Design of a hybrid quantum machine learning architecture and analysis of quantum noise effects

Abstract Quantum Machine Learning (QML) is emerging as a promising technology for tackling complex computational challenges, although its practical implementation faces significant obstacles owed to the inherent noise in current quantum devices. This paper presents a hybrid architecture that combines classical and quantum elements for the development and training of QML models under noisy conditions. This research evaluates the impact of noise on superconducting systems through emulation. The study shows that, compared to noise-free configurations, certain noise levels tend to allow for a reduction in the number of qubits, thus simplifying the architecture of the quantum neural network, which has a direct impact on the computational cost and execution times. Experimental validation was performed by applying three biomedical datasets related to breast cancer detection. The experimental findings revealed that the variations in accuracy between noiseless configurations and those subjected to noisy conditions were minimal, with deviations ranging from 0.11% to 1.68%. Additionally, it was observed that the incorporation of noise during training contributed positively to the efficiency of the process for the datasets under test, achieving improvements in training execution times ranging from a factor of 1.61 to 4.39, when the proposed architecture was emulated on Qaptiva 802.