Magic-Informed Quantum Architecture Search

TL;DR

This paper introduces a quantum architecture search method guided by a neural network that estimates and controls the 'magic' resource in quantum circuits, improving solution quality.

Contribution

It presents a novel magic-informed quantum architecture search using Monte Carlo Tree Search and Graph Neural Networks to steer quantum circuit design based on magic levels.

Findings

Effective control of magic in quantum circuits demonstrated.

Improved solution quality across various quantum problems.

GNN-based bias influences final circuit magic levels.

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.