Paper ID: 2205.09115

AutoQML: Automated Quantum Machine Learning for Wi-Fi Integrated Sensing and Communications

Toshiaki Koike-Akino, Pu Wang, Ye Wang

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.

Submitted: May 17, 2022