A Coverage-Guided Testing Framework for Quantum Neural Networks
This addresses verification challenges for QNNs, which are critical for applications like quantum chemistry and optimization, but it is an incremental advance in testing methods.
The paper tackles the reliability and safety issues of Quantum Neural Networks (QNNs) by proposing QCov, a coverage-guided testing framework that improves QNN robustness through efficient fuzz testing, as validated on benchmark datasets and models.
Quantum Neural Networks (QNNs) integrate quantum computing and deep neural networks, leveraging quantum properties like superposition and entanglement to enhance machine learning algorithms. These characteristics enable QNNs to outperform classical neural networks in tasks such as quantum chemistry simulations, optimization problems, and quantum-enhanced machine learning. Despite their early success, their reliability and safety issues have posed threats to their applicability. However, due to the inherently non-classical nature of quantum mechanics, verifying QNNs poses significant challenges. To address this, we propose QCov, a set of test coverage criteria specifically designed to systematically evaluate QNN state exploration during testing, with an emphasis on superposition. These criteria help evaluate test diversity and detect underlying defects within test suites. Extensive experiments on benchmark datasets and QNN models validate QCov's effectiveness in reflecting test quality, guiding fuzz testing efficiently, and thereby improving QNN robustness. We also evaluate sampling costs of QCov under realistic quantum scenarios to justify its practical feasibility. Finally, the effects of unrepresentative training data distribution and parameter choice are further explored.