Wireless Sensing in Artificial Intelligence of Things: A General Quantum Machine Learning Framework

With the emergence of the 5G and beyond, wireless networks have transformed from a simple communication medium to ubiquitous versatile platforms. This trend has enabled numerous device-free and non-contact applications. As computing power and machine learning algorithms continue to improve, deep learning techniques are increasingly used in wireless sensing applications. However, the limits of deep learning-centered wireless sensing approaches are still being explored. Concurrently, research in quantum computing is advancing rapidly, prompting researchers to explore the burgeoning field of quantum machine learning, which combines quantum computing and machine learning, for its boundless potential. In this article, we propose a general quantum machine learning framework for wireless sensing applications in the Artificial Internet of Things (AIoT). The proposed framework provides a systematic approach for designing deeply interpreted wireless sensing models based on quantum machine learning. We then present several representative applications and case studies, and conclude this article with a discussion of the challenges and future research directions in this exciting area.