Non-Linear Quantum Circuit Optimization

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Quantum circuit optimization is a crucial challenge in quantum computing, particularly for realizing complex quantum algorithms. Traditional optimization methods often rely on manually defined parameters, limiting their adaptability to varying circuit designs. This paper introduces a novel approach based on machine learning to dynamically adjust circuit structure during optimization, thereby enhancing circuit efficiency. We propose a self-adaptive optimization strategy that learns circuit characteristics and automatically refines the circuit's layout to achieve optimal performance. The core mechanism leverages a neural network to learn and predict optimal circuit parameter adjustments, allowing for a more flexible and efficient optimization process. This research offers a significant advancement in quantum circuit design, addressing a key limitation of existing techniques.

DOI: 10.5281/zenodo.22199080

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