TR2022-068

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


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-theart performance greater than 80% accuracy for a limited data size with a significantly small number of trainable parameters. Index Terms—Integrated sensing and communication (ISAC), Wi-Fi sensing, human monitoring, quantum machine learning.

 

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  •  Koike-Akino, T., Wang, P., Wang, Y., "AutoQML: Automated Quantum Machine Learning for Wi-Fi Integrated Sensing and Communications", arXiv, May 2022.
    BibTeX arXiv
    • @article{Koike-Akino2022may4,
    • author = {Koike-Akino, Toshiaki and Wang, Pu and Wang, Ye},
    • title = {AutoQML: Automated Quantum Machine Learning for Wi-Fi Integrated Sensing and Communications},
    • journal = {arXiv},
    • year = 2022,
    • month = may,
    • url = {https://arxiv.org/abs/2205.09115}
    • }