Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

EPJ Quantum Technology

A hybrid spiking neural network - quantum framework for spatio-temporal data classification: a case study on EEG data

Ravi Kumar Jha, Nikola Kirilov Kasabov, Saugat Bhattacharyya, Damien Coyle, Girijesh Prasad

Published Nov 11, 2025• 3 citations
Publisher

Abstract

Abstract The study introduces a hybrid computational framework that combines neuro-inspired information processing using spiking neural networks (SNN) and quantum information processing using quantum kernels to develop quantum-enhanced machine learning models, demonstrated through the classification of EEG data as a case study. In the proposed SNN-quantum kernel classifier (SNN-QC), SNN with spike time information representation is employed to learn spatio-temporal interactions (EEG recorded from multiple channels over time). Frequency-based (rate-based) information as spike frequency state vectors are extracted from the SNN and classified using a quantum classifier. In the latter part, we use the quantum kernel approach utilizing feature maps for classification tasks. The proposed SNN-QC is demonstrated on a benchmark EEG dataset to classify three distinct wrist movement tasks in six binary classification setups as a proof of concept. We introduce a novel feature map with high-order nonlinearity, which has outperformed current state-of-the-art feature maps and various machine learning methods in most of the case studies. Furthermore, the role of hyperparameters for enhanced feature maps is also highlighted. The performance of SNN-QC is evaluated using statistical metrics and cross-validation techniques, demonstrating its 1 efficacy across multiple binary classifiers. An experimental validation is also performed on an IBM QPU. The results demonstrate that the SNN-QC significantly outperforms the models that use statistical features rather than features extracted from the SNN as SNN accounts for the temporal interaction between the spatio-temporal input variables. Finally, we conclude that the SNN-QC offers a potential pathway for developing more accurate neuromorphic-quantum enhanced systems that are both energy-efficient and biologically-inspired, well-suited for dealing with spatio-temporal data.

DOI
10.1140/epjqt/s40507-025-00443-1

Related papers

A Spiking Neural Network-Quantum Model for Spatiotemporal Data Analysis: An Experimental Framework

Ravi Kumar Jha, Nikola Kirilov Kasabov, Saugat Bhattacharyya +2 more

The study introduces a novel computational framework combining neuro-inspired information through spiking neural networks (SNNs) and quantum information using quantum kernels to develop quantum machine learning models. The framework uses a 3D brain-inspired SNN model to learn spatiotemporal EEG signals through their dynamic interaction. Spike frequency state vectors are extracted from a 3D SNN and a quantum kernel classifier is used to classify them into predefined classes. The study also proposes a new embedding function to enhance the performance of the quantum kernel classifier. The performance of the model is evaluated using statistical metrics and cross-validation techniques, demonstrating its superior efficacy on multiple state-of-the-art embedding functions and classical baseline classifiers. The results indicate a clear advantage of having an integrated framework of an SNN and a quantum kernel, which overcomes the limitations of existing classifiers in dealing with spatiotemporal data and enhances the performance of existing SNNs by improving the classification of their complex internal states.

IEEE International Conference on Quantum Artificial Intelligence (QAI)•Nov 2, 2025•0 citations

Quantum Neuromorphic Classification of EEG Brain Signals

Dean Brand, Ravi Kumar Jha

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.

IEEE International Conference on Quantum Artificial Intelligence (QAI)•Nov 2, 2025•1 citation

Edge to quantum: hybrid quantum-spiking neural network image classifier

Aswathy Ajayan, Alex Pappachen James

Abstract The extreme parallelism property warrant convergence of neural networks with that of quantum computing. As the size of the network grows, the classical implementation of neural networks becomes computationally expensive and not feasible. In this paper, we propose a hybrid image classifier model using spiking neural networks (SNN) and quantum circuits that combines dynamic behaviour of SNN with the extreme parallelism offered by quantum computing. The proposed model outperforms models in comparison with spiking neural network in classical computing, and hybrid convolution neural network-quantum circuit models in terms of various performance parameters. The proposed hybrid SNN-QC model achieves an accuracy of 99.9% in comparison with CNN-QC model accuracy of 96.3%, and SNN model of accuracy 91.2% in MNIST classification task. The tests on KMNIST and CIFAR-1O also showed improvements.

