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 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.
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 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.
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.
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,
Neuromorphic and quantum computing have recently emerged as promising paradigms for advancing artificial intelligence, each offering complementary strengths. Neuromorphic systems built on spiking neurons excel at processing time series data efficiently through sparse, event-driven computation, consuming energy only upon input events. Quantum computing, on the other hand, operates on state spaces that grow exponentially in dimension with the number of qubits -- as a consequence of tensor-product composition -- with quantum states admitting superposition across basis states and entanglement between subsystems. Hybrid approaches combining these paradigms have begun to show potential, but existing quantum spiking models have important limitations. Notably, they implement classical memory mechanisms on single qubits, requiring repeated measurements to estimate firing probabilities, while relying on conventional backpropagation for training. In this paper, we propose a novel stochastic quantum spiking (SQS) neuron model that addresses these challenges. The SQS neuron uses multi-qubit quantum circuits to realize a spiking unit with internal quantum memory, enabling event-driven probabilistic spike generation in a single shot during inference. Furthermore, we study networks of SQS neurons, dubbed SQS neural networks (SQSNN), and demonstrate that they can be trained via a hardware-friendly local learning rule, eliminating the need for global classical backpropagation. The proposed SQSNN model is shown via experiments with both conventional and neuromorphic datasets to improve over previous quantum spiking neural networks, as well as over classical counterparts, when fixing the overall number of trainable parameters, highlighting its potential for event-driven applications such as neuromorphic integrated sensing and communications (N-ISAC).
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.
Quantum-AI is Ushering in a new scientific revolution, where Quantum Spiking Neural Networks (QSNN) with Quantum Leaky Integrate-and-Fire (QLIF) neurons propel advancements in the modeling of complex biological systems and neural networks. Inspired by the remarkable synergy between quantum mechanics and biological spiking neurons, we present a novel QSNN architecture that leverages QLIF to tackle complex AI tasks. Leveraging entanglement, the quantum variant of SNNs acts as a game-changer, offering faster computation, enhanced accuracy. QLIF Spiking Neurons provide a powerful solution to scalability issues, advancing the potential of AI and quantum technologies in neural networks and complex problem-solving across industries and science. Our approach presents the QLIF model, where neurons integrate incoming signals and fire upon a threshold crossing, creating spikes that interact with quantum circuits to form a quantum-spiking neural network. Our work aims to: (i) enhance computational time and (ii) enrich the learning capabilities of the training model. Our results highlight that QSNN not only accelerates training but also improves accuracy, particularly in deeper quantum circuits. This demonstrates the effectiveness of integrating spiking neural dynamics into quantum models, paving the way for more efficient and biologically inspired quantum learning systems. The proposed approach holds promise for future applications in neuromorphic quantum computing and real-time edge intelligence.
The integration of neuro-inspired algorithms and quantum computing in machine learning presents a promising frontier for addressing complex computational challenges in modern AI. Neuro-inspired algorithms, such as artificial neural networks (ANNs), deep learning (DL), and spiking neural networks (SNNs), have demonstrated impressive performance in various domains, including image recognition, natural language processing, and autonomous systems. This research explores the synergy between neuro-inspired algorithms and quantum computing, focusing on how quantum-enhanced machine learning models can accelerate training and inference processes in neuro-inspired systems. Quantum neural networks (QNNs) leverage quantum principles, such as superposition and entanglement, to represent and manipulate data in ways that classical systems cannot. By combining quantum computing's parallelism with the flexibility and learning capability of neuro-inspired algorithms, the proposed hybrid models can provide exponential speedups in tasks involving large-scale data processing and optimization. To evaluate the performance of these hybrid models, experiments were conducted using a quantum-enhanced deep learning model applied to image classification and a neuro-inspired algorithm augmented by quantum optimization techniques for optimization tasks. The quantum-enhanced deep learning model achieved a 45% reduction in training time compared to classical deep learning models while maintaining similar accuracy levels. These findings highlight the significant potential of combining quantum computing with neuro-inspired algorithms, opening new avenues for faster, more efficient machine learning models capable of solving previously unsolvable problems. The synergy between these two domains could lead to breakthroughs in areas like artificial general intelligence (AGI), drug discovery, and autonomous systems, where large-scale optimization and pattern recognition are critical.