QuanForge: A Mutation Testing Framework for Quantum Neural Networks

2026-04-22

AI Summary

This work addresses the lack of effective testing methodologies for quantum neural networks (QNNs) due to inherent quantum randomness and measurement noise. To this end, we propose QuanForge, the first systematic mutation testing framework tailored for QNNs. QuanForge introduces a novel mutation killing criterion grounded in statistical hypothesis testing to rigorously handle quantum uncertainty, and incorporates nine gate-level and parameter-level mutation operators designed to emulate realistic hardware errors. Experimental evaluation demonstrates that QuanForge effectively assesses test suite quality, precisely identifies vulnerable regions in quantum circuits, and thereby guides data augmentation and architectural refinement. The framework exhibits strong practical utility and robustness in noisy environments, offering a reliable foundation for the development and validation of QNNs.

Technology Category

• Machine Learning: Quantum Machine Learning
• Reasoning under Uncertainty: Stochastic Optimization
• Computer Vision: Adversarial Attacks & Robustness

Application Category

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

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.

Problem

• Quantum Neural Networks
• Mutation Testing
• Quantum Computing
• Software Testing
• Quantum Software Engineering

Innovation

• mutation testing
• quantum neural networks
• statistical mutation killing
• quantum circuit mutation
• post-training operators