Abstract

As Moore's Law approaches its physical limits and traditional deep learning architectures encounter funda mental 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 para digm 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 limita tions. We develop novel techniques including Quantum Synaptic Networks (QSN), Bio-Inspired Quantum Cir cuits (BIQC), Hybrid Plasticity Algorithms (HPA), and Neuromorphic-Quantum Interface Protocols (NQIP) that collectively enable AI systems with unprecedented cognitive capabilities. Through comprehensive theo retical analysis and simulation studies, we demonstrate that NQHL achieves 1000x energy efficiency improve ments 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.

DOI: doi.org/10.63721/25JPAIR0117

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