Quantum light promises improved precision in optical remote sensing, but its practical advantage depends critically on whether nonclassical resources remain useful under realistic noise and experimentally accessible detection. This question becomes especially relevant for LiDAR systems, where a quantum advantage has been demonstrated for target detection and joint range-velocity estimation, but mostly under idealized conditions or simple noise models, such as optical loss and thermal background. A key open point is whether entanglement provides an operational advantage when the dominant disturbance is not independent noise, but structured interference across sensing modes. Here, we address this question by studying the joint estimation of target range and velocity with bright two-mode Gaussian probes and homodyne detection, comparing coherent, separable squeezed, and twin-beam states at a fixed resource budget. Our results reveal a hierarchy of quantum resources set by the noise structure: separable squeezing provides a robust advantage over coherent illumination under loss and thermal background, whereas twin-beam probes become superior under correlated jamming when the receiver is adaptively optimized. These results establish correlated noise as the operational regime in which entanglement provides a robustness advantage beyond local squeezing, opening a receiver-aware route to quantum-enhanced LiDAR in realistic and potentially adversarial environments.
Quantum light promises improved precision in optical remote sensing, but its practical advantage depends critically on whether nonclassical resources remain useful under realistic noise and experimentally accessible detection. This question becomes especially relevant for LiDAR systems, where a quantum advantage has been demonstrated for target detection and joint range-velocity estimation, but mostly under idealized conditions or simple noise models, such as optical loss and thermal background. A key open point is whether entanglement provides an operational advantage when the dominant disturbance is not independent noise, but structured interference across sensing modes. Here, we address this question by studying the joint estimation of target range and velocity with bright two-mode Gaussian probes and homodyne detection, comparing coherent, separable squeezed, and twin-beam states at a fixed resource budget. Our results reveal a hierarchy of quantum resources set by the noise structure: separable squeezing provides a robust advantage over coherent illumination under loss and thermal background, whereas twin-beam probes become superior under correlated jamming when the receiver is adaptively optimized. These results establish correlated noise as the operational regime in which entanglement provides a robustness advantage beyond local squeezing, opening a receiver-aware route to quantum-enhanced LiDAR in realistic and potentially adversarial environments.
The signal half of an entangled twin beam, generated using spontaneous parametric downconversion, interrogates a region of space that is suspected of containing a target and has high loss and high (entanglement-breaking) background noise. A joint measurement is performed on the returned light and the idler beam that was retained at the transmitter. An optimal quantum receiver, whose implementation is not yet known, was shown to achieve 6 dB gain in the error-probability exponent relative to that achieved with a single coherent-state (classical) laser transmitter and the optimum receiver. We present two structured optical receivers that achieve up to 3 dB gain in the error exponent over that attained with the classical sensor. These are designs of quantum-optical sensors for target detection, which can be readily implemented in a proof-of-concept experiment, that appreciably outperform the best classical sensor in the low-signal-brightness, high-loss, and high-noise operating regime.
Abstract We propose a quantum-enhanced lidar system to estimate a target’s radial velocity, which employs squeezed and frequency-entangled signal and idler beams. We compare its performance against a classical protocol using a coherent state with the same pulse duration and energy, showing that quantum resources provide a precision enhancement in the estimation of the velocity of the object. We identify three distinct parameter regimes characterized by the amount of squeezing and frequency entanglement. In two of them, a quantum advantage exceeding the standard quantum limit is achieved assuming no photon losses. Additionally, we show that an optimal measurement to attain these results in the lossless case is frequency-resolved photon counting. Finally, we consider the effect of photon losses for the high-squeezing regime, which leads to a constant factor quantum advantage higher than 3 dB in the variance of the estimator, given a roundtrip lidar-to-target-to-lidar transmissivity larger than 50%.
