Bio-Inspired Algorithms for Mitigating Quantum Decoherence

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

Quantum decoherence remains a significant obstacle to the development of practical quantum computers. The rapid loss of quantum information due to environmental interactions severely limits the duration and fidelity of quantum computations. This paper investigates a novel approach to mitigating decoherence by drawing inspiration from biological neural networks, specifically the mechanisms of long-term potentiation (LTP) and long-term depression (LTD). We propose a bio-inspired algorithm that dynamically adjusts the interactions between quantum bits (qubits) based on simulated synaptic plasticity, aiming to enhance the stability and coherence of quantum systems. The core claim is that mimicking the adaptive nature of biological synapses can effectively counteract decoherence. The key mechanism involves translating LTP and LTD principles into a control strategy for qubit interactions. We present a mathematical framework for quantifying decoherence and demonstrate, through theoretical analysis, the potential of this approach. The proposed algorithm leverages the inherent resilience of biological systems to improve the robustness of quantum computations. Further research will explore the practical implementation of this algorithm in various quantum computing architectures.

DOI: 10.5281/zenodo.22599651

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