AutoQML: Automated Quantum Machine Learning for Wi-Fi Integrated Sensing and Communications
This work addresses gesture recognition for indoor monitoring using commercial Wi-Fi devices, but it appears incremental as it builds on automated quantum machine learning frameworks.
The paper tackled the problem of designing quantum circuits for quantum neural networks to recognize human gestures using Wi-Fi signals for integrated sensing and communications, achieving over 80% accuracy with a small number of trainable parameters in an in-house experiment.
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.