论文标题
AutoQML：Wi-Fi集成感应和通信的自动化量子机学习
AutoQML: Automated Quantum Machine Learning for Wi-Fi Integrated Sensing and Communications
论文作者
论文摘要
商业Wi-Fi设备可用于集成感应和通信（ISAC），以共同交换数据并监视室内环境。在本文中，我们使用称为Autoansatz的自动量子机学习（AUTOQML）框架来研究一种概念验证方法，以识别人类的手势。我们解决如何有效设计量子电路以配置量子神经网络（QNN）。自动QML的有效性通过人体姿势识别的内部实验来验证，对于有限的数据大小，可训练参数的有限数据大小，实现最先进的性能大于80％的精度。
Commercial Wi-Fi devices can be used for integrated sensing and communications (ISAC) to jointly exchange data and monitor indoor environment. In this paper, we investigate a proof-of-concept approach using automated quantum machine learning (AutoQML) framework called AutoAnsatz to recognize human gesture. We address how to efficiently design quantum circuits to configure quantum neural networks (QNN). The effectiveness of AutoQML is validated by an in-house experiment for human pose recognition, achieving state-of-the-art performance greater than 80% accuracy for a limited data size with a significantly small number of trainable parameters.
