Quantum-Inspired Memory Networks with Adaptive Decoherence

Jincheng Zhang

Published Aug 29, 2026• 0 citations

Abstract

Classical Memory Networks (MemNets) grapple with the "forgetting" problem, struggling to maintain relevant information over extended sequences. This paper proposes a novel architecture – Quantum-Inspired Memory Networks with Adaptive Decoherence – designed to mitigate this issue. The core idea is to introduce a decoherence mechanism inspired by quantum physics, allowing the network to dynamically adjust the decay rate of information stored in memory slots. This adaptation is governed by relevance and usage patterns, leading to optimized information retention and retrieval. Unlike existing approaches that rely on fixed decay rates or simple gating mechanisms, our system leverages an adaptive decoherence rate, providing a more nuanced and effective strategy for managing memory in sequence modeling tasks. We demonstrate the potential of this approach through a theoretical analysis and outline a framework for implementation.

DOI: 10.5281/zenodo.22152463

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