hybrid quantum–classical machine learning frameworks, the study examines how quantum-enhanced feature mapping, probabilistic modeling, and high-dimensional optimization can improve the fusion and interpretation of multimodal datasets. The analysis evaluates the potential of quantum algorithms to capture nonlinear interactions across neural, temporal, and genetic domains, while also addressing computational and hardware limitations associated with the Noisy Intermediate-Scale Quantum (NISQ) era. The findings suggest that quantum neuroinformatics may offer a novel pathway for advancing precision psychiatry by enabling more accurate phenotypic classification, biomarker discovery, and personalized treatment strategies.
Electroencephalography (EEG) is a critical tool in neuroscience and clinical practice for monitoring and analyzing brain activity. Traditional neural network models, such as EEG-Net, have achieved considerable success in decoding EEG signals but often struggle with the complexity and high dimensionality of the data. Recent advances in quantum computing present new opportunities to enhance machine learning models through quantum machine learning (QML) techniques. In this paper, we introduce Quantum-EEGNet (QEEGNet), a novel hybrid neural network that integrates quantum computing with the classical EEGNet architecture to improve EEG encoding and analysis, as a forward-looking approach, acknowledging that the results might not always surpass traditional methods but it shows its potential. QEEGNet incorporates quantum layers within the neural network, allowing it to capture more intricate patterns in EEG data and potentially offering computational advantages. We evaluate QEEGNet on a benchmark EEG dataset, BCI Competition IV 2a, demonstrating that it consistently outperforms traditional EEG-Net on most of the subjects and other robustness to noise. Our results highlight the significant potential of quantum-enhanced neural networks in EEG analysis, suggesting new directions for both research and practical applications in the field.
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.
Artificial intelligence (AI) has achieved remarkable progress in single-modal tasks such as text analysis and image recognition. Several real-world problems often demand multimodal intelligence that can integrate and reason across diverse data sources. Traditional machine learning methods face scalability and efficiency limitations for handling heterogeneous multimodal datasets. Recent work in quantum computing introduces new opportunities for AI using superposition and entanglement. Quantum Machine Learning (QML) has shown early promise in classification, optimization, and learning, but applications to multimodal intelligence remain at an early stage. Existing studies are fragmented, and there is a lack of systematic understanding of how QML can address the challenges of multimodal learning. Therefore, this paper provides a structured survey of QML approaches for multimodal tasks. We review data encoding strategies, quantum models such as quantum support vector machines and variational quantum circuits, and hybrid quantum-classical frameworks. We analyze their strengths, limitations, current applications, and future directions. Our findings show that QML faces obstacles in scalability, noise, and benchmarking for efficient cross-modal reasoning and representation. Quantum generative models and quantum-inspired multimodal architectures could significantly expand the capabilities of AI in the future. It highlights both the opportunities and open challenges in applying QML to multimodal intelligence. Overall, the paper provides researchers with a roadmap for advancing this field and sets the stage for future applications in precision medicine, autonomous systems, cybersecurity, and climate modeling.
Quantum computing has recently inspired many applications in machine learning. In this paper, we present a novel idea of combining neuromorphic computing and quantum computing to develop an advanced application for studying and analysing brain signals. We integrate a quantum spiking leaky integrate-and-fire (QLIF) neuron into a 3D spiking neural network framework designed for analysing and classifying biological data. This modified model is applied to EEG brain signals obtained from experimental measurements of a subject's wrist movements, providing a real-world demonstration of the model's capabilities. The proof-of-concept results show that the QLIF model outperforms its classical counterpart across a series of binary classification tasks, suggesting the promising compatibility of quantum neuromorphic algorithms for the spatio-temporal EEG dataset. Our illustration offers a new direction for enhancing the advantages of quantum computing in complex machine learning applications.