Quantum enhanced self supervised learning

该项目将有助于QCS WP9算法与WP8架构，控制和仿真的紧密联系。WP9的研究探讨了下一代NISQ设备如何用于解决工业相关问题，以及如何与经典数据源进行接口。该项目涉及混合量子经典机器学习网络，用于解决分类和决策问题，并将利用最先进的自监督学习（SSL）算法[arXiv：2002.05709]。SSL在处理未分类数据方面具有很大的优势，但需要比相应的监督算法更多的资源

This project will contribute to QCS WP9 Algorithms with strong links to WP8 Architectures, Control and Emulation. The research in WP9 investigates how next generation NISQ devices could be used to solve industrially relevant problems and how they can be interfaced with classical data sources. This project is concerned with hybrid quantum-classical machine learning networks for solving classification and decision problems and will utilize state-of-the-art self-supervised learning (SSL) algorithms [arXiv:2002.05709]. SSL has the great advantage of working with unclassified data but requires a lot more resources than corresponding supervised algorithms