Neuromorphic Computing and Engineering•Aug 12, 2021•16 citations

Deep Spiking Quantum Neural Network for Noisy Image Classification

Debanjan Konar, Vaneet Aggarwal, Aditya Das Sarma +3 more

Recently, quantum machine learning has been ap-plied to stochastic-based modelling, promising that the inherent uncertainty in quantum computing will be a significant advan-tage, driving neuromorphic computing research to new heights. Spiking Neural Networks (SNNs) and their neuromorphic are gaining popularity due to their inherent ability to process spatial and temporal data. However, learning the interconnection weights is daunting due to the inherent stochastic characteristics of neuron signals and the inherent non-differentiable spike events in classical SNN. This paper introduces a supervised Deep Spiking Quantum Neural Network (DSQ-Net) using a hybrid quantum- classical architecture having the merits of amplitude encoding in a dressed quantum layer. A novel attempt has been made to obviate the challenges in training a classical deep SNN, assisted by a Variational Quantum Circuit (VQC) in the proposed hy-brid quantum-classical framework. The DSQ-Net has undergone thorough validation and benchmarking procedures using the PennyLane Quantum Simulator and the limited volume real IBM Quantum hardware. The experiments have been conducted on images from the MNIST, FashionMNIST, KMNIST, and CIFAR-I0 datasets. Classification accuracy has been reported in the high nineties for unseen noisy test images using the proposed DSQ-Net model on the quantum simulator. It outper-forms its classical counterpart (Deep Spiking Neural Networks), shallow Random Quantum Neural Networks (RQNN), Quantum Superposition-inspired Spiking Neural Networks (SQIN), ResNet- 18, and AlexNet. The PyTorch implementation of DSQ-Net is made available on Github11https://anonymous.4open.science/r/DSQ-Net-037E,

International Joint Conference on Neural Networks (IJCNN)•Jun 18, 2023•13 citations

Quantum-Enhanced Spiking Neural Networks

Ria Khatoniar, Debanjan Konar, Vaneet Aggarwal

This article explores the integration of Quantum Computing (QC) into classical Spiking Neural Networks (SNN) architectures to address limitations in encoding spiking signals into quantum circuits. Incorporating QC techniques to encode temporal dependencies within quantum circuits, the study aims to enhance the capabilities of SNN. It introduces a method lever-aging quantum systems' parallelism and high-dimensional state spaces to encode temporal information from classical spiking signals into a quantum framework relying on angle encoding and using two successive rotation gates. While the quantum circuit faced challenges in reconstructing complex temporal patterns, the proposed hybrid quantum-classical model, referred to as Quantum-enhanced Spiking Neural Networks (SQ-Net), significantly improved the efficiency and accuracy of processing temporal data using trigonometric functions. This advancement in temporal encoding within quantum circuits enables quantum systems to handle intricate temporal information effectively for sequential problems.

IEEE International Conference on Quantum Computing and Engineering (QCE)•Sep 15, 2024•3 citations

Parameter efficient hybrid spiking-quantum convolutional neural network with surrogate gradient and quantum data-reupload

