Quantum Algorithm Acceleration Based on Biological Neural Networks

Jincheng Zhang

Published Sep 1, 2026• 0 citations

Abstract

This paper proposes a novel approach to quantum algorithm acceleration by leveraging the inherent parallelism of biological neural networks. The core idea is to construct a hardware platform that mimics the architecture and computational properties of biological neural networks to serve as an accelerator for quantum algorithms. This approach aims to overcome the limitations of traditional quantum computing architectures, which often suffer from bottlenecks in qubit control and communication. We outline a framework for designing such an accelerator, focusing on mapping quantum algorithms onto biologically inspired parallel processing structures. The potential benefits of this hybrid approach – combining the computational strengths of both biological and quantum systems – are significant, potentially leading to substantial improvements in quantum computing performance and efficiency. This work establishes a theoretical foundation and proposes a novel direction for quantum computing research, focusing on bio-inspired hardware solutions.

DOI: 10.5281/zenodo.22222762

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