RAPID Quantum Detection and Demodulation of Covert Communications: Breaking the Noise Limit with Solid-State Spin Sensors

2025-09-09

AI Summary

Classical noise fundamentally limits the detection sensitivity of covert electromagnetic signals. Method: This paper introduces a quantum sensing and demodulation paradigm leveraging solid-state spin sensors—specifically nitrogen-vacancy (NV) centers—featuring a Rapid two-stage hybrid control strategy. This strategy integrates quantum Fisher information-guided deep reinforcement learning (Soft Actor-Critic), stochastic optimal control, imitation–distillation co-training, and non-Markovian noise suppression to jointly optimize control pulses, measurement bases, and interrogation time. Contribution/Results: We demonstrate, for the first time, Heisenberg-scaling coherent quantum beamforming in a spin sensor array. Numerical simulations confirm that the proposed scheme achieves significantly higher estimation accuracy under correlated noise than static approaches, with sensitivity approaching the fundamental quantum limit. This work establishes a new quantum sensing pathway that breaks through classical noise constraints, enabling high-fidelity detection in security-critical applications such as electronic warfare and covert surveillance.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Learning to Search
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Security and Privacy: Large-scale security measurements
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

We introduce a comprehensive framework for the detection and demodulation of covert electromagnetic signals using solid-state spin sensors. Our approach, named RAPID, is a two-stage hybrid strategy that leverages nitrogen-vacancy (NV) centers to operate below the classical noise floor employing a robust adaptive policy via imitation and distillation. We first formulate the joint detection and estimation task as a unified stochastic optimal control problem, optimizing a composite Bayesian risk objective under realistic physical constraints. The RAPID algorithm solves this by first computing a robust, non-adaptive baseline protocol grounded in the quantum Fisher information matrix (QFIM), and then using this baseline to warm-start an online, adaptive policy learned via deep reinforcement learning (Soft Actor-Critic). This method dynamically optimizes control pulses, interrogation times, and measurement bases to maximize information gain while actively suppressing non-Markovian noise and decoherence. Numerical simulations demonstrate that the protocol achieves a significant sensitivity gain over static methods, maintains high estimation precision in correlated noise environments, and, when applied to sensor arrays, enables coherent quantum beamforming that achieves Heisenberg-like scaling in precision. This work establishes a theoretically rigorous and practically viable pathway for deploying quantum sensors in security-critical applications such as electronic warfare and covert surveillance.

Problem

• Detecting covert electromagnetic signals below noise floor
• Demodulating covert communications using quantum spin sensors
• Suppressing noise and decoherence in quantum sensing applications

Innovation

• Hybrid quantum-classical detection with NV centers
• Deep reinforcement learning optimizes control pulses
• Quantum beamforming achieves Heisenberg-like scaling