# Quantum Transfer Learning for Wi-Fi Sensing

arXiv: https://arxiv.org/abs/2205.08590 · HTML: https://mlsanity.com/paper/2205.08590/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 55 references that are in the map.
- First posted on arXiv 17 May 2022.

## Builds on (most cited first, 10 of 10)

- [Energy and Policy Considerations for Deep Learning in NLP](https://mlsanity.com/paper/1906.02243/) (2019)
- [Deep Visual Domain Adaptation: A Survey](https://mlsanity.com/paper/1802.03601/) (2018)
- [Barren plateaus in quantum neural network training landscapes](https://mlsanity.com/paper/1803.11173/) (2018)
- [Quantum generative adversarial networks](https://mlsanity.com/paper/1804.08641/) (2018)
- [Transfer learning in hybrid classical-quantum neural networks](https://mlsanity.com/paper/1912.08278/) (2019)
- [Noisy intermediate-scale quantum (NISQ) algorithms](https://mlsanity.com/paper/2101.08448/) (2021)
- [Quantum Graph Neural Networks](https://mlsanity.com/paper/1909.12264/) (2019)
- [Person-in-WiFi: Fine-grained Person Perception using WiFi](https://mlsanity.com/paper/1904.00276/) (2019)
- [Joint Activity Recognition and Indoor Localization with WiFi Fingerprints](https://mlsanity.com/paper/1904.04964/) (2019)
- [BeSense: Leveraging WiFi Channel Data and Computational Intelligence for Behavior Analysis](https://mlsanity.com/paper/1907.06005/) (2019)

## Built on by (most cited first, 4 of 4)

- [Gesture Recognition with mmWave Wi-Fi Access Points: Lessons Learned](https://mlsanity.com/paper/2306.17062/) (2023)
- [Hybrid Classical-Quantum Transfer Learning with Noisy Quantum Circuits](https://mlsanity.com/paper/2603.16973/) (2026)
- [quEEGNet: Quantum AI for Biosignal Processing](https://mlsanity.com/paper/2210.00864/) (2022)
- [Quantum Feature Extraction for THz Multi-Layer Imaging](https://mlsanity.com/paper/2207.09285/) (2022)

## Abstract (arXiv)

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.