Title: Adaptive Quantum Circuit Design for Optimization

Jincheng Zhang

Published Sep 1, 2026• 0 citations

Abstract

Quantum computing promises revolutionary advancements in optimization, particularly in complex search spaces and machine learning. However, designing optimal quantum circuits for these tasks presents significant challenges, largely due to the inherent variability of problem landscapes. This research proposes a novel adaptive quantum circuit design methodology leveraging meta-learning to dynamically adjust circuit parameters based on the characteristics of the optimization problem. We introduce a meta-learning framework that learns optimal circuit configurations from a representative set of problem instances, enabling the circuit to adapt to the unique properties of each new problem. The core mechanism centers around a feedback loop that continuously assesses the circuit's performance and adjusts parameters to improve solution quality. This approach aims to overcome the limitations of traditional fixed-parameter circuit design, offering a path towards more robust and efficient optimization solutions. This work explores the potential for significant improvements in solution quality across a range of benchmark problems, demonstrating the efficacy of the proposed adaptive design strategy.

DOI: 10.5281/zenodo.22223644

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