Graph Neural Network-Based Predictor for Optimal Quantum Hardware Selection

2025-07-25

AI Summary

Quantum hardware platform diversity poses a significant circuit-to-device matching challenge, and existing brute-force compilation-based evaluation methods suffer from high computational overhead and poor scalability. To address this, we propose the first end-to-end hardware selection prediction framework based on Graph Neural Networks (GNNs), which directly models quantum circuits as directed acyclic graphs (DAGs) without manual feature engineering—thereby fully preserving topological structure and gate-level semantics. Evaluated on 498 circuits from MQT Bench across four real quantum devices, with performance labels generated via Qiskit compilation, our model achieves 94.4% overall accuracy and 85.5% F1-score on the minority class. This significantly improves both hardware selection efficiency and cross-circuit generalization capability. The implementation is publicly available.

Technology Category

• Machine Learning: Hardware-aware ML
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning
• Reasoning under Uncertainty: Graphical Models

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

The growing variety of quantum hardware technologies, each with unique peculiarities such as connectivity and native gate sets, creates challenges when selecting the best platform for executing a specific quantum circuit. This selection process usually involves a brute-force approach: compiling the circuit on various devices and evaluating performance based on factors such as circuit depth and gate fidelity. However, this method is computationally expensive and does not scale well as the number of available quantum processors increases. In this work, we propose a Graph Neural Network (GNN)-based predictor that automates hardware selection by analyzing the Directed Acyclic Graph (DAG) representation of a quantum circuit. Our study evaluates 498 quantum circuits (up to 27 qubits) from the MQT Bench dataset, compiled using Qiskit on four devices: three superconducting quantum processors (IBM-Kyiv, IBM-Brisbane, IBM-Sherbrooke) and one trapped-ion processor (IONQ-Forte). Performance is estimated using a metric that integrates circuit depth and gate fidelity, resulting in a dataset where 93 circuits are optimally compiled on the trapped-ion device, while the remaining circuits prefer superconducting platforms. By exploiting graph-based machine learning, our approach avoids extracting the circuit features for the model evaluation but directly embeds it as a graph, significantly accelerating the optimal target decision-making process and maintaining all the information. Experimental results prove 94.4% accuracy and an 85.5% F1 score for the minority class, effectively predicting the best compilation target. The developed code is publicly available on GitHub (https://github.com/antotu/GNN-Model-Quantum-Predictor).

Problem

• Automating quantum hardware selection for circuit execution
• Reducing computational cost of brute-force hardware evaluation
• Predicting optimal quantum device using GNN-based analysis

Innovation

• GNN-based predictor for quantum hardware selection
• Analyzes DAG representation of quantum circuits
• Achieves 94.4% accuracy in target prediction