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

TL;DR

This paper introduces AutoQML, a framework that automates the design of quantum neural networks for Wi-Fi-based human gesture recognition, achieving high accuracy with limited data and few parameters.

Contribution

It presents a novel automated quantum machine learning approach, AutoAnsatz, for designing quantum circuits tailored to human pose recognition in Wi-Fi sensing.

Findings

Achieved over 80% accuracy in human pose recognition.

Demonstrated effectiveness with limited data and small model size.

Validated approach through in-house experiments.

Abstract

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.