paper

Realizing Quantum Wireless Sensing Without Extra Reference Sources: Architecture, Algorithm, and Sensitivity Maximization

arXiv:2504.21234

Abstract

Rydberg Atomic REceivers (RAREs) have demonstrated remarkable capabilities for radio-frequency signal measurement, enabling advanced quantum wireless sensing. Existing RARE-based sensing systems popularly adopt the heterodyne detection methodology, which requires an additional reference source to serve as an atomic mixer. However, this approach entails a bulky transceiver architecture and is limited in the supportable sensing bandwidth. To address these limitations, we propose a self-heterodyne sensing paradigm where the transmitter's self-interference naturally provides the reference signal. We demonstrate that a self-heterodyne RARE functions as an atomic autocorrelator, eliminating the need for external reference sources while supporting substantially wider bandwidth than conventional heterodyne methods. Next, a two-stage algorithm is devised to perform target ranging in self-heterodyne RARE systems. This algorithm is shown to closely approach the Cramer-Rao lower bound. Furthermore, we introduce the power-trajectory (-trajectory) design for RAREs, which maximizes the sensing sensitivity through time-varying transmission power control. An internal noise (ITN)-limited -trajectory is developed to capture the profile of the asymptotically optimal time-varying power in the presence of ITN only. This design is then extended to the practical -trajectory by incorporating both the ITN and external noise. Numerical results validate that the proposed self-heterodyne sensing can achieve a 100 MHz-level bandwidth with high sensitivity, substantially surpassing existing heterodyne counterparts and paving the way for high-resolution quantum wireless sensing.

A self-heterodyne sensing approach, which equivalents to an atomic autocorrelator, is proposed to enable high-resolution quantum wireless sensing

Realizing Quantum Wireless Sensing Without Extra Reference Sources: Architecture, Algorithm, and Sensitivity Maximization · wovepaper