Noise-Robust Hybrid Quantum Neural Networks

Abstract

uantum computing offers the potential to solve selected computational problems more efficiently than classical systems by leveraging quantum mechanical principles such as superposition and entanglement. However, current quantum devices operate in the noisy intermediatescale quantum (NISQ) regime, where limited coherence times, gate errors, measurement instability, and hardware variability significantly constrain practical performance. As a result, many quantum machine learning architectures that appear promising under idealized simulation conditions experience substantial degradation when executed under realistic noisy environments. This thesis investigates the problem of robustness in hybrid quantum neural network (HQNN) systems operating under realistic NISQ conditions. Rather than focusing primarily on quantum expressivity or claims of quantum advantage, the work examines whether practical hybrid quantum learning can maintain stable optimization and reliable inference in the presence of stochastic perturbations. A robustness-engineering framework is developed that integrates learned-readout decoding, multi-observable quantum feature extraction, architecture-level optimization, stability-aware training, and noise-augmented learning within a unified hybrid quantum-classical pipeline. To evaluate the framework, a comprehensive benchmarking methodology is developed across multiple datasets, architectures, perturbation environments, software frameworks, and hardware platforms. The experiments compare classical machine learning models, quantum kernel methods, and several HQNN architectures, including quanvolutional neural networks, quantum convolutional neural networks, and quantum transfer learning systems. Performance is evaluated under both noiseless and noisy conditions using standardized metrics, cross-noise validation, architecture-level comparisons, and real IBM Quantum hardware execution. The results demonstrate that baseline HQNN systems are highly sensitive to optimization instability, parity-based information loss, measurement variability, and perturbation accumulation. The experiments further reveal that robustness, rather than representational expressivity alone, is the dominant factor limiting practical hybrid quantum learning under realistic NISQ conditions. Through successive robustness-engineering stages, noisy performance improves from approximately 0.37 to 0.50 in baseline architectures to approximately 0.97 under several benchmark environments while maintaining substantially improved stability and reduced degradation under perturbation. These findings establish robustness as a central design objective for practical quantum machine learning. The thesis demonstrates that reliable hybrid quantum learning emerges through coordinated interaction between quantum feature generation, adaptive classical inference, stability-aware optimization, and hardware-aware architecture design. More broadly, the results suggest that hybrid quantum-AI systems are most effective when viewed as integrated quantum-classical computational frameworks in which quantum circuits function as probabilistic feature generators operating within larger robustness-oriented learning pipelines.