Nhan Trong Luu, Duong Trung Luu, Nam Ngoc Pham +1 more

The rapid advancement of artificial intelligence (AI) and deep learning (DL) has catalyzed the emergence of several optimization-driven subfields, notably neuromorphic computing and quantum machine learning. Leveraging the differentiable nature of hybrid models, researchers have explored their potential to address complex problems through unified optimization strategies. One such development is the Spiking Quantum Neural Network (SQNN), which combines principles from spiking neural networks (SNNs) and quantum computing. However, existing SQNN implementations often depend on pretrained SNNs due to the non-differentiable nature of spiking activity and the limited scalability of current SNN encoders. In this work, we propose a novel architecture—Spiking-Quantum Data Re-upload Convolutional Neural Network (SQDR-CNN)—that enables joint training of convolutional SNNs and quantum circuits within a single backpropagation framework. Unlike its predecessor, SQDR-CNN allow convergence to reasonable performance without the reliance of pretrained spiking encoder and subsetting datasets. We also clarified some theoretical foundations, testing new design using quantum data-reupload with different training algorithm-initialization and evaluate the performance of the proposed model under noisy simulated quantum environments. As a result, we were able to achieve 86% of the mean top-performing accuracy of the state of the art (SOTA) SNN baselines, yet uses only 0.5% of the smallest spiking model’s parameters. Through this integration of neuromorphic and quantum paradigms, we aim to open new research directions and foster technological progress in multi-modal, learnable systems.

