Kolmogorov-Arnold networks trained on meteorological data from diverse sites predict the degradation of quantum advantage in turbulent quantum illumination channels.
Quantum illumination for enhanced detection of Rayleigh-fading targets
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abstract
Quantum illumination (QI) is an entanglement-enhanced sensing system whose performance advantage over a comparable classical system survives its usage in an entanglement-breaking scenario plagued by loss and noise. In particular, QI's error-probability exponent for discriminating between equally-likely hypotheses of target absence or presence is 6 dB higher than that of the optimum classical system using the same transmitted power. This performance advantage, however, presumes that the target return, when present, has known amplitude and phase, a situation that seldom occurs in lidar applications. At lidar wavelengths, most target surfaces are sufficiently rough that their returns are speckled, i.e., they have Rayleigh-distributed amplitudes and uniformly-distributed phases. QI's optical parametric amplifier receiver -- which affords a 3 dB better-than-classical error-probability exponent for a return with known amplitude and phase -- fails to offer any performance gain for Rayleigh-fading targets. We show that the sum-frequency generation receiver [Phys. Rev. Lett. 118, 040801 (2017)] -- whose error-probability exponent for a nonfading target achieves QI's full 6 dB advantage over optimum classical operation -- outperforms the classical system for Rayleigh-fading targets. In this case, QI's advantage is subexponential: its error probability is lower than the classical system's by a factor of $1/\ln(M\bar{\kappa}N_S/N_B)$, when $M\bar{\kappa}N_S/N_B \gg 1$, with $M\gg 1$ being the QI transmitter's time-bandwidth product, $N_S \ll 1$ its brightness, $\bar{\kappa}$ the target's average reflectivity, and $N_B$ the background light's brightness.
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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.