Learning from Radio using Variational Quantum RF Sensing

Ivana Nikoloska

quant-phMar 10, 2026

Learning from Radio using Variational Quantum RF Sensing

Authors: Ivana Nikoloska

Organizations: Signal Processing Systems Group, Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, 5612 AP, The Netherlands

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.

Figures & tables

Figure 1: Learning from radio waves via quantum sensing: An agent uses a quantum sensing probe $\ket{\psi_{\lambda}}$ that interacts with the incident RF electromagnetic field $\xi$ , modeled as a unitary transformation $U_{\text{int}}$ derived from the rotating wave approximation of the physical interaction. The agent then measures the perturbed state $\ket{\psi_{\lambda}(\xi)}$ , producing measurements $\tilde{z}$ , and learns to make predictions $\tilde{r}$ using a machine learning model $f_{\gamma}(\tilde{z})$ . Both the quantum circuit parameters $\lambda$ and the neural network parameters $\gamma$ are jointly optimised during training.

Figure 2: In a realistic environment, many physical components affect the propagation of radio waves. Structures such as buildings, vehicles, and terrain features cause the transmitted signal to undergo reflection, diffraction, and scattering. These result in multiple copies of the original signal traveling along different paths — a phenomenon known as multipath propagation.

Figure 3: We consider an urban scenario with two transmitters (blue circles) communicating in an urban environment with multiple buildings (grey rectangles). We assume that communication occurs at a frequency $f_{c}=2.14$ GHz. The transmitter has single-antenna equipment and the agent’s location $[\rm{x},\rm{y}]$ is generated uniformly at random within the deployment area. We consider two target locations: one which has a transmitter nearby and, as a result, strong line-of-sight path (orange rectangle), and the second which is hidden behind an obstacle (red rectangle).

Panel of Figure 4 — Figure 4: Accrued training loss (left), testing loss (center), and prediction accuracy (right) over training epochs. We consider the target location shown in Fig. 3 (orange rectangle) which has a transmitter nearby and strong line-of-sight path. All results are averaged over three independent trials. Shaded regions represent the variance across trials, which reflects random initialisation and dropout during training.

Panel of Figure 5 — Figure 5: Accrued training loss (left), testing loss (center), and prediction accuracy (right) over training epochs. We consider the target location shown in Fig. 3 (red rectangle) which is hidden behind obstacles. All results are averaged over three independent trials. Shaded regions represent the variance across trials, which reflects random initialisation and dropout during training.

Wireless Localization

Wireless localization uses radio signals and sensor fusion to estimate the positions and trajectories of people, devices, and transmitters.

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Quantum technology has the potential to transform scientific discovery, but quantum advantages often require processing capabilities well beyond the reach of experimental platforms. We show that coupling a single controllable qubit to an otherwise conventional sensor can exponentially reduce the number of measurements required to learn classical signals. These rigorous quantum advantages apply to fundamental sensing tasks, including learning Fourier coefficients, extracting temporal correlations from time-varying signals, and estimating transformations of physical observables. Using a superconducting cavity--qubit architecture, we experimentally demonstrate

10^7

-fold reductions in the number of measurements required for Fourier-amplitude and time-varying signal learning. Our

\textit{quantum feature sensing}

algorithms further enable orders-of-magnitude improvements in simulations of weak-signal dark matter detection and wireless communication applications. These quantum advantages are derived from Quantum Phase-Space Inference (Q

Ψ

), a unifying theory of quantum-enhanced experiments that simultaneously converts a set of experimental objectives and constraints into tight lower bounds and optimal quantum-enhanced learning algorithms while producing a certificate of quantum advantage. Q

Ψ

extends beyond the regimes captured by quantum Fisher information and provides a framework for systematically identifying rigorous quantum advantages in practical experimental tasks. Together, our results establish that near-term quantum technology can exponentially enhance our ability to learn from classical signals.

Abstract

Wireless Localization

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