基于自适应几何变换的量子神经网络

Jincheng Zhang

Published Aug 29, 2026• 0 citations

Abstract

This paper presents a novel approach to quantum neural networks (QNNs) leveraging adaptive geometric transformations. Traditional QNNs often rely on fixed parameters, limiting their ability to adapt to complex data. We propose a dynamic parameter adjustment mechanism within the QNN architecture, enabling the network to learn and optimize its representation based on input data characteristics. This adaptive approach enhances the network's capacity and improves its overall performance, addressing a key limitation of conventional QNNs. The proposed method focuses on the core mechanism of dynamically adjusting network parameters using geometric transformations to achieve enhanced performance.

DOI: 10.5281/zenodo.22152724

Related papers

Quantum Neural Network Adjustment

Jincheng Zhang

Quantum Neural Networks (QNNs) represent a promising paradigm in artificial intelligence, leveraging the unique properties of quantum mechanics to potentially surpass classical neural networks. This paper investigates a novel approach to QNNs – Adaptive Quantum Neural Network Adjustment – designed to dynamically optimize network parameters, thereby enhancing performance across a range of applications. We propose a mechanism that iteratively adjusts the network's weights based on feedback from a quantum-enhanced learning process, fostering a more robust and adaptable system. The paper details the architecture of the adaptive adjustment mechanism, explores its impact on network performance through simulations, and provides a preliminary analysis of its potential benefits. The core of the work centers around a novel quantum-inspired optimization strategy to address the limitations of traditional training methods. This research highlights the potential of incorporating adaptive adjustment into QNN architectures to achieve superior results.

Aug 31, 20260 citations

Adaptive Topology Transformation for Quantum Optimization

Jincheng Zhang

Abstract: This paper presents a novel approach to quantum optimization leveraging adaptive topology transformation. Traditional optimization methods often rely on manually calibrated parameters, limiting their adaptability to complex data sets. We introduce a dynamically adjusted topology transformation strategy utilizing an adaptive neural network to learn optimal transformation parameters, resulting in significantly improved geometric modeling and simulation accuracy. The core mechanism centers around the network's ability to autonomously adjust parameter values based on input data, allowing for a more refined geometric representation and enhanced model performance. The research demonstrates the effectiveness of this approach through a series of simulations, showcasing its superior capabilities compared to existing methods.

Sep 3, 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

基于量子态的神经网络的自适应学习策略

Jincheng Zhang

This paper presents a novel neural network architecture leveraging quantum state dynamics for adaptive learning. Traditional neural networks often rely on static learning rates, limiting their ability to optimize for complex data patterns. We propose a quantum-inspired approach that utilizes the inherent properties of quantum states – specifically, self-avoidance and entanglement – to dynamically adjust learning parameters, resulting in enhanced prediction accuracy. This research explores the potential of harnessing quantum phenomena to improve the learning process, moving beyond the limitations of conventional methods. The core of this work is the development of a quantum state optimization algorithm that dynamically adjusts the network's parameters based on the quantum state's evolution. We analyze the impact of this dynamic adjustment on prediction accuracy and demonstrate a significant improvement over baseline techniques. The presented method offers a promising avenue for advancing neural network learning, particularly in scenarios demanding high precision and adaptability.

Sep 6, 20260 citations

基于量子自旋的自适应神经网络训练

Jincheng Zhang

This paper introduces a novel self-adaptive neural network training framework leveraging the inherent quantum spin properties. Traditional neural network optimization methods often rely on static parameter adjustments, limiting the network's adaptability and potential for generalization. We propose a quantum spin neural network (QSN) architecture that utilizes quantum spin dynamics to dynamically adjust network weights and biases during training. This framework allows for inherent self-adaptation, enabling the network to learn optimal representations and improve performance across a wider range of datasets and tasks. The study demonstrates the effectiveness of this approach through extensive simulations and preliminary experiments on a benchmark dataset, showcasing significant improvements in generalization and convergence speed compared to conventional methods. The core mechanism involves utilizing quantum spin dynamics to iteratively refine the network's parameters, promoting a more robust and flexible learning process. This research presents a promising avenue for advancing neural network research by incorporating quantum phenomena into the training paradigm.

Sep 6, 20260 citations

基于量子态的神经网络的自适应自校正机制

Jincheng Zhang

This paper introduces a novel neural network architecture, termed "Quantum-Adaptive Neural Network" (QANN), that leverages the unique properties of quantum states for self-correction and stable learning. Traditional neural networks often rely on static parameter adjustments, limiting their adaptability and stability. The QANN employs a dynamic mechanism based on quantum state evolution to proactively address and mitigate errors, achieving significantly enhanced learning performance and robustness. The core of this design lies in the utilization of quantum state self-entanglement to dynamically adjust network parameters, offering a fundamentally different approach to neural network training. The proposed method addresses the limitations of current techniques by providing a mechanism for continuous error correction, enhancing both the learning process and the network's overall resilience. The paper details the mathematical framework, experimental validation, and preliminary analysis of the QANN, demonstrating its potential for improved machine learning capabilities.

Sep 6, 20260 citations