noisy quantum circuit classification (quantum ML, error mitigation))

Classifying noisy quantum circuits is a key technology for performing quantum machine learning on noisy quantum devices. This task, through error mitigation methods, aims to improve the classification accuracy of quantum circuits in real quantum computing environments. Its core objective is to develop and optimize algorithms to effectively address the impact of quantum noise on computational results, thereby enhancing the robustness and reliability of quantum machine learning models. This technology has significant application value in fields such as quantum chemistry, quantum optimization, and quantum data science.