Quantum Neuron Self-Adaptation

Jincheng Zhang

Published Sep 2, 2026• 0 citations

Abstract

Quantum Neuron Self-Adaptation is a novel quantum neural network architecture that leverages dynamic neuron connection weights to achieve efficient neural network operation. Traditional neural networks often rely on static weights, hindering their adaptability to dynamic input environments. This paper presents a new approach utilizing a quantum neuron self-adaptation mechanism, dynamically adjusting connection weights to minimize computational cost and maximize model performance. We demonstrate the efficacy of this architecture through a series of simulations, highlighting its ability to effectively handle complex data transformations and achieve superior performance compared to existing neural network models.

DOI: 10.5281/zenodo.22243930

Related papers

Adaptive Neuron Connection for Quantum Neural Networks

Jincheng Zhang

Quantum neural networks (QNNs) hold immense promise for revolutionizing artificial intelligence, offering the potential for enhanced learning and complex pattern recognition. However, training QNNs often requires significant computational resources and time, hindering their widespread adoption. This paper introduces an innovative approach to QNNs – the Adaptive Neuron Connection (ANC) – designed to address this challenge by implementing a dynamic adjustment mechanism for neural neuron connections. This system leverages the inherent adaptability of quantum systems to dynamically adjust connection weights based on input signal variations, thereby reducing computational burden and improving model performance. We detail the architecture of the ANC, its operational principles, and preliminary experimental results demonstrating its effectiveness in several benchmark datasets. The core of this design lies in a novel dynamic weight adjustment algorithm that ensures optimal network performance across a wide range of input conditions. This work provides a new framework for QNNs, offering a pathway towards more efficient and scalable learning.

Sep 2, 20260 citations

Quantum Neural Networks: Adaptive Neuron Network Structures

Jincheng Zhang

Quantum Neural Networks (QNNs) represent a novel approach to artificial neural networks, leveraging the unique properties of quantum mechanics to enhance computation capabilities. This paper investigates the design and implementation of a quantum neural network (QNN) that employs adaptive neuron structures, dynamically adjusting their connections based on input data. We explore the potential of quantum entanglement to facilitate this self-adaptive behavior, aiming to achieve improved efficiency and robustness compared to classical neural networks. The core mechanism centers around utilizing quantum entanglement to enable neurons to subtly influence each other's states, promoting a form of self-organization within the network. This research offers a promising avenue for advancing QNNs and opens up new possibilities for complex computational tasks.

Sep 5, 20260 citations

Self-Adaptive Quantum Neural Networks: Parameter Adjustment Algorithm

Jincheng Zhang

This paper introduces a novel self-adaptive quantum neural network (QNN) parameter adjustment algorithm. Traditional QNN parameter adjustment methods often rely on static adjustments, failing to effectively handle complex problems. Our algorithm dynamically adjusts the quantum neural network's parameters based on the input data, enabling automated and optimized training. We present a method that leverages a dynamic parameter adjustment strategy, improving the efficiency of quantum neural network training. The core mechanism centers around a feedback loop that continuously refines the network's parameters using the input data, leading to enhanced performance and adaptability. This work represents a significant advancement in QNN parameter optimization, offering a robust and automated approach to training.

Aug 29, 20260 citations

Self-Organizing Neural Networks for Quantum Computing Implementation

Jincheng Zhang

This paper investigates the implementation of self-organizing neural networks (SONNs) within a quantum computing framework. The core focus is on developing a novel SONN architecture capable of dynamically adjusting connection weights and neuron counts based on input data characteristics. Traditional neural networks often struggle with adapting to dynamic environments, necessitating a solution that can enhance learning efficiency. This work proposes a design incorporating adaptive mechanisms to facilitate this adaptation, promising a more efficient and flexible approach to quantum machine learning. The paper outlines the core mechanism, its potential benefits, and the challenges associated with this approach. The goal is to demonstrate a practical pathway toward harnessing the power of SONNs for quantum computations.

Aug 31, 20260 citations

Adaptive Quantum Neural Networks: Dynamic Architecture Optimization

Jincheng Zhang

This paper introduces an adaptive quantum neural network architecture optimization algorithm, termed "Quantum Self-Adaptation Adjustment" (QSAA). We propose a novel approach to automatically tune the network's structure and parameters, leveraging the inherent dynamic properties of quantum neural networks. The core mechanism centers around a "Quantum Self-Adaptation Adjustment" (QSAA) process, which employs quantum computing principles to learn more complex network configurations and parameter adjustments. The algorithm's design aims to improve model performance and generalization capabilities compared to traditional neural network approaches. We present preliminary results demonstrating the effectiveness of QSAA in optimizing network structure for a specific classification task, highlighting its potential for enhancing deep learning models.

Aug 31, 20260 citations

Based on Quantum Coherence: Adaptive Neural Networks

Jincheng Zhang

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. ---

Sep 7, 20260 citations