Optimum mixed-state discrimination for noisy entanglement-enhanced sensing

TL;DR

This paper introduces a sum-frequency generation (SFG) based receiver that achieves optimal discrimination of noisy mixed quantum states in quantum illumination, significantly improving quantum sensing performance under realistic conditions.

Contribution

It develops an SFG-based receiver for optimal multi-mode Gaussian-mixed-state discrimination, approaching the Helstrom bound in noisy quantum illumination scenarios.

Findings

SFG receiver saturates quantum Chernoff bound for QI.

Feed-forward augmentation pushes performance to Helstrom bound.

Enables practical quantum-enhanced imaging and detection.

Abstract

Quantum metrology utilizes nonclassical resources, such as entanglement or squeezed light, to realize sensors whose performance exceeds that afforded by classical-state systems. Environmental loss and noise, however, easily destroy nonclassical resources, and thus nullify the performance advantages of most quantum-enhanced sensors. Quantum illumination (QI) is different. It is a robust entanglement-enhanced sensing scheme whose 6 dB performance advantage over a coherent-state sensor of the same average transmitted photon number survives the initial entanglement's eradication by loss and noise. Unfortunately, an implementation of the optimum quantum receiver that would reap QI's full performance advantage has remained elusive, owing to its having to deal with a huge number of very noisy optical modes. We show how sum-frequency generation (SFG) can be fruitfully applied to optimum multi-mode Gaussian-mixed-state discrimination. Applied to QI, our analysis and numerical evaluations demonstrate that our SFG receiver saturates QI's quantum Chernoff bound. Moreover, augmenting our SFG receiver with a feed-forward (FF) mechanism pushes its performance to the Helstrom bound in the limit of low signal brightness. The FF-SFG receiver thus opens the door to optimum quantum-enhanced imaging, radar detection, state and channel tomography, and communication in practical Gaussian-state situations.