Quantum-Inspired Optimization of Neural Network Architecture

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper explores a novel approach to neural network architecture optimization inspired by quantum superposition and measurement. Traditional neural network architecture search methods, such as reinforcement learning or evolutionary algorithms, often suffer from computational bottlenecks and slow convergence rates. We propose a genetic algorithm that leverages quantum-inspired probability amplitudes to represent and evaluate neural network architectures. Each architecture is assigned a set of probability amplitudes, reflecting the potential for success. Through a simulated measurement process, the algorithm dynamically adjusts these amplitudes, favoring architectures with higher potential and accelerating the search for optimal configurations. The core idea is that quantum superposition allows us to explore a vast solution space concurrently, while the measurement process mimics the collapse of the wave function, focusing the search on promising candidates. This approach demonstrates the potential to significantly reduce the time and resources required for neural network architecture design, paving the way for more efficient and powerful deep learning models. We present a detailed theoretical framework and outline the key components of the algorithm, emphasizing its ability to mimic quantum phenomena for architectural optimization.

DOI: 10.5281/zenodo.22194188

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