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arXiv:2402.08047 · New Journal of Physics

Adaptive quantum accelerated imaging for space domain awareness

Hyunsoo Choi, Fanglin Bao, Zubin Jacob

Published Jul 1, 2024• 3 citations
arXivHTMLPDF

Abstract

Abstract The growth in space activity has increased the need for Space Domain Awareness (SDA) to ensure safe space operations. Imaging and detecting space targets is, however, challenging due to their dim appearance, small angular size/separation, dense distribution, and atmospheric turbulence. These challenges render space targets in ground-based imaging observations as point-like objects in the sub-Rayleigh regime, with extreme brightness contrast but a low photon budget. Here, we propose to use the recently developed quantum-accelerated imaging (QAI) for the SDA challenge. We mainly focus on three SDA challenges (1) minimal a priori assumptions (2) many-object problem (3) extreme brightness ratio. We also present results on source estimation and localization in the presence of atmospheric turbulence. QAI shows significantly improved estimation in position, brightness, and number of targets for all SDA challenges. In particular, we demonstrate up to 2.5 times better performance in source detection than highly optimized direct imaging in extreme scenarios like stars with a 1000 times brightness ratio. With over 10 000 simulations, we verify the increased resolution of our approach compared to conventional state-of-the-art direct imaging paving the way towards quantum optics approaches for SDA.

DOI
10.1088/1367-2630/ad668c

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Abstract The growth in space activity has increased the need for Space Domain Awareness (SDA) to ensure safe space operations. Imaging and detecting space targets is, however, challenging due to their dim appearance, small angular size/separation, dense distribution, and atmospheric turbulence. These challenges render space targets in ground-based imaging observations as point-like objects in the sub-Rayleigh regime, with extreme brightness contrast but a low photon budget. Here, we propose to use the recently developed quantum-accelerated imaging (QAI) for the SDA challenge. We mainly focus on three SDA challenges (1) minimal a priori assumptions (2) many-object problem (3) extreