There has been a rapid advancement in developing computational models originating from the governing principles of quantum mechanics. Quantum computing approaches such as variational quantum algorithms (VQAs) and quantum-inspired algorithms have shown advantages across various applications. VQAs have been widely used as hybrid classical-quantum computational frameworks that are well-suited for NISQ advantages. Alternatively, the quantum-inspired evolutionary algorithm (QiEA) provides an efficient search space and parameter optimization solution, useful in handling problems that consist of large parameters. Following the recent trends, quantum-inspired technique, developments, limitations, and future scope are highlighted. Also, an approach towards advancing neuromorphic computation is discussed. Neuromorphic computation and systems are a fast developing domain inherited from fast and massively parallel processing, low energy consumption, high density of millions of neurons in a chip, and have found successful applications across spatio-temporal domain areas including brain data modelling. However, they require a fast and efficient parameter optimization process in large parameter space. Finally, future directions for the development of quantum-enhanced spiking neural networks and neuromorphic systems are outlined.
There has been a rapid advancement in developing computational models originating from the governing principles of quantum mechanics. Quantum computing approaches such as variational quantum algorithms (VQAs) and quantum-inspired algorithms have shown advantages across various applications. VQAs have been widely used as hybrid classical-quantum computational frameworks that are well-suited for NISQ advantages. Alternatively, the quantum-inspired evolutionary algorithm (QiEA) provides an efficient search space and parameter optimization solution, useful in handling problems that consist of large parameters. Following the recent trends, quantum-inspired technique, developments, limitations, and future scope are highlighted. Also, an approach towards advancing neuromorphic computation is discussed. Neuromorphic computation and systems are a fast developing domain inherited from fast and massively parallel processing, low energy consumption, high density of millions of neurons in a chip, and have found successful applications across spatio-temporal domain areas including brain data modelling. However, they require a fast and efficient parameter optimization process in large parameter space. Finally, future directions for the development of quantum-enhanced spiking neural networks and neuromorphic systems are outlined.
There has been a rapid advancement in developing computational models originating from the governing principles of quantum mechanics. Quantum computing approaches such as variational quantum algorithms (VQAs) and quantum-inspired algorithms have shown advantages across various applications. VQAs have been widely used as hybrid classical-quantum computational frameworks that are well-suited for NISQ advantages. Alternatively, the quantum-inspired evolutionary algorithm (QiEA) provides an efficient search space and parameter optimization solution, useful in handling problems that consist of large parameters. Following the recent trends, quantum-inspired technique, developments, limitations, and future scope are highlighted. Also, an approach towards advancing neuromorphic computation is discussed. Neuromorphic computation and systems are a fast developing domain inherited from fast and massively parallel processing, low energy consumption, high density of millions of neurons in a chip, and have found successful applications across spatio-temporal domain areas including brain data modelling. However, they require a fast and efficient parameter optimization process in large parameter space. Finally, future directions for the development of quantum-enhanced spiking neural networks and neuromorphic systems are outlined.
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.
As Moore’s Law approaches its physical limits and traditional deep learning architectures encounter fundamental scaling barriers, the convergence of neuromorphic computing, quantum information processing, and deep learning represents the most promising pathway toward next-generation artificial intelligence systems. Current deep learning approaches, while revolutionary, face critical limitations including exponential energy consumption, limited adaptability, and architectural constraints that prevent true cognitive capabilities. This paper introduces Neuromorphic-Quantum Hybrid Learning (NQHL), a revolutionary computational paradigm that synergistically combines brain-inspired neuromorphic circuits, quantum computational advantages, and deep learning methodologies to create the first truly cognitive artificial intelligence architecture. Our approach addresses four fundamental challenges in next-generation AI: energy-efficient computation through neuromorphic substrates, exponential processing advantages via quantum parallelism, adaptive learning through bio-inspired plasticity mechanisms, and scalable architectures that transcend von Neumann limitations. We develop novel techniques including Quantum Synaptic Networks (QSN), Bio-Inspired Quantum Circuits (BIQC), Hybrid Plasticity Algorithms (HPA), and Neuromorphic-Quantum Interface Protocols (NQIP) that collectively enable AI systems with unprecedented cognitive capabilities. Through comprehensive theoretical analysis and simulation studies, we demonstrate that NQHL achieves 1000x energy efficiency improvements while maintaining 95% accuracy across diverse cognitive tasks including pattern recognition, causal reasoning, and creative problem-solving. Our framework successfully bridges the gap between biological intelligence principles and quantum computational advantages, providing the foundational architecture for post-digital AI systems that can operate at the scale and efficiency required for artificial general intelligence.
In recent years, as datasets have grown larger, quantum computing has shown potential in addressing the rapidly increasing costs of data processing due to its superposition and entanglement capabilities. Limited by quantum hardware and the number of controllable qubits, variational quantum algorithms (VQAs) are algorithms suitable for NISQ devices, capable of computation on medium-scale noisy devices. VQAs are hybrid classical-quantum algorithms that use parameterized quantum circuits to prepare parameterized quantum states and employ classical optimizers to minimize objective functions, enabling efficient solutions for complex problems. Classical optimizers reduce the stringent requirements on quantum hardware and can achieve quantum advantage on current devices. This paper systematically elaborates on the core foundational theories of quantum computing and neural networks, with a focus on reviewing the research and application status of VQAs in three main scenarios: machine learning (supervised, unsupervised, and reinforcement learning), solving systems of equations (linear and nonlinear), and network attack detection. It analyzes the current technical bottlenecks in this field and provides prospects for future development, offering a reference for further research on variational quantum algorithms.