Title: Adaptive Quantum Circuit Design – Neural Network-Guided Optimization

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

This paper presents an adaptive quantum circuit design framework leveraging a neural network to automate the process of circuit creation. Traditional quantum circuit design is often a laborious and iterative process, relying heavily on expert knowledge and trial-and-error. We introduce a novel system that utilizes a trained neural network to automatically generate quantum circuits with optimized properties, aiming to significantly reduce manual effort and accelerate quantum algorithm development. The framework employs a multi-stage training process, beginning with a pre-trained model and iteratively refining it based on a defined set of performance metrics. The core mechanism centers on the neural network's ability to learn the optimal circuit parameters to maximize desired properties such as fidelity, coherence, and connectivity. This approach promises to provide a more efficient and scalable method for quantum circuit design.

DOI: 10.5281/zenodo.22453180

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