Quantum-Cognitive Radar: Adaptive Detection with Entanglement under Thermal-Loss Channels

Abstract

An adaptive Quantum-Cognitive Radar (QCR), which incorporates a two-mode squeezed-vacuum (TMSV) transmitter, a joint idler-signal receiver, and a Quantum Neural Network (QNN) controller to optimize parameters in real time, is introduced through this exchange of correspondence. An expression for a Gaussian correlation detector has been found for thermal-loss channels and compared with the quantum Chernoff bound (QCB). Hardware-aware simulations show that QCR achieves higher detection probability P<inf>D</inf> at a fixed false-alarm probability PFA (i.e., the probability of declaring a target when it is absent) than both coherent-state radar and nonadaptive quantum baselines. At P<inf>FA</inf> = 0.05, QCR provides an approximately 3 dB advantage with up to 40% reduction in integration time while maintaining robustness as background noise increases. At the operationally stringent P<inf>FA</inf> = 10^{−3}, QCR achieves P<inf>D</inf> = 0.47 versus 0.20 for classical radar, corresponding to a 135% relative improvement. The receiver requires only homodyne/heterodyne sampling and digital correlation, making it compatible with noisy intermediate-scale quantum (NISQ) hardware. The adaptive policy optimizes the parameter vector (M, N<inf>S</inf> , B, T<inf>int</inf>, G) under fixed energy constraints, demonstrating that online adaptation preserves and ex-tends quantum-illumination advantages in nonstationary sensing environments.