Quantum Transfer Learning for Wi-Fi Sensing

TL;DR

This paper explores the use of quantum neural networks alongside classical deep neural networks to improve Wi-Fi-based human sensing, demonstrating high accuracy in pose recognition despite domain shifts.

Contribution

It introduces quantum transfer learning for Wi-Fi sensing, comparing QNN and DNN performance in human activity monitoring under changing environments.

Findings

Both QNN and DNN achieved over 90% accuracy.

Quantum transfer learning shows promise for adaptive Wi-Fi sensing.

Classical DNN performs comparably to QNN in experiments.

Abstract

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.