Quantum Neural Networks with Adaptive Topological Structures Learning

Jincheng Zhang

Published Aug 28, 2026• 0 citations

Abstract

This paper presents a novel approach to designing and implementing quantum neural networks (QNNs) capable of adapting their topological structures through a learning process. The core idea is to leverage reinforcement learning (RL) to guide the QNN's evolution, enabling it to dynamically optimize its architecture for specific tasks. Traditional QNNs typically employ fixed, pre-defined topologies, limiting their flexibility and potential performance. This work introduces a framework where the QNN autonomously learns and adjusts its topology, representing a significant advancement in QNN development. We detail the theoretical foundations, the RL algorithm employed, and the overall learning process. The system's ability to adapt to varying input data and computational demands is demonstrated, showcasing the potential of self-organizing QNNs for complex problems. The system's performance is evaluated using synthetic benchmarks, demonstrating the efficacy of the adaptive topological structure learning method. This research contributes to the burgeoning field of adaptive quantum computing, paving the way for more robust and efficient QNN designs. ---

DOI: 10.5281/zenodo.22137895

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