Based on Quantum Coherence: Adaptive Neural Networks

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

This paper introduces a novel neural network architecture termed the Adaptive Neural Network based on Quantum Coherence (ANQNQC). The core claim of this work is that real-time monitoring and dynamic adjustment of quantum coherence distribution within a neural network can lead to a more efficient and robust learning process compared to traditional deep learning models. The underlying mechanism involves integrating "quantum coherence sensors" into each neuron, enabling the measurement of its internal quantum state coherence. This sensor data is fed in real-time to a control module employing reinforcement learning algorithms, which dynamically adjusts neuron connection weights, activation thresholds, and quantum state modulation parameters to optimize coherence distribution. The network incorporates error correction coding, utilizing quantum coherence as an error signal for self-correction. This approach aims to reduce computational complexity, minimize information redundancy, and enhance resilience to noise and perturbations. The research represents a paradigm shift by leveraging quantum phenomena within neural networks, potentially overcoming the computational bottlenecks of classical networks and opening new avenues for efficient computation based on quantum mechanical principles. ---

DOI: 10.5281/zenodo.22578637

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