Quantum Neural Networks with Bio-Inspired Adaptive Learning

Jincheng Zhang

Published Sep 5, 2026• 0 citations

Abstract

This paper proposes a novel architecture for quantum neural networks (QNNs) inspired by biological neural networks, specifically focusing on adaptive learning capabilities. The core idea is to integrate biological mechanisms such as synaptic plasticity and long-range dependencies into the quantum learning process. The traditional approach to QNN training often relies on gradient-based methods, which can be inefficient and susceptible to local minima. This work introduces a bio-inspired approach that leverages the inherent parallelism and quantum superposition of QNNs to achieve more robust and adaptive learning. We formulate the learning process using a modified Hebbian learning rule combined with a quantum measurement process. The key innovation lies in dynamically adjusting the quantum gates based on the network's internal state, mimicking the neural network's ability to adapt to changing environments. The resulting QNN architecture exhibits enhanced learning speed and improved generalization performance compared to conventional QNNs. The theoretical framework is presented, and the underlying mathematical equations are detailed, providing a robust foundation for further research and development in this field.

DOI: 10.5281/zenodo.22338318

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