# QuanForge: A Mutation Testing Framework for Quantum Neural Networks

arXiv: https://arxiv.org/abs/2604.20706 · HTML: https://mlsanity.com/paper/2604.20706/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 58 references that are in the map.
- First posted on arXiv 22 Apr 2026.

## Builds on (most cited first, 19 of 19)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Certified Adversarial Robustness via Randomized Smoothing](https://mlsanity.com/paper/1902.02918/) (2019)
- [Barren plateaus in quantum neural network training landscapes](https://mlsanity.com/paper/1803.11173/) (2018)
- [Absence of Barren Plateaus in Quantum Convolutional Neural Networks](https://mlsanity.com/paper/2011.02966/) (2020)
- [DeepMutation: Mutation Testing of Deep Learning Systems](https://mlsanity.com/paper/1805.05206/) (2018)
- [Quantum Adversarial Machine Learning](https://mlsanity.com/paper/2001.00030/) (2019)
- [Adversarial Sample Detection for Deep Neural Network through Model Mutation Testing](https://mlsanity.com/paper/1812.05793/) (2018)
- [Causality-based Neural Network Repair](https://mlsanity.com/paper/2204.09274/) (2022)
- [Towards quantum enhanced adversarial robustness in machine learning](https://mlsanity.com/paper/2306.12688/) (2023)
- [Quantum Neural Network Classifiers: A Tutorial](https://mlsanity.com/paper/2206.02806/) (2022)
- [On Testing Quantum Programs](https://mlsanity.com/paper/1812.09261/) (2018)
- [Testing Multi-Subroutine Quantum Programs: From Unit Testing to Integration Testing](https://mlsanity.com/paper/2306.17407/) (2023)
- [Equivalence, Identity, and Unitarity Checking in Black-Box Testing of Quantum Programs](https://mlsanity.com/paper/2307.01481/) (2023)
- [MuFF: Stable and Sensitive Post-training Mutation Testing for Deep Learning](https://mlsanity.com/paper/2501.09846/) (2025)
- [A Black-box Testing Framework for Oracle Quantum Programs](https://mlsanity.com/paper/2505.07243/) (2025)
- [Quantum Concolic Testing](https://mlsanity.com/paper/2405.04860/) (2024)
- [QCRMut: Quantum Circuit Random Mutant generator tool](https://mlsanity.com/paper/2410.01415/) (2024)
- [NovaQ: Improving Quantum Program Testing through Diversity-Guided Test Case Generation](https://mlsanity.com/paper/2509.04763/) (2025)

## Abstract (arXiv)

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.