QuanForge: A Mutation Testing Framework for Quantum Neural Networks

TL;DR

QuanForge is a specialized mutation testing framework for Quantum Neural Networks that introduces statistical mutation killing and multiple mutation operators to evaluate QNN robustness.

Contribution

It presents the first comprehensive mutation testing framework for QNNs, including novel mutation operators and a mutant generation algorithm tailored for quantum circuits.

Findings

QuanForge effectively distinguishes different test suites for QNNs.

It localizes vulnerable circuit regions in QNNs.

Performance is validated under simulated noisy quantum conditions.

Abstract

With the growing synergy between deep learning and quantum computing, Quantum Neural Networks (QNNs) have emerged as a promising paradigm by leveraging quantum parallelism and entanglement. However, testing QNNs remains underexplored due to their complex quantum dynamics and limited interpretability. Developing a mutation testing technique for QNNs is promising while requires addressing stochastic factors, including the inherent randomness of mutation operators and quantum measurements. To tackle these challenges, we propose QuanForge, a mutation testing framework specifically designed for QNNs. We first introduce statistical mutation killing to provide a more reliable criterion. QuanForge incorporates nine post-training mutation operators at both gate and parameter levels, capable of simulating various potential errors in quantum circuits. Finally, a mutant generation algorithm is formalized that systematically produces effective mutants, thereby enabling a robust and reliable mutation analysis. Through extensive experiments on benchmark datasets and QNN architectures, we show that QuanForge can effectively distinguish different test suites and localize vulnerable circuit regions, providing insights for data enhancement and structural assessment of QNNs. We also analyze the generation capabilities of different operators and evaluate performance under simulated noisy conditions to assess the practical feasibility of QuanForge for future quantum devices.