Toward Quantum Circuit Execution Success Estimation via Graph Neural Network-Based Prediction

Quantum computing has attracted meaningful interest in recent years due to rapid advancements in applications such as optimization, cryptography, and machine learning. However, the successful implementation of quantum algorithms on real quantum hardware requires selecting the most suitable device via detailed performance evaluation, which is both time- and cost-intensive. This article proposes an automated framework to estimate the Probability of Success per Trial (PST) of executing quantum circuits on specific quantum devices. The approach involves constructing a dataset involving more than 4000 quantum circuits from the MQT Bench set. These circuits are then converted into Directed Acyclic Graph (DAG) representations, and Graph Neural Network (GNN)-based models are trained to predict the optimal associated PST for a target device, avoiding a brute-force exploration based on compiling and executing the quantum circuit, varying the compilation setting. The trained models have achieved good accuracy in estimating the PST value, guaranteeing a Root Mean Squared Error (RMSE) lower than 0.1 for all three target devices considered, significantly reducing the time and cost required for estimating the metric and proving the effectiveness of the proposed methodology