Thresholded Quantum LIDAR – Exploiting Photon-Number-Resolving Detection

Cohen, Lior; Matekole, Elisha S.; Sher, Yoni; Istrati, D.; Eisenberg, Henryk; Dowling, Jonathan P.

doi:10.1364/cqo.2019.m5a.14

Cited by 5 publications

(7 citation statements)

References 7 publications

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“…Thus, no target is presumably detected if a photon number less than the threshold is measured, and the target is presumably detected if a larger photon number is measured. Thus, we have found that the near-optimal detection is photon-number thresholding [25] after displacement. For the case of unstable phase, photon num- ber thresholding is optimal.…”

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confidence: 77%

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Towards Optimal Quantum Ranging -- Hypothesis Testing for an Unknown Return Signal

Cohen,

Wilde

2021

Preprint

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Quantum information theory sets the ultimate limits for any information-processing task. In rangefinding and LIDAR, the presence or absence of a target can be tested by detecting different states at the receiver. In this Letter, we use quantum hypothesis testing for an unknown coherentstate return signal in order to derive the limits of symmetric and asymmetric error probabilities of single-shot ranging experiments. We engineer a single measurement independent of the range, which in some cases saturates the quantum bound and for others is presumably the best measurement to approach it. This work bridges the gap between quantum information and quantum sensing and engineering and will contribute to devising better ranging sensors, as well as setting the path for finding practical limits for other quantum tasks.

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confidence: 77%

“…Here, unlike in Ref. [25], we show how to set the optimal threshold. For the setting with asymmetric error probability, the stochastic decision rule results in a non-deterministic detection strategy, announcing a target with probability Ω(n), upon measuring n photons.…”

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confidence: 98%

Towards Optimal Quantum Ranging -- Hypothesis Testing for an Unknown Return Signal

Cohen,

Wilde

2021

Preprint

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“…Indeed, this limitation has made unfeasible the realistic implementation of this family of technologies [6,10,12]. So far, this vulnerability has been tackled through conventional approaches that rely on the measurement of coherence functions, the implementation of thresholding and quantum state tomography [6,10,12,33]. Unfortunately, these approaches to characterize photon-fluctuations rely on the acquisition of large number of measurements that impose constraints on the identification of light sources.…”

Section: Discussionmentioning

confidence: 99%

Identification of Light Sources using Machine Learning

You¹,

Quiroz-Juárez²,

Lambert³

et al. 2019

Preprint

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The identification of light sources represents a task of utmost importance for the development of multiple photonic technologies. Over the last decades, the identification of light sources as diverse as sunlight, laser radiation and molecule fluorescence has relied on the collection of photon statistics or the implementation of quantum state tomography. In general, this task requires an extensive number of measurements to unveil the characteristic statistical fluctuations and correlation properties of light, particularly in the low-photon flux regime. In this article, we exploit the self-learning features of artificial neural networks and naive Bayes classifier to dramatically reduce the number of measurements required to discriminate thermal light from coherent light at the single-photon level. We demonstrate robust light identification with tens of measurements at mean photon numbers below one. Our work demonstrates an improvement in terms of the number of measurements of several orders of magnitude with respect to conventional schemes for characterization of light sources. Our work has important implications for multiple photonic technologies such as LIDAR and microscopy.

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“…More recently, investigations have examined discrimination between a broader class of quantum states to include mixed states [13][14][15][16][17]. Experimental work has demonstrated techniques for improved discrimination be-tween single-copy pure and mixed quantum states [18] including a demonstration that approached the Helstrom bound [19].…”

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confidence: 99%

Demonstration of quantum-limited discrimination of multi-copy pure versus mixed states

Jagannathan,

Brasher,

Grace

et al. 2021

Preprint

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We provide a demonstration of an optical receiver that achieves the quantum Chernoff bound for discriminating pure coherent laser states from thermal states. We find that the receiver approaching the Helstrom bound, for single-copy measurement for this discrimination task, is sub-optimal in the multi-copy case. We also provide a theoretical framework proving that, for a large class of discrimination tasks between a pure and a mixed state, any Helstrom-achieving receiver is suboptimal by a factor of at least two in error exponent compared to a receiver that achieves the quantum Chernoff bound.

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Thresholded Quantum LIDAR – Exploiting Photon-Number-Resolving Detection

Cited by 5 publications

References 7 publications

Towards Optimal Quantum Ranging -- Hypothesis Testing for an Unknown Return Signal

Towards Optimal Quantum Ranging -- Hypothesis Testing for an Unknown Return Signal

Identification of Light Sources using Machine Learning

Demonstration of quantum-limited discrimination of multi-copy pure versus mixed states

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