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arXiv:1609.00684

Attaining the quantum limit of passive imaging

Hari Krovi, Saikat Guha, Jeffrey H. Shapiro

Published Sep 2, 2016• 5 citations
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Abstract

We consider the problem, where a camera is tasked with determining one of two hypotheses: first with an incoherently-radiating quasi-monochromatic point source and the second with two identical closely spaced point sources. We are given that the total number of photons collected over an integration time is assumed to be the same under either hypothesis. For the one-source hypothesis, the source is taken to be on-axis along the line of sight and for the two-source hypothesis, we give ourselves the prior knowledge of the angular separation of the sources, and they are assumed to be identical and located symmetrically off-axis. This problem was studied by Helstrom in 1973, who evaluated the probability of error achievable using a sub-optimal optical measurement, with an unspecified structured realization. In this paper, we evaluate the quantum Chernoff bound, a lower bound on the minimum probability of error achievable by any physically-realizable receiver, which is exponentially tight in the regime that the integration time is high. We give an explicit structured receiver that separates three orthogonal spatial modes of the aperture field followed by quantum-noise-limited time-resolved photon measurement and show that this achieves the quantum Chernoff bound. In other words, the classical Chernoff bound of our mode-resolved detector exactly matches the quantum Chernoff bound for this problem. Finally, we evaluate the classical Chernoff bound on the error probability achievable using an ideal focal plane array---a signal shot-noise limited continuum photon-detection receiver with infinitely many infinitesimally-tiny pixels---and quantify its performance gap with the quantum limit.

