Magic-Informed Quantum Architecture Search

2026-05-05

AI Summary

This work addresses the challenge of targeted control over “magic”—a key resource for quantum advantage stemming from non-stabilizerness—in quantum circuit design by proposing a magic-aware quantum architecture search method that integrates Monte Carlo tree search with graph neural networks. For the first time, magic is explicitly incorporated as an optimization objective, enabling the graph neural network to guide the search toward regions of either high or low magic, thereby overcoming the limitations of conventional unbiased search strategies. Evaluated on ground-state energy estimation and quantum state approximation tasks, the proposed approach not only effectively modulates the magic content of synthesized circuits but also demonstrates superior performance on out-of-distribution instances, confirming its generalization capability and practical utility.

Abstract

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.

Problem

• magic
• quantum architecture search
• nonstabilizerness
• quantum circuit design
• quantum resource

Innovation

• magic
• quantum architecture search
• Monte Carlo Tree Search
• Graph Neural Network
• nonstabilizerness