Learning from Radio using Variational Quantum RF Sensing

TL;DR

This paper explores how quantum sensors can learn environmental information from radio signals in wireless networks, enabling more sensitive and efficient localization without classical channel measurements.

Contribution

It introduces a quantum sensing approach optimized with quantum circuits for RF signal analysis, demonstrating advantages over classical methods in localization tasks.

Findings

Quantum sensors improve sensitivity to weak RF signals.

The approach enables environment learning without classical channel measurements.

Quantum learning remains effective with less information than classical baselines.

Abstract

In modern wireless networks, radio channels serve a dual role. Whilst their primary function is to carry bits of information from a transmitter to a receiver, the intrinsic sensitivity of transmitted signals to the physical structure of the environment makes the channel a powerful source of knowledge about the world. In this paper, we consider an agent that learns about its environment using a quantum sensing probe, optimised using a quantum circuit, which interacts with the radio-frequency (RF) electromagnetic field. We use data obtained from a ray-tracer to train the quantum circuit and learning model and we provide extensive experiments under realistic conditions on a localisation task. We show that using quantum sensors to learn from radio signals can enable intelligent systems that require no channel measurements at deployment, remain sensitive to weak and obstructed RF signals, and can learn about the world despite operating with strictly less information than classical baselines.