Quantum-inspired optimization algorithms have emerged as a compelling alternative to fully quantum approaches, leveraging classical hardware to simulate key quantum features—such as superposition, entanglement analogs, and parameterized variational circuits—to solve high-dimensional, nonconvex optimization problems more efficiently than traditional heuristics. This manuscript presents Quantum-Inspired Edge AI Optimization for Neural Prosthetics (QEAI-O-NP), a comprehensive framework that integrates a Variational Quantum-Inspired Optimizer (VQIO) with a Spiking Neural Network (SNN) decoder, both tailored for on-device execution on resource-constrained edge platforms. Combining a hybrid quantum-classical metaheuristic surrogate of the Quantum Approximate Optimization Algorithm (QAOA) with low-power neuromorphic inference, QEAI-O-NP addresses challenges of adaptive weight tuning, latency, and energy efficiency inherent to real-time electromyographic (EMG) signal decoding for prosthetic control. The framework preprocesses multi-channel EMG via bandpass filtering and windowing, encodes features into spike trains, and performs classification through a three-layer LIF-based SNN. Periodically, the VQIO formulates the current loss landscape as a cost Hamiltonian and executes two-layer QAOA-inspired tensor-network simulations to extract gradient estimates for synaptic weight updates. Deployed on an NVIDIA Jetson Xavier NX, QEAI-O-NP achieves a 15% absolute gain in decoding accuracy (from 80.2% to 95.2%), reduces inference latency by 30% (from 45.8 ms to 32.1 ms), and cuts energy consumption per inference by 25% (from 193 mJ to 145 mJ) compared to state-of-the-art edge AI baselines. An ablation study explores the trade-offs of QAOA depth and update frequency, guiding practical parameter selection.
Quantum-inspired optimization algorithms have emerged as a compelling alternative to fully quantum approaches, leveraging classical hardware to simulate key quantum features—such as superposition, entanglement analogs, and parameterized variational circuits—to solve high-dimensional, nonconvex optimization problems more efficiently than traditional heuristics. This manuscript presents Quantum-Inspired Edge AI Optimization for Neural Prosthetics (QEAI-O-NP), a comprehensive framework that integrates a Variational Quantum-Inspired Optimizer (VQIO) with a Spiking Neural Network (SNN) decoder, both tailored for on-device execution on resource-constrained edge platforms. Combining a hybrid quantum-classical metaheuristic surrogate of the Quantum Approximate Optimization Algorithm (QAOA) with low-power neuromorphic inference, QEAI-O-NP addresses challenges of adaptive weight tuning, latency, and energy efficiency inherent to real-time electromyographic (EMG) signal decoding for prosthetic control. The framework preprocesses multi-channel EMG via bandpass filtering and windowing, encodes features into spike trains, and performs classification through a three-layer LIF-based SNN. Periodically, the VQIO formulates the current loss landscape as a cost Hamiltonian and executes two-layer QAOA-inspired tensor-network simulations to extract gradient estimates for synaptic weight updates. Deployed on an NVIDIA Jetson Xavier NX, QEAI-O-NP achieves a 15% absolute gain in decoding accuracy (from 80.2% to 95.2%), reduces inference latency by 30% (from 45.8 ms to 32.1 ms), and cuts energy consumption per inference by 25% (from 193 mJ to 145 mJ) compared to state-of-the-art edge AI baselines. An ablation study explores the trade-offs of QAOA depth and update frequency, guiding practical parameter selection.
The exponential runtime of quantum simulators on classical machines, along with long queue times and high costs associated with real quantum devices, presents significant challenges for the efficient optimization of Variational Quantum Algorithms (VQAs) such as the Variational Quantum Eigensolver (VQE), Quantum Approximate Optimization Algorithm (QAOA), and Quantum Neural Networks (QNNs). To address these limitations, we propose a novel approach, DyPP (Dynamic Parameter Prediction), which accelerates the convergence of VQAs by leveraging regular trends in the parameter weights to update parameters efficiently. We introduce two techniques for optimal prediction performance: Naive Prediction (NaP) and Adaptive Prediction (AdaP). Through extensive experimentation and training of multiple QNN models on various datasets, we demonstrate that DyPP achieves a speedup of approximately 2.25×compared to standard training methods, while also providing improved accuracy (up to 2.3% higher) and lower loss (up to 6.1% reduction) with minimal storage and computational overhead. We further evaluate DyPP’s effectiveness in VQE for molecular ground-state energy estimation and in QAOA for graph MaxCut. Our results show that, on average, DyPP leads to a speedup of up to 3.1× for VQE and 2.91× for QAOA compared to traditional optimization, while requiring up to 3.3× fewer shots (i.e., repeated circuit executions). Even in the presence of hardware noise, DyPP outperforms existing optimization techniques, delivering up to 3.33× speedup and requiring 2.5× fewer shots, thereby enhancing the efficiency of VQAs.
