Neuro-Morphic Quantum Computing: A Hardware Architecture Exploration

Jincheng Zhang

Published Sep 2, 2026• 0 citations

Abstract

This paper explores a novel hardware architecture for neuro-morphic quantum computing, aiming to leverage the strengths of both quantum and neuromorphic computing paradigms for enhanced parallel processing. The proposed architecture integrates quantum bits (qubits) with a neural network, utilizing the inherent parallelism of quantum computation and the adaptive learning capabilities of neuromorphic systems. This work outlines the core design principles and theoretical foundations, focusing on the efficient interaction between these two distinct computational modalities. The architecture's potential to significantly improve computational efficiency and scalability is discussed. The key elements include a quantum processing unit (QPU) coupled to a spiking neural network (SNN) via a carefully designed interface. The theoretical framework examines the impact of quantum entanglement and neural network plasticity on the overall computation. Further research directions are proposed to refine and validate this architecture.

DOI: 10.5281/zenodo.22245544

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