Learning from Radio using Variational Quantum RF Sensing

2026-03-10

AI Summary

This work addresses the challenge that conventional channel measurements struggle to support environmental sensing and localization under weak or obstructed signal conditions. To overcome this limitation, the authors propose a novel approach based on variational quantum sensing. By employing parameterized quantum circuits as quantum RF probes, the method interacts with electromagnetic fields and learns environmental features directly from ray-tracing data, enabling highly robust localization without requiring channel measurements during deployment. This study represents the first application of variational quantum sensing to RF-based environmental awareness, achieving high sensitivity to weak or occluded signals while significantly reducing the amount of required information. The approach demonstrates superior performance in realistic simulations, highlighting the feasibility and potential of quantum sensors for intelligent environmental perception.

Technology Category

• Machine Learning: Quantum Machine Learning
• Intelligent Robots: Localization, Mapping, and Navigation
• Search and Optimization: Learning to Search

Application Category

• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

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.

Problem

• quantum sensing
• radio-frequency signals
• environmental perception
• wireless networks
• intelligent systems

Innovation

• quantum sensing
• variational quantum circuit
• RF signal learning
• channel-free localization
• radio environment perception