Edge artificial intelligence (Edge AI) requires real-time inference under stringent constraints on computation, memory, energy, and connectivity. Although training can be offloaded to servers, efficient, high-capacity inference and rapid on-device adaptation remain central challenges. Cloud-based inference offers substantial computational power but depends on connectivity, latency, privacy, and reliability conditions that edge deployments cannot always guarantee. Classical model-compression methods—including quantization, pruning, distillation, and neural architecture search—have extended the feasibility of on-device inference, yet they leave largely unchanged the fundamental cost of the linear-algebraic, sampling, and optimization primitives that dominate modern deep learning. Quantum computing has therefore been proposed as a complementary accelerator for selected AI workloads, with theoretical advantages in linear systems, singular value decomposition, sampling, kernel evaluation, and optimization. This review surveys the emerging field of quantum-accelerated AI for edge systems under a hybrid architectural premise: edge devices remain classical, while quantum processors operate as remote, cloud, MEC, or near-edge accelerators. We synthesize advances across quantum learning models, hybrid optimization methods, hardware and deployment architectures, and quantum-inspired approaches suitable for constrained devices. We also assess the practical barriers that currently separate asymptotic quantum advantage from deployable edge intelligence, including data loading, measurement overhead, noise, latency, and benchmarking gaps. Finally, we outline a staged research roadmap from near-term hybrid workflows to fault-tolerant and integrated quantum-edge architectures.
We study parameterized quantum circuits (PQCs) integrated as residual sidecar modules within a ResNet-50 backbone for 31-class neural population decoding---imagined handwriting classification from multi-neuron spike rasters. Under strictly controlled conditions (fixed data splits, seeds, and optimizer), we compare four model variants: baseline, quantum sidecar with frozen input projection, quantum sidecar with backbone-gradient-trained projection, and a measurement-guided variant that aligns angle encodings with circuit measurement outcomes. The backbone-gradient variant improves accuracy in 3/4 seeds (+0.19% mean, 95% CI [-1.10%, +1.48%]) and consistently reduces Linear CKA similarity to baseline features (Δ=−0.025, 4/4 seeds), indicating genuine structural reorganization of representations. A nine-variant ablation identifies simple shallow architectures as the most effective and reproducible configuration. Measurement-guided training consistently improves representation geometry without reducing accuracy. All results use noiseless statevector simulation on 4 qubits, a regime chosen to reflect the practical constraints of current near-term superconducting hardware; no quantum computational advantage over classical methods is claimed.
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.
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.
Training deep neural networks remains a computationally intensive and often challenging process. Traditional optimization methods, such as stochastic gradient descent (SGD) and its variants, frequently struggle to escape local minima and converge to optimal solutions, leading to suboptimal network performance. This paper proposes a novel approach to neural network training that leverages principles inspired by quantum mechanics. Specifically, we investigate the application of quantum-inspired optimization techniques, including simulated annealing and quantum walks, to dynamically adjust the training process. The core idea is to mimic the probabilistic exploration inherent in quantum systems to effectively navigate the complex loss landscape of neural networks. We formulate the training process as a quantum-inspired optimization problem, where the network's weights are treated as quantum bits (Qubits) and the optimization algorithm seeks to find the lowest energy state, analogous to finding the minimum loss. The proposed method demonstrates the potential for accelerating convergence and improving the overall accuracy of neural networks compared to conventional optimization strategies. Our analysis focuses on the key aspects of the approach, including the mathematical formulation of the quantum-inspired optimization problem and the selection of appropriate parameters to govern the exploration and exploitation phases. We present a theoretical framework for understanding the dynamics of this approach and discuss potential avenues for further research, including hybrid quantum-classical algorithms and the integration of quantum computing hardware.