Sarayut-HQCNN: A Hybrid Quantum–Classical Neural Network with Semantic Stability Operator for Robust Classification Under Noise
Abstract Hybrid quantum–classical neural networks have emerged as a promising computational paradigm for learning complex data representations under the constraints of noisy intermediate-scale quantum devices. However, practical deployment remains limited by instability, noise sensitivity, uncertain decision boundaries, and semantic drift between input perturbations and model outputs. This paper proposes Sarayut-HQCNN, a Hybrid Quantum–Classical Neural Network equipped with a Semantic Stability Operator (SSO) for robust classification under noisy conditions. The proposed architecture combines classical feature extraction, variational quantum encoding, hybrid decision layers, and an explicit semantic stability gate. The SSO estimates output variance under controlled input perturbations and rejects unstable predictions before final classification. A triple-guard decision mechanism based on confidence, class margin, and semantic sigma is introduced to improve decision reliability. Experim...
