TR2022-068
AutoQML: Automated Quantum Machine Learning for Wi-Fi Integrated Sensing and Communications
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- , "AutoQML: Automated Quantum Machine Learning for Wi-Fi Integrated Sensing and Communications", IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM), DOI: 10.1109/SAM53842.2022.9827846, June 2022.BibTeX TR2022-068 PDF Video Presentation
- @inproceedings{Koike-Akino2022jun,
- author = {Koike-Akino, Toshiaki and Wang, Pu and Wang, Ye},
- title = {{AutoQML: Automated Quantum Machine Learning for Wi-Fi Integrated Sensing and Communications}},
- booktitle = {IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM)},
- year = 2022,
- month = jun,
- doi = {10.1109/SAM53842.2022.9827846},
- url = {https://www.merl.com/publications/TR2022-068}
- }
- , "AutoQML: Automated Quantum Machine Learning for Wi-Fi Integrated Sensing and Communications", IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM), DOI: 10.1109/SAM53842.2022.9827846, June 2022.
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MERL Contacts:
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Research Areas:
Artificial Intelligence, Communications, Computational Sensing, Machine Learning, Signal Processing
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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