Quantum Neuro-Morphic Computing Architecture

Jincheng Zhang

Published Aug 29, 2026• 0 citations

Abstract

This paper proposes a novel quantum neuro-morphic computing architecture designed to enhance computational efficiency and parallelism. The core concept involves integrating quantum logic gates with neural networks to create a hybrid system capable of leveraging the strengths of both paradigms. We explore the potential of this architecture to achieve significant improvements in computational performance, particularly for complex problems that benefit from parallel processing and quantum phenomena. The architecture is predicated on a fundamentally new approach to computation, moving beyond traditional von Neumann architectures and exploring the synergistic relationship between quantum and neuromorphic computing. The primary goal is to develop a scalable and efficient computing solution that can tackle problems currently intractable for either quantum or classical systems alone. This paper outlines the key components of the architecture, the underlying mathematical framework, and the potential applications of this integrated system.

DOI: 10.5281/zenodo.22158150

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