Quantum Cognitive Radar: Adaptive Detection With Entanglement Under Thermal-Loss Channels

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$P_{D}$at a fixed false-alarm probability$P_{\text{FA}}$(i.e., the probability of declaring a target when it is absent) than both coherent-state radar and nonadaptive quantum baselines. At$P_{\text{FA}}=0.05$, 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$P_{\text{FA}}=10^{-3}$, QCR achieves$P_{D}=0.47$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$(M,N_{S},B,T_{\text{int}},G)$under fixed energy constraints, demonstrating that online adaptation preserves and extends quantum-illumination advantages in nonstationary sensing environments.