Quantum Neural Networks (QNNs) combine quantum computing and neural networks, leveraging quantum properties such as superposition and entanglement to improve machine learning models. These quantum characteristics enable QNNs to potentially outperform classical neural networks in tasks such as quantum chemistry simulations, optimization problems, and quantum-enhanced machine learning. However, they also introduce significant challenges in verifying the correctness and reliability of QNNs. To address this, we propose QCov, a set of test coverage criteria specifically designed for QNNs to systematically evaluate QNN state exploration during testing, focusing on superposition and entanglement. These criteria help detect quantum-specific defects and anomalies. Extensive experiments on benchmark datasets and QNN models validate QCov's effectiveness in identifying quantum-specific defects and guiding fuzz testing, thereby improving QNN robustness and reliability.
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