  Beyond data communications, commercial-off-the-shelf Wi-Fi devices can be
used to monitor human activities, track device locomotion, and sense the
ambient environment. In particular, spatial beam attributes that are inherently
available in the 60-GHz IEEE 802.11ad/ay standards have shown to be effective
in terms of overhead and channel measurement granularity for these indoor
sensing tasks. In this paper, we investigate transfer learning to mitigate
domain shift in human monitoring tasks when Wi-Fi settings and environments
change over time. As a proof-of-concept study, we consider quantum neural
networks (QNN) as well as classical deep neural networks (DNN) for the future
quantum-ready society. The effectiveness of both DNN and QNN is validated by an
in-house experiment for human pose recognition, achieving greater than 90%
accuracy with a limited data size.
