REVIEW 1 cited by
AutoQML: Automated Quantum Machine Learning for Wi-Fi Integrated Sensing and Communications
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
AutoQML: Automated Quantum Machine Learning for Wi-Fi Integrated Sensing and Communications
read the original 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.
Forward citations
Cited by 1 Pith paper
-
Auto Quantum Machine Learning for Multisource Classification
AQML-found quantum circuits match classical MLPs on multisource classification and improve on a previous QML change-detection result (0.743 vs 0.720).
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.