Kolmogorov-Arnold networks trained on meteorological data from diverse sites predict the degradation of quantum advantage in turbulent quantum illumination channels.
Optimum mixed-state discrimination for noisy entanglement-enhanced sensing
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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.
fields
quant-ph 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
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Quantum advantage prediction in turbulent free-space quantum illumination
Kolmogorov-Arnold networks trained on meteorological data from diverse sites predict the degradation of quantum advantage in turbulent quantum illumination channels.