Learning from Radio using Variational Quantum RF Sensing

TL;DR

It is shown 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.

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 (5)

• 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).
• Figure 4: Accrued training loss (left), testing loss (center), and prediction accuracy (right) over training epochs. We consider the target location shown in Fig. \ref{['fig:scen']} (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.
• Figure 5: Accrued training loss (left), testing loss (center), and prediction accuracy (right) over training epochs. We consider the target location shown in Fig. \ref{['fig:scen']} (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.