Quantum-Inspired Optimization of Deep Neural Network Architectures

Jincheng Zhang

Published Sep 2, 2026• 0 citations

Abstract

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.

DOI: 10.5281/zenodo.22241479

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