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Neurocomputing

Neuromorphic quantum computing: A survey and future directions in evolutionary and gradient-based learning strategies for quantum spiking neural networks

Yingchao Cheng, Meijia Wang, Zhifeng Hao, Rajkumar Buyya

Published Jul 24, 2026• 0 citations
Publisher
DOI
10.1016/j.neucom.2026.134580

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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.
The convergence of quantum computing and neuromorphic engineering represents a promising frontier for next-generation artificial intelligence. Hybrid quantum-neuromorphic intelligence is emerging as an alternative to conventional von Neumann computing by combining the strengths of both paradigms. Quantum systems provide powerful capabilities for optimization, simulation, and complex problem solving, while neuromorphic systems enable energy-efficient, event-driven processing inspired by biological neural networks. Their integration offers new opportunities to address challenges related to computational complexity, scalability, energy consumption, and adaptive learning. Quantum machine learning, quantum neural networks, spiking neural networks, hybrid optimization approaches, and applications in healthcare, smart cities, cybersecurity, and autonomous systems. It also highlights explainability, ethical considerations, and future research directions, concluding that quantum-neuromorphic intelligence could become a key enabler of future AI industries and advanced intelligent systems.