PeerJ Computer Science•Feb 24, 2026•4 citations
Abstract The study introduces a hybrid computational framework that combines neuro-inspired information processing using spiking neural networks (SNN) and quantum information processing using quantum kernels to develop quantum-enhanced machine learning models, demonstrated through the classification of EEG data as a case study. In the proposed SNN-quantum kernel classifier (SNN-QC), SNN with spike time information representation is employed to learn spatio-temporal interactions (EEG recorded from multiple channels over time). Frequency-based (rate-based) information as spike frequency state vectors are extracted from the SNN and classified using a quantum classifier. In the latter part, we use the quantum kernel approach utilizing feature maps for classification tasks. The proposed SNN-QC is demonstrated on a benchmark EEG dataset to classify three distinct wrist movement tasks in six binary classification setups as a proof of concept. We introduce a novel feature map with high-order nonlinearity, which has outperformed current state-of-the-art feature maps and various machine learning methods in most of the case studies. Furthermore, the role of hyperparameters for enhanced feature maps is also highlighted. The performance of SNN-QC is evaluated using statistical metrics and cross-validation techniques, demonstrating its 1 efficacy across multiple binary classifiers. An experimental validation is also performed on an IBM QPU. The results demonstrate that the SNN-QC significantly outperforms the models that use statistical features rather than features extracted from the SNN as SNN accounts for the temporal interaction between the spatio-temporal input variables. Finally, we conclude that the SNN-QC offers a potential pathway for developing more accurate neuromorphic-quantum enhanced systems that are both energy-efficient and biologically-inspired, well-suited for dealing with spatio-temporal data.
The study introduces a novel computational framework combining neuro-inspired information through spiking neural networks (SNNs) and quantum information using quantum kernels to develop quantum machine learning models. The framework uses a 3D brain-inspired SNN model to learn spatiotemporal EEG signals through their dynamic interaction. Spike frequency state vectors are extracted from a 3D SNN and a quantum kernel classifier is used to classify them into predefined classes. The study also proposes a new embedding function to enhance the performance of the quantum kernel classifier. The performance of the model is evaluated using statistical metrics and cross-validation techniques, demonstrating its superior efficacy on multiple state-of-the-art embedding functions and classical baseline classifiers. The results indicate a clear advantage of having an integrated framework of an SNN and a quantum kernel, which overcomes the limitations of existing classifiers in dealing with spatiotemporal data and enhances the performance of existing SNNs by improving the classification of their complex internal states.
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.
Abstract The extreme parallelism property warrant convergence of neural networks with that of quantum computing. As the size of the network grows, the classical implementation of neural networks becomes computationally expensive and not feasible. In this paper, we propose a hybrid image classifier model using spiking neural networks (SNN) and quantum circuits that combines dynamic behaviour of SNN with the extreme parallelism offered by quantum computing. The proposed model outperforms models in comparison with spiking neural network in classical computing, and hybrid convolution neural network-quantum circuit models in terms of various performance parameters. The proposed hybrid SNN-QC model achieves an accuracy of 99.9% in comparison with CNN-QC model accuracy of 96.3%, and SNN model of accuracy 91.2% in MNIST classification task. The tests on KMNIST and CIFAR-1O also showed improvements.
Recently, quantum machine learning has been ap-plied to stochastic-based modelling, promising that the inherent uncertainty in quantum computing will be a significant advan-tage, driving neuromorphic computing research to new heights. Spiking Neural Networks (SNNs) and their neuromorphic are gaining popularity due to their inherent ability to process spatial and temporal data. However, learning the interconnection weights is daunting due to the inherent stochastic characteristics of neuron signals and the inherent non-differentiable spike events in classical SNN. This paper introduces a supervised Deep Spiking Quantum Neural Network (DSQ-Net) using a hybrid quantum- classical architecture having the merits of amplitude encoding in a dressed quantum layer. A novel attempt has been made to obviate the challenges in training a classical deep SNN, assisted by a Variational Quantum Circuit (VQC) in the proposed hy-brid quantum-classical framework. The DSQ-Net has undergone thorough validation and benchmarking procedures using the PennyLane Quantum Simulator and the limited volume real IBM Quantum hardware. The experiments have been conducted on images from the MNIST, FashionMNIST, KMNIST, and CIFAR-I0 datasets. Classification accuracy has been reported in the high nineties for unseen noisy test images using the proposed DSQ-Net model on the quantum simulator. It outper-forms its classical counterpart (Deep Spiking Neural Networks), shallow Random Quantum Neural Networks (RQNN), Quantum Superposition-inspired Spiking Neural Networks (SQIN), ResNet- 18, and AlexNet. The PyTorch implementation of DSQ-Net is made available on Github11https://anonymous.4open.science/r/DSQ-Net-037E,
This article explores the integration of Quantum Computing (QC) into classical Spiking Neural Networks (SNN) architectures to address limitations in encoding spiking signals into quantum circuits. Incorporating QC techniques to encode temporal dependencies within quantum circuits, the study aims to enhance the capabilities of SNN. It introduces a method lever-aging quantum systems' parallelism and high-dimensional state spaces to encode temporal information from classical spiking signals into a quantum framework relying on angle encoding and using two successive rotation gates. While the quantum circuit faced challenges in reconstructing complex temporal patterns, the proposed hybrid quantum-classical model, referred to as Quantum-enhanced Spiking Neural Networks (SQ-Net), significantly improved the efficiency and accuracy of processing temporal data using trigonometric functions. This advancement in temporal encoding within quantum circuits enables quantum systems to handle intricate temporal information effectively for sequential problems.
The rapid advancement of artificial intelligence (AI) and deep learning (DL) has catalyzed the emergence of several optimization-driven subfields, notably neuromorphic computing and quantum machine learning. Leveraging the differentiable nature of hybrid models, researchers have explored their potential to address complex problems through unified optimization strategies. One such development is the Spiking Quantum Neural Network (SQNN), which combines principles from spiking neural networks (SNNs) and quantum computing. However, existing SQNN implementations often depend on pretrained SNNs due to the non-differentiable nature of spiking activity and the limited scalability of current SNN encoders. In this work, we propose a novel architecture—Spiking-Quantum Data Re-upload Convolutional Neural Network (SQDR-CNN)—that enables joint training of convolutional SNNs and quantum circuits within a single backpropagation framework. Unlike its predecessor, SQDR-CNN allow convergence to reasonable performance without the reliance of pretrained spiking encoder and subsetting datasets. We also clarified some theoretical foundations, testing new design using quantum data-reupload with different training algorithm-initialization and evaluate the performance of the proposed model under noisy simulated quantum environments. As a result, we were able to achieve 86% of the mean top-performing accuracy of the state of the art (SOTA) SNN baselines, yet uses only 0.5% of the smallest spiking model’s parameters. Through this integration of neuromorphic and quantum paradigms, we aim to open new research directions and foster technological progress in multi-modal, learnable systems.