Physics-Aware Power Optimization for Rydberg Quantum Arrays via Barankin Bound Minimization

2026-10-07

AI Summary

This study addresses the direction-finding performance collapse caused by spatial ambiguity and the power allocation challenge in Rydberg atom arrays under low signal-to-noise ratio conditions. To overcome these issues, a physics-aware optimization framework based on random Barankin bound minimization is proposed. Methodologically, an arcsine prior calibration and a Lindblad-guided electromagnetically induced transparency readout model are introduced, coupling photon noise with laser parameters to accurately capture threshold effects. A closed-form stochastic multi-point Barankin bound matrix is derived, and a backtracking majorization-minimization algorithm is employed to solve the resulting non-convex optimization problem. Compared with the conventional Cramér-Rao bound, this framework more accurately predicts threshold breakdown and significantly extends the reliable operating region under severe terahertz attenuation.

Abstract

Rydberg-atom quantum uniform linear arrays (RAQ-ULAs) offer a promising sensing architecture for direction-of-arrival (DOA) estimation in terahertz (THz) beam alignment. Existing studies often model the quantum receiver as a macroscopic linear block and rely on the Cramer-Rao bound (CRB) for array evaluation or power allocation. However, as a local variance bound, the CRB cannot capture threshold breakdown caused by spatial ambiguities in low-signal-to-noise-ratio (SNR) regimes. This paper proposes a physics-aware power optimization framework for RAQ-ULAs based on stochastic Barankin bound minimization. We derive a closed-form stochastic multipoint Barankin bound (BRB) matrix under a low-SNR integrability condition and introduce an arcsine-prior calibration to characterize bounded-domain error saturation. By incorporating a Lindblad-guided electromagnetically induced transparency (EIT) readout model, we couple the ambiguity-sensitive BRB with photon shot noise, power broadening, and laser Rabi frequencies. A CRB-optimized analytical baseline is further derived to expose the limitation of local-SNR-based allocation. To solve the resulting nonconvex problem, we develop a backtracking majorization-minimization (MM) algorithm with a Lipschitz-based quadratic surrogate. The algorithm ensures monotonic decrease and converges to a feasible stationary point. Simulations show that the proposed framework predicts threshold breakdown more accurately than the CRB and enlarges the reliable operating region under severe THz attenuation.

Problem

• Rydberg quantum arrays
• Direction-of-arrival estimation
• Power optimization
• Barankin bound
• Threshold breakdown

Innovation

• Barankin bound minimization
• Rydberg quantum arrays
• physics-aware power optimization
• majorization-minimization algorithm
• electromagnetically induced transparency