Quantum-Assisted Optimization of Neural Network Architecture

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

Quantum-assisted optimization of neural network architecture is a burgeoning field aiming to accelerate the design and training of deep learning models. This paper explores the application of quantum annealing to this domain, proposing a novel algorithm that leverages quantum phenomena to efficiently explore the vast parameter space of neural network architectures. We detail the core mechanism, highlighting the unique advantages of quantum annealing in tackling the inherent complexities of neural network design. The paper presents a comprehensive analysis of the theoretical underpinnings, experimental validation, and potential future directions of this research. The core claim is that quantum annealing can significantly improve the performance and efficiency of neural network architectures compared to classical optimization methods, particularly in complex scenarios.

DOI: 10.5281/zenodo.22265865

Related papers

Quantum-Inspired Optimization for Neural Network Training

Jincheng Zhang

Quantum-inspired optimization offers a promising approach to accelerate and stabilize neural network training, addressing the limitations of classical optimization methods. This paper explores the implementation of a novel quantum-inspired technique, incorporating quantum annealing and enhanced gradient descent, to enhance the learning process and mitigate overfitting. We provide a detailed examination of the theoretical underpinnings, experimental results, and potential applications of this approach. The core claim centers around achieving significant improvements in training speed and model robustness compared to traditional optimization methods. We present a comprehensive analysis of the algorithm's effectiveness, highlighting its advantages and limitations.

Aug 29, 20260 citations

Quantum-Inspired Optimization for Deep Neural Network Architecture Search

Jincheng Zhang

The process of designing optimal deep neural network architectures is a computationally expensive and often manual task. Traditional gradient-based optimization methods struggle to efficiently explore the vast architectural search space, leading to sub-optimal designs. This research proposes a novel approach leveraging quantum-inspired optimization techniques, specifically quantum annealing, to accelerate the architecture search process. We formulate the architecture search problem as a minimization task within a quantum annealing landscape. The objective function is designed to reflect the performance metrics of a given neural network architecture. Our approach offers a potentially more efficient method for discovering high-performing architectures by harnessing the principles of quantum mechanics, particularly superposition and tunneling, to navigate the complex search space. The core claim is that gradient-based optimization is often inefficient for this task, and quantum annealing provides a viable alternative. This paper outlines the theoretical framework and proposes an initial methodology for applying quantum annealing to deep neural network architecture search.

Sep 5, 20260 citations

Quantum-Driven Bayesian Optimization of Neural Network Architecture

Jincheng Zhang

Quantum-driven Bayesian optimization (QBO) presents a novel approach to neural network architecture search, employing quantum annealing or QAOA to efficiently explore the vast design space. This research investigates the potential of quantum algorithms to accelerate the discovery of optimal neural network architectures, addressing limitations of traditional methods like grid search. The core mechanism centers on utilizing quantum annealing to sample neural network designs, enabling a significantly broader exploration of potential architectures compared to classical methods. The study will analyze the impact of quantum optimization on the search process, focusing on speed and the ability to uncover unexpected architectural patterns. The paper will detail the implementation of a quantum algorithm, its performance compared to classical approaches, and the potential benefits of leveraging quantum mechanics for neural network design. This work aims to provide a fundamental understanding of the application of quantum optimization to neural network architecture discovery.

Sep 6, 20260 citations

Quantum-Enhanced Optimization of Neural Network Architecture

Jincheng Zhang

Quantum computing holds the potential to revolutionize various fields, and the field of neural networks is no exception. This research investigates the application of quantum optimization algorithms to enhance the design of neural network architectures, aiming to improve performance across specific tasks. We explore the feasibility of utilizing a novel quantum optimization technique to systematically explore network architectures and identify configurations that yield superior results. This work presents a theoretical framework and preliminary results demonstrating the potential for quantum acceleration in neural network architecture design. The core claim centers around the design of a quantum algorithm capable of efficiently navigating a complex space of possible network architectures, ultimately leading to optimized neural network configurations. The research will focus on identifying key architectural parameters and evaluating their impact on performance using benchmark datasets. This paper provides a foundational analysis of this approach and outlines potential avenues for future exploration.

Sep 6, 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

Quantum-Inspired Optimization of Deep Neural Network Architectures

Jincheng Zhang

The design of deep neural network (DNN) architectures is a complex and computationally intensive task. Traditional methods often rely on manual tuning or random search, which are inefficient in exploring the exponentially large architectural space. This paper proposes a novel approach to DNN architecture optimization leveraging quantum-inspired algorithms. Specifically, we explore the application of algorithms inspired by quantum annealing and quantum Monte Carlo methods to efficiently search for optimal DNN configurations. Our framework aims to minimize the trade-off between accuracy, latency, and energy consumption – key considerations in real-world DNN deployments. We present a theoretical framework and outline a methodology for integrating these quantum-inspired techniques. The core idea is to map the architectural design problem onto a suitable quantum system, allowing for the parallel exploration of potential solutions. This research provides a new direction for automated DNN design, potentially leading to significant improvements in performance and efficiency.

Sep 2, 20260 citations