Quantum-Inspired Optimization of Deep Learning Architectures

Jincheng Zhang

Published Aug 29, 2026• 0 citations

Abstract

This paper proposes a novel approach to optimizing deep learning architectures, drawing inspiration from the principles of quantum mechanics. Traditional deep learning architecture optimization relies heavily on trial-and-error methods, often hampered by the vast search space and lack of efficient strategies. We introduce a quantum-inspired optimization algorithm that utilizes probabilistic simulations mirroring quantum superposition and interference to evaluate multiple network architectures concurrently. The core idea is to model the architecture search process as a quantum computation, where different architectures are represented as quantum states and evaluated through simulated interference effects. This allows for a significantly faster exploration of the architecture space compared to conventional methods. The algorithm aims to identify optimal architectures by exploiting the inherent parallelism and potential for constructive interference within the simulated quantum system. The primary contribution lies in translating quantum concepts into a practical optimization framework for deep learning, potentially leading to improved model accuracy, faster training times, and more efficient resource utilization. The theoretical framework is presented, along with a detailed explanation of the algorithm's components and a discussion of its potential benefits and limitations.

DOI: 10.5281/zenodo.22158315

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