Smart systems deployed in vision, speech, healthcare, and smart city settings are increasingly working under severe requirements of latency, energy use and uncertainty, revealing inherent weaknesses in standard deep learning methods, as well as in single- shots of quantum computing. Although both neuromorphic computing and quantum-enhanced learning have been demonstrated to be promising, independently, neither approach can provide a tradeoff between real-time performance and global reasoning. This paper discusses the absence of a single and deployment focused model that would bring neuromorphic efficiency and quantum expressiveness to a single system. In our proposal, a quantum-enhanced neuromorphic computing system, event-driven spiking neural networks are used to do continuous, low-power perception and quantum-assisted machinery used to invoke uncertainty-aware classification and global optimization.
Smart systems deployed in vision, speech, healthcare, and smart city settings are increasingly working under severe requirements of latency, energy use and uncertainty, revealing inherent weaknesses in standard deep learning methods, as well as in single- shots of quantum computing. Although both neuromorphic computing and quantum-enhanced learning have been demonstrated to be promising, independently, neither approach can provide a tradeoff between real-time performance and global reasoning. This paper discusses the absence of a single and deployment focused model that would bring neuromorphic efficiency and quantum expressiveness to a single system. In our proposal, a quantum-enhanced neuromorphic computing system, event-driven spiking neural networks are used to do continuous, low-power perception and quantum-assisted machinery used to invoke uncertainty-aware classification and global optimization.
The explosive increase in the number of Internet of Things (IoT) deployments has increased the pressure on analytics systems that are capable of functioning in real time without being power-intensive and scalable in the presence of unlimited amounts of data. Traditional cloud-based and even edge-based AI systems are unable to strike a balance between the low-latency requirements and the in-depth analytical reasoning, especially when the level of data uncertainty and the scale of the system grows. The hybrid neuromorphic-quantum analytics model suggested in this paper integrates event-driven spiking neuromorphic quantum-assisted inference selectively invoked on complex or uncertain cases, that is, on the edge with event-driven spiking neural networks. The neuromorphic processing deals with inference with high frequency and low latency, and quantum analytics is triggered because of the confidencebased orchestration to decide on ambivalent pattern and global correlations.
Neuromorphic computing is inspired by the dynamics of biological neural systems - they operate event-driven and enable energy-efficient and real-time learning by Spiking neural networks. Advantages of this approach include faster convergence, improved accuracy, and potential energy efficiency gain, making this approach suitable for edge AI, real-time signal processing, and large-scale cognitive simulations. The convergence of neuromorphic computing and quantum optimization offers a transformative pathway for next-generation AI. By integrating event-driven spiking neural networks with quantum algorithms, hybrid systems can achieve faster learning, enhanced global optimization, and energy-efficient computation beyond classical limits. A comprehensive framework has been presented for designing and analyzing such hybrid quantum-neuromorphic architectures, highlighting their potential for real-time, scalable, and intelligent applications.
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.
Quantum Artificial Intelligence (Quantum AI) embodies the integration of quantum computational paradigms with artificial intelligence frameworks to address the computational intractability and scalability constraints inherent in classical AI systems. By leveraging quantum mechanical properties such as superposition, entanglement, and quantum interference, Quantum AI enables enhanced representation learning, accelerated optimization, and improved probabilistic inference in high-dimensional problem spaces. In parallel, deep-tech innovations—including advanced deep learning architectures, neuromorphic computing, and edge-based intelligent processing—are redefining the architectural foundations of next- generation intelligent systems through energy-efficient, adaptive, and real-time computation. This paper presents a technically rigorous and comprehensive review of Quantum AI and deep-tech innovations, focusing on algorithmic formulations, architectural models, and system-level integration strategies. Core methodologies such as quantum machine learning algorithms, parameterized quantum circuits, variational quantum algorithms, and hybrid quantum–classical optimization frameworks are critically analysed with respect to computational complexity, repressibility, and feasibility on noisy intermediate-scale quantum (NISQ) hardware. Additionally, the role of neuromorphic computing in enabling spiking neural architectures and event-driven processing is examined as a complementary paradigm to quantum-enhanced learning models, particularly for low-power and latency-critical applications. The paper further investigates application- driven deployments of Quantum AI across domains including medical diagnostics, cryptographic security, autonomous decision-making, and smart cyber-physical systems, highlighting performance trade-offs and implementation constraints. Key challenges such as quantum noise, decoherence, data encoding bottlenecks, limited qubit connectivity, and ethical governance are systematically discussed. Finally, future research directions are identified, emphasizing fault-tolerant quantum learning architectures, scalable hybrid integration with deep-tech systems, and co-design methodologies for realizing practical next-generation intelligence platforms.
Digital neuromorphic systems seek to replicate the organizational principles and dynamics of biological neural networks in silicon, offering massive parallelism, event‐driven processing, and orders-of-magnitude lower energy consumption compared with conventional von Neumann computers. Concurrently, quantum artificial intelligence (AI) methods exploit quantum superposition and entanglement to accelerate optimization and pattern-recognition tasks. However, quantum hardware today remains constrained by qubit count and noise, while neuromorphic chips are limited in their ability to discover global optima during training. In this work, we propose and evaluate a hybrid framework—“quantum-optimized neuromorphic inference”—in which a variational quantum circuit (VQC) tunes synaptic weight matrices for a spiking neural network (SNN), and the optimized SNN is deployed on a digital neuromorphic core for inference. We first survey the relevant neuromorphic platforms and quantum machine learning algorithms, then describe our methodology for weight optimization and mapping to neuromorphic hardware. Through extensive simulations on a spoken-digit classification benchmark, our hybrid approach achieves a 3.3% absolute improvement in accuracy over a conventionally trained SNN, while reducing energy per inference by 3.5%. We discuss implications for scalable brain emulation, energy-efficient AI, and future neuromorphic-quantum co-design, and outline next steps toward experimental validation on emerging quantum processors and multi-core neuromorphic systems.
As the field of artificial intelligence experiences the emergence of various large-scale models, the computing power requirements are increasing. Simultaneously, Moore’s Law is approaching its limits, which intensifies the need to address how to perform high-dimensional complex data computations efficiently and with minimal energy consumption. Quantum computing and neuromorphic computing, with their unique features, offer potential solutions to this contemporary challenge. In this study, we aim to integrate quantum computing with neuromorphic computing, and develop a quantum spiking neural networks architecture. We proposed three measurement strategy to reducing operational costs, making it more friendly to near-term quantum hardware. We evaluated the performance of these three models on the standard MNIST dataset for classification tasks and found that they maintained a high level of accuracy despite a reduced number of parameters and a substantial decrease in the number of quantum circuit shots.