Dynamic Topology of Quantum Neural Networks

Jincheng Zhang

Published Sep 5, 2026• 0 citations

Abstract

Quantum Neural Networks (QNNs) offer a promising avenue for accelerating machine learning algorithms by leveraging quantum phenomena. This paper explores the concept of dynamic topology in QNNs, proposing a novel architecture that allows the network's neural network structure and weights to adapt in real-time based on input data. We introduce a dynamic topology mechanism that iteratively refines the network's connectivity, enabling the QNN to learn more effectively and achieve superior performance compared to static QNNs. The proposed model demonstrates significant improvements in various learning tasks, particularly in complex pattern recognition and optimization.

DOI: 10.5281/zenodo.22322630

Related papers

Quantum Neural Networks: Dynamic Topology Control

Jincheng Zhang

Quantum Neural Networks (QNNs) represent a promising paradigm shift in artificial intelligence, leveraging the unique properties of quantum mechanics to enhance computational capabilities. This paper explores the concept of dynamic topology control within QNNs, focusing on the design of a novel architecture that dynamically adjusts the network's topology to optimize learning and expressiveness. We propose a quantum neural network architecture incorporating a self-adaptive topology, achieved through the manipulation of quantum states. This dynamic adjustment allows the network to learn and represent data more effectively than traditional neural networks, offering potential improvements in both accuracy and efficiency. The core mechanism hinges on the application of quantum state transformations to dynamically shape the network's topology, fostering a more flexible and adaptable learning process. We present a preliminary implementation demonstrating the benefits of this approach, showcasing increased model performance and a novel approach to represent data.

Aug 29, 20260 citations

Quantum Neural Networks: Learning Topological Layers Through Dynamic Structure Optimization

Jincheng Zhang

Quantum Neural Networks (QNNs) represent a novel paradigm in neural network architecture, leveraging quantum mechanics to potentially surpass classical neural networks in terms of learning capabilities and expressiveness. This paper investigates the learning of topological layers within QNNs, proposing a dynamic structure optimization method that systematically evolves the network's representation to achieve enhanced model performance. We explore a framework where the network's topology, represented as a dynamic structure, is continuously refined through a series of learning steps. The core mechanism focuses on optimizing the network's representation by manipulating the underlying topology, ultimately leading to improved generalization and robustness. This research introduces a novel approach to learning topological layers, offering a potential pathway toward more sophisticated and adaptable neural network models.

Sep 6, 20260 citations

Quantum Neural Networks with Adaptive Topological Structures Learning

Jincheng Zhang

This paper presents a novel approach to designing and implementing quantum neural networks (QNNs) capable of adapting their topological structures through a learning process. The core idea is to leverage reinforcement learning (RL) to guide the QNN's evolution, enabling it to dynamically optimize its architecture for specific tasks. Traditional QNNs typically employ fixed, pre-defined topologies, limiting their flexibility and potential performance. This work introduces a framework where the QNN autonomously learns and adjusts its topology, representing a significant advancement in QNN development. We detail the theoretical foundations, the RL algorithm employed, and the overall learning process. The system's ability to adapt to varying input data and computational demands is demonstrated, showcasing the potential of self-organizing QNNs for complex problems. The system's performance is evaluated using synthetic benchmarks, demonstrating the efficacy of the adaptive topological structure learning method. This research contributes to the burgeoning field of adaptive quantum computing, paving the way for more robust and efficient QNN designs. ---

Aug 28, 20260 citations

Quantum Neural Networks with Dynamic Topological Structures

Jincheng Zhang

This paper presents a novel architecture for quantum neural networks (QNNs) characterized by dynamically adjustable topological structures. Traditional QNNs often suffer from limitations in scalability and adaptability, hindering their potential for complex computations. The proposed framework leverages quantum entanglement and interference to enable real-time modifications to the network's connectivity and topology. This dynamic capability is hypothesized to significantly enhance the learning efficiency and generalization performance of QNNs. We outline the theoretical foundations, detailing the mathematical formalism underpinning the dynamic topology control and its impact on network behavior. Specifically, we introduce a method for evolving the network's connectivity based on feedback from the output, creating a self-optimizing system. The core claim is that designing a QNN with the ability to dynamically change its topology will allow for more efficient learning and reasoning. The key mechanism relies on quantum entanglement and quantum interference to dynamically adjust the network's connection strength and topological structure. This research contributes to a deeper understanding of QNN design and opens new avenues for developing highly adaptive and powerful quantum computing systems.

Aug 31, 20260 citations

Quantum Neural Networks: Self-Organized Learning for Enhanced Pattern Recognition

Jincheng Zhang

This paper explores the potential of quantum neural networks (QNNs) leveraging self-organization to achieve superior performance in pattern recognition and classification tasks. The core concept involves constructing a QNN utilizing quantum phenomena like decoherence and entanglement, followed by employing self-organizing learning algorithms to dynamically adjust the network's connection weights and topology. We posit that this combination can significantly enhance learning efficiency and accuracy compared to classical neural networks. The theoretical framework presented outlines a novel approach, and while experimental validation is not included, the proposed architecture and learning process provide a foundational understanding for future research and development in this emerging field. Key elements include the mathematical formulation of the QNN's dynamics, the self-organization learning algorithm, and the evaluation metrics used to assess performance. The primary goal is to demonstrate a theoretical pathway toward a more efficient and robust QNN through self-adaptive learning.

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