Integration of Hybrid Quantum–Neuromorphic AI With Cloud, Edge, and High-Performance Computing Environments
Abstract
The convergence of quantum computing, neuromorphic learning, and distributed cloud infrastructures has occurred very rapidly, and intelligent systems are now providing new opportunities, but the challenge of instability, complexity of orchestration, and noise sensitivity remains in the way of practical integration. The proposed work is based on a hybrid quantum and neuromorphic architecture, which is the integration of event-based neuromorphic adaptation and quantum-assisted global optimization, orchestrated by cloud-HPC. The architecture presents the thermodynamically regularized learning and resourceful task scheduling to the probabilistic search and the continuous local adaptation. Experimental evaluation across financial modeling, medical imaging, and physical system prediction shows an average accuracy of 94.3%, a reduction in convergence time to 3.5 s, and improved reconstruction quality (SSIM = 0.93, PSNR = 33.0 dB). Workflow robustness also increases, achieving a 96.1% task success rate under secure orchestration.