DOI
10.48550/arxiv.1609.00684

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We consider the problem, where a camera is tasked with determining one of two hypotheses: first with an incoherently-radiating quasi-monochromatic point source and the second with two identical closely spaced point sources. We are given that the total number of photons collected over an integration time is assumed to be the same under either hypothesis. For the one-source hypothesis, the source is taken to be on-axis along the line of sight and for the two-source hypothesis, we give ourselves the prior knowledge of the angular separation of the sources, and they are assumed to be identical and located symmetrically off-axis. This problem was studied by Helstrom in 1973, who evaluated the probability of error achievable using a sub-optimal optical measurement, with an unspecified structured realization. In this paper, we evaluate the quantum Chernoff bound, a lower bound on the minimum probability of error achievable by any physically-realizable receiver, which is exponentially tight in the regime that the integration time is high. We give an explicit structured receiver that separates three orthogonal spatial modes of the aperture field followed by quantum-noise-limited time-resolved photon measurement and show that this achieves the quantum Chernoff bound. In other words, the classical Chernoff bound of our mode-resolved detector exactly matches the quantum Chernoff bound for this problem. Finally, we evaluate the classical Chernoff bound on the error probability achievable using an ideal focal plane array---a signal shot-noise limited continuum photon-detection receiver with infinitely many infinitesimally-tiny pixels---and quantify its performance gap with the quantum limit.
Abstract We analyze the fundamental quantum limit of the resolution of an optical imaging system from the perspective of the detection problem of deciding whether the optical field in the image plane is generated by one incoherent on-axis source with brightness ϵ\epsilonϵ ϵ or by two ϵ/2\epsilon {\mathrm{/}}2ϵ/2 ϵ ∕ 2 -brightness incoherent sources that are symmetrically disposed about the optical axis. Using the exact thermal-state model of the field, we derive the quantum Chernoff bound for the detection problem, which specifies the optimum rate of decay of the error probability with increasing number of collected photons that is allowed by quantum mechanics. We then show that recently proposed linear-optic schemes approach the quantum Chernoff bound—the method of binary spatial-mode demultiplexing (B-SPADE) is quantum-optimal for all values of separation, while a method using image inversion interferometry (SLIVER) is near-optimal for sub-Rayleigh separations. We then simplify our model using a low-brightness approximation that is very accurate for optical microscopy and astronomy, derive quantum Chernoff bounds conditional on the number of photons detected, and show the optimality of our schemes in this conditional detection paradigm. For comparison, we analytically demonstrate the superior scaling of the Chernoff bound for our schemes with source separation relative to that of spatially resolved direct imaging. Our schemes have the advantages over the quantum-optimal (Helstrom) measurement in that they do not involve joint measurements over multiple modes, and that they do not require the angular separation for the two-source hypothesis to be given a priori and can offer that information as a bonus in the event of a successful detection.
Determining the presence of a potential optical source in the interest region is important for an imaging system and can be achieved by using hypothesis testing. The previous studies assume that the potential source is completely incoherent. In this paper, this problem is generalized to the scenario with partially coherent sources and any prior probabilities. We consider quantum-optimal error probability and a classical benchmark of two scenarios depending on the number of available temporal modes M . For M =1, we calculate the quantum Helstrom bound and the error probability of prior-based direct decision. For M ≫1, we analyze the quantum Chernoff bound as well as the Chernoff bound of intensity-based direct imaging. In addition, we propose binary spatial-mode demultiplexing and the result demonstrates asymptotically optimal detection performance. Our findings may shed new light on super-resolution imaging in partially coherent scenarios.
For more than a century, the diffraction limit has defined the resolution achievable by passive optical imaging systems. Although some resolution improvement can be gained through classical data processing of the image, it is limited by the noise arising from quantum nature of light. Minimizing the effect of this noise requires quantum treatment of optical imaging. By reformulating imaging as a problem of quantum measurement and estimation, it becomes possible to identify optimal detection strategies that recover spatial information previously thought inaccessible. This review summarizes the theoretical framework that underpins this development, from the formulation of quantum Cramer-Rao bounds and Chernoff bounds to the construction of receivers that attain them, such as those based on spatial-mode demultiplexing. We show how these methods can beat conventional imaging in the classification, localization, and imaging of sub-Rayleigh incoherent sources. We then discuss extensions to multiparameter and partially coherent scenarios, and highlight the unifying connections between estimation and discrimination tasks. Finally, we survey recent experimental demonstrations that approach quantum-limited resolution and outline emerging applications in microscopy, astronomy, and optical sensing.
We consider imaging tasks involving discrimination between known objects and investigate the best possible accuracy with which the correct object can be identified. Using the quantum Chernoff bound, we analytically find the ultimate achievable asymptotic error rate for symmetric hypothesis tests between \emph{any} two incoherent 2D objects when the imaging system is dominated by optical diffraction. Furthermore, we show that linear-optical demultiplexing of the spatial modes of the collected light exactly saturates this ultimate performance limit, enabling a quadratic improvement over the asymptotic error rate achieved by direct imaging as the objects become more severely diffraction-limited. We extend our results to identify the quantum limit and optimal measurement for discrimination between an arbitrary number of candidate objects. Our work constitutes a complete theoretical treatment of the ultimate quantitative limits on passive, sub-diffraction, incoherent object discrimination and is readily applicable to a multitude of real-world applications.
Resolving extended incoherent objects below the diffraction limit poses an application-rich imaging challenge whose solution may enable a new generation of observational instruments and capabilities. In this work, we invoke a practical model for general imaging by approximating an arbitrary extended incoherent object as a finite grid of thermal point emitters parameterized by their brightnesses. We derive the quantum Fisher information matrix (QFIM) for simultaneous brightness estimation and show that the symmetric logarithmic derivatives weakly commute, indicating that the Helstrom bound furnishes the ultimate quantum limit on the estimation error for incoherent imaging. Furthermore, for deeply sub-diffraction scenes, we find numerical evidence of a gap between the Nagaoka-Hayashi (NH) bound and the Helstrom bound. This gap reveals that separable measurements, though more experimentally accessible, are insufficient to reach the quantum limit, and points to the prospective advantage of joint measurements acting on multiple state copies. Additionally, we show that spatial mode-demultiplexing (SPADE) often saturates the NH bound solidifying its status as a near-optimal separable measurement strategy that significantly outperforms direct imaging. Finally, we articulate two joint detection receivers implemented with bona fide quantum resources that asymptotically achieve the Helstrom bound.
We investigate our ability to determine the mean position, or centroid, of a linear array of equally bright incoherent point sources of light whose continuum limit is the problem of estimating the center of a uniformly radiating object. We consider two receivers, an image-plane ideal direct-detection imager and a receiver that employs Hermite-Gaussian (HG) spatial-mode demultiplexing in the image plane, prior to shot-noise-limited photon detection. We compare the Fisher information (FI) for estimating the centroid achieved by these two receivers, which quantifies the information-accrual rate per photon, and compare those with the quantum Fisher information (QFI): the maximum attainable FI by any choice of measurement on the collected light allowed by physics. We find that focal-plane direct imaging is strictly suboptimal, although not by a large margin. We also find that not only is the HG mode sorter, which is the optimal measurement for estimating the separation between point sources (or the length of a line object), suboptimal, but it performs worse than direct imaging. We study the scaling behavior of the QFI and direct imaging's FI for a continuous uniformly bright object in terms of its length and find that both are inversely proportional to the object's length when it is sufficiently larger than the Rayleigh length. Finally, we propose a two-stage adaptive modal receiver design that attains the QFI for centroid estimation.