🤖 AI Summary
This study systematically evaluates the practical contribution of quantum components—such as encoding strategies, entanglement structures, and circuit depth—to the performance of hybrid quantum-classical neural networks. Through controlled experiments on multimodal real-world datasets, including medical signals and two- and three-dimensional images, the work provides the first quantitative analysis of the role played by quantum modules within the overall architecture. The results demonstrate that, in most configurations, incorporating quantum components actually degrades model performance, with parity to purely classical models achieved only under optimal settings. These findings challenge prevailing optimistic assumptions about near-term quantum advantage and offer empirical grounding and cautious guidance for the practical design of hybrid quantum-classical systems.
📝 Abstract
Quantum machine learning has emerged as a promising application domain for near-term quantum hardware, particularly through hybrid quantum-classical models that leverage both classical and quantum processing. Although numerous hybrid architectures have been proposed and demonstrated successfully on benchmark tasks, a significant open question remains regarding the specific contribution of quantum components to the overall performance of these models. In this work, we aim to shed light on the impact of quantum processing within hybrid quantum-classical neural network architectures through a rigorous statistical study. We systematically assess common hybrid models on medical signal data as well as planar and volumetric images, examining the influence attributable to classical and quantum aspects such as encoding schemes, entanglement, and circuit size. We find that in best-case scenarios, hybrid models show performance comparable to their classical counterparts, however, in most cases, performance metrics deteriorate under the influence of quantum components. Our multi-modal analysis provides realistic insights into the contributions of quantum components and advocates for cautious claims and design choices for hybrid models in near-term applications.