Nonstabilizerness, commonly referred to as magic, is a fundamental resource underpinning quantum advantage. In this paper, we propose a magic-informed quantum architecture search (QAS) technique that enables control over a quantum resource within the general framework of circuit design. Inspired by the AlphaGo approach, we tackle the problem with a Monte Carlo Tree Search technique equipped with a Graph Neural Network (GNN) that estimates the magic of candidate quantum circuits. The GNN model induces a magic-based bias that steers the search toward either high- or low-magic regimes, depending on the target objective. We benchmark the proposed magic-informed QAS technique on both the structured ground-state energy problem and on the more general quantum state approximation problem, spanning different sizes and target magic levels. Experimental results show that the proposed technique effectively influences the magic across the search tree and notably also on the resulting final circuit, even in regimes where the GNN operates on out-of-distribution instances. Although introducing a problem-agnostic magic bias could, in principle, constrain the search dynamics, we observe consistent improvements in solution quality across all problems tested.

翻译：非稳定化能力（即通常所说的魔法）是支撑量子优势的基础资源。本文提出一种魔法引导的量子架构搜索（QAS）技术，可在电路设计的通用框架中实现对量子资源的可控调节。受AlphaGo方法启发，我们采用配备图神经网络（GNN）的蒙特卡洛树搜索技术来解决该问题，该网络能够估算候选量子电路的魔法值。GNN模型引入基于魔法的偏置项，根据目标需求引导搜索朝向高魔法或低魔法区域。我们分别在结构化基态能量问题与更通用的量子态近似问题上对该技术进行基准测试，涵盖不同规模与目标魔法水平。实验结果表明，即使GNN处理分布外实例时，该技术仍能有效影响搜索树及最终定稿电路中的魔法值。尽管引入与问题无关的魔法偏置在理论上可能约束搜索动态，但我们在所有测试问题中均观察到解质量的持续提升。