Quantum lidar scenario with simple detection

Abstract

Quantum Lidar is a future technology that can mean different things to different people. To some it means detection and identification of classical-type lidar signals reflected from an illuminated target at or near the single photon level. This is facilitated by advances in semiconductor detector technology that allow highly efficient photocounting. To others it is more stringent, requiring exploitation of covertness associated with being essentially undetectable by the target in a thermal background, whilst allowing the lidar operator to filter her own signal from this noise. Again there is nothing specifically quantum about this. Any source that mimics of the energy rate per bandwidth seen by the illuminated object in the direction of the source can appear covert. In order to exploit quantum optics one can use inherent correlations, whether entangled or otherwise between light beams. Simple correlated optical beams can do this in two ways, perhaps by storing one beam as a reference and later performing correlated detection with the return signal in a phase sensitive manner. This is the “gold standard” for quantum lidar, but will be difficult outside of laboratory situations. In microscopy such phase control might be possible, or perhaps in the radio or microwave region for a quantum radar. Less challenging is a scenario in which an immediate local measurement of one beam conditions the beam sent to the target, allowing the return signal to be sifted from noise more easily. Here we will consider such a simple system, providing a range equation for a basic quantum lidar if it is to be operated covertly or openly. Open operation allows increased quantum signal brightness but also provides a quantum advantage, unspoofability – the lidar operator can in principle recognise their own return signal. We will also provide an analysis of quantum lidar detection based on quantum hypothesis testing and use this to perform Monte Carlo simulation of both target detections and false positive avoidance in a noisy background. The theory will be applied to experimental data.