AQUA: Analytics-driven quantum neural network (QNN) user assistance for software validation

Abstract

This paper proposes a novel analytics-driven user assistance software validation approach for quantum neural network (QNN) codes. The proposed analytics-driven QNN user assistance (AQUA) for software validation considers user interactive feedback for constructing efficient QNN software. Our proposed AQUA is based on dynamic software testing and analysis due to undetermined qubit states in QNN which is hard to be tracked via static software analysis. AQUA is for plotting gradient variances to determine whether the QNN software suffers from local minima situations, which are called barren plateaus in QNN. By utilizing AQUA software validation, the stability, feasibility, and explainability of QNN software can be tested. AQUA has been tested using realworld case study with quantum convolutional neural network software for point cloud data processing in autonomous driving applications.