Quantum-Inspired Optimization of Neural Network Architecture

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Quantum-inspired optimization represents a paradigm shift in neural network architecture design, leveraging the principles of quantum mechanics to enhance efficiency and generalization. This paper explores a novel framework, inspired by quantum systems, designed to optimize network structure through a dynamic interplay of quantum tunneling and superposition. We present a proposed optimization algorithm, incorporating quantum-like mechanisms, which aims to overcome limitations of traditional methods. This research focuses on the design of a new framework for neural network architecture, offering a potential path toward more robust and adaptable models.

DOI: 10.5281/zenodo.22194189

Related papers

Quantum-Inspired Optimization via Hybrid Neural Networks

Jincheng Zhang

Quantum-inspired optimization represents a promising avenue for accelerating the optimization of complex systems, leveraging the potential of quantum mechanics to guide the search process. This paper proposes a novel architecture that intelligently combines classical optimization techniques with quantum-inspired algorithms. We design a hybrid neural network framework that incorporates quantum-like principles, such as quantum-like activation functions and exploration strategies, to enhance the efficiency and effectiveness of optimization. The core claim is that this hybrid approach will significantly accelerate the optimization of complex systems compared to conventional methods. This work explores the integration of these concepts into a neural network architecture, detailing the design of the network's components and their interplay to achieve optimal results.

Aug 31, 20260 citations

Quantum-Inspired Optimization for Neural Networks

Jincheng Zhang

Quantum-inspired optimization is a burgeoning field aiming to leverage the principles of quantum mechanics to enhance the efficiency of training neural networks. This paper explores the application of quantum-like concepts, specifically superposition and entanglement, to optimize neural network parameters. We detail a novel algorithm, dubbed "Quantum-Guided Adaptation," which utilizes these principles to accelerate convergence and potentially improve the accuracy of neural network models. The research investigates the potential benefits of these methods compared to traditional optimization techniques, focusing on enhancing training speed and achieving better results, particularly for complex neural network architectures. The core mechanism centers on creating a representation of the network's parameters that mirrors quantum phenomena, allowing for parallel processing and exploration of parameter space. We demonstrate the effectiveness of this approach through simulations and preliminary analysis, suggesting a significant improvement in training time and model accuracy.

Aug 29, 20260 citations

Adaptive Quantum Computing Neural Network Architecture Optimization

Jincheng Zhang

This paper explores the integration of adaptive quantum computing principles into neural network architecture optimization. Traditional neural network training relies heavily on manual parameter tuning, often requiring extensive experimentation. We propose a novel framework, "Quantum-Adaptive Optimization," that dynamically adjusts network parameters based on quantum computation's inherent parallelism and probabilistic nature. This framework aims to automate the optimization process, enhancing model performance and efficiency through targeted parameter adjustments. The core mechanism leverages quantum-inspired algorithms to guide the adjustment of neural network weights and biases, leading to improved generalization and robustness. The paper details the framework's design, explores its implementation with a simplified quantum simulation, and analyzes its potential impact on various machine learning tasks. The results demonstrate a significant improvement in model accuracy and reduced training time compared to conventional methods.

Sep 6, 20260 citations

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

Quantum Neural Networks: An Adaptive Optimization Mechanism

Jincheng Zhang

Quantum Neural Networks (QNNs) represent a paradigm shift in neural network design, leveraging the unique properties of quantum mechanics to potentially surpass classical neural networks in terms of efficiency and accuracy. This paper explores the concept of a novel adaptive optimization mechanism within QNNs, utilizing quantum entanglement and quantum state adaptation to enhance training and inference processes. The core of this approach focuses on establishing a dynamic feedback loop that allows the network to continually refine its parameters and structure based on the inherent properties of its data. The proposed mechanism offers a potential path towards more robust and adaptable neural network architectures. We delve into the theoretical foundations and preliminary experimental validation of this approach, demonstrating its potential for accelerating learning and improving generalization performance.

Sep 3, 20260 citations

Title: Quantum-Inspired Optimization with Hybrid Neural Networks

Jincheng Zhang

Quantum-inspired optimization represents a paradigm shift in the field of optimization, aiming to mimic the inherent properties of quantum mechanics – specifically, entanglement and superposition – to enhance algorithm performance. This paper introduces a novel hybrid neural network architecture designed to exploit these principles, specifically through the utilization of a 'quantum-aware feature map' and a 'quantum-inspired momentum term.' We explore the integration of these mechanisms to improve the efficiency and robustness of optimization algorithms, particularly in complex, high-dimensional problem spaces. The core claim is that this hybrid approach yields significant improvements in convergence speed, solution quality, and adaptability compared to traditional optimization methods. We present a comprehensive analysis of the proposed architecture and demonstrate its effectiveness through illustrative examples.

Sep 1, 20260 citations