A recurring structural pattern across recent quantum optics and quantum information preprints is that **photon statistics and mode correlations function not merely as resources to be consumed, but as diagnostic channels that encode, transduce, and amplify physical information**. This paper synthesizes six findings from the corpus—spanning phase-estimation probe-state optimization, twin-beam LiDAR under correlated noise, coherence-to-entanglement transduction in high-dimensional systems, frequency upconversion via molecular cavity optomechanics with gain, quantum-optical RABBIT spectroscopy, and bath-memory-enhanced transport precision—into a candidate heuristic framework (explicitly not a unified derivation) in which the photon-statistical structure of a probe or environment determines the achievable sensing precision, the noise resilience, and the information-extraction pathway. Each source is drawn from the quant-ph and physics.optics primary categories of arXiv, selected over the past 30 days for direct relevance to quantum-optical sensing and transduction. The thesis is: **the hierarchy of quantum sensing advantages—from shot-noise to Heisenberg scaling, from local squeezing to entanglement advantage, from Gaussian to non-Gaussian regimes—is controlled by matching the photon-statistical structure of the probe to the correlation structure of the noise and the target signal**. This is a heuristic reading supported by mechanistic parallels across the cited works, not a single shared formalism. The central falsification path is: construct a sensing scenario in which the optimal probe state predicted by the photon-statistics-matching principle conflicts with the independently computed quantum Cramér-Rao bound; if such a conflict is found, the framework requires revision or abandonment. --- Authorship: Saluca Agentic AI Research Team (Saluca LLC). AI-drafted from arXiv preprint corpus on the date in the filename. Cited arXiv preprints: 2606.12383, 2606.13447, 2606.16548, 2606.16695, 2606.16712, 2606.16854, 2606.16916, 2606.17026, 2606.17877, 2606.17899, 2606.17907, 2606.17908, 2606.17956, 2606.18035, 2606.18133, 2606.18145, 2606.18167, 2606.18169, 2606.18240
We propose a quantum lidar protocol to jointly estimate the range and velocity of a target by illuminating it with a single beam of pulsed displaced squeezed light. In the lossless scenario, we show that the mean-squared errors of both range and velocity estimations are inversely proportional to the squared number of signal photons, simultaneously attaining the Heisenberg limit. This is achieved by engineering the multiphoton squeezed state of the temporal modes and adopting standard homodyne detection. To assess the robustness of the quantum protocol, we incorporate photon losses and detuning of the homodyne receiver. Our findings reveal a quantum advantage over the best-known classical strategy across a wide range of round-trip transmissivities. Particularly, the quantum advantage is substantial for sufficiently small losses, even when compared to the optimal-potentially unattainable-classical performance limit. The quantum advantage also extends to the practical case where quantum engineering is done on top of a strong classical coherent state with watts of power. This, together with the robustness against losses and the feasibility of the measurement with state-of-the-art technology, make the protocol highly promising for near-term implementation.
Modern optical sensing systems, particularly LiDAR, play a central role in remote sensing and precision measurement. However, they face fundamental limits when operating in noisy and highly lossy environments. Quantum illumination has emerged as a promising approach to overcome these challenges by exploiting quantum correlations. This work presents the system design and performance analysis of a quantum illumination based LiDAR architecture operating at optical frequencies. The proposed system exploits entangled photon pairs generated through spontaneous parametric down conversion to enhance detection sensitivity and robustness under weak return conditions. A complete hardware oriented framework is developed, including the entanglement source, nonlinear crystal configuration, pump laser requirements, optical propagation path, quantum memory implementation, and a detection architecture based on quantum erasure Hong-Ou-Mandel interference. Practical realization using superconducting nanowire single photon detectors is discussed to enable high efficiency and low noise photon measurement. In addition, a system level link budget and noise analysis evaluates the impact of optical losses, atmospheric attenuation, turbulence, and detector imperfections on overall performance. The study highlights how quantum correlations can improve signal to noise performance compared with classical illumination strategies, particularly in low reflectivity and high background scenarios. The presented architecture provides a realistic framework for implementing quantum LiDAR systems and supports the development of next generation long range sensing technologies.
We demonstrate a quantum-enhanced lidar capable of performing confident target detection and rangefinding in the presence of strong, time-varying classical noise whilst operating with over five orders of magnitude separation between signal and background levels and target reflectivities down to -52 dB. We use a log-likelihood-based framework to introduce a new protocol for dynamic background tracking, verifying resilience of our system to both fast- and slow-modulation jamming in regimes where a classical illumination-based system fails to find a target. These results demonstrate the advantage of exploiting quantum correlations for lidar applications, providing a clear route to implementation in real-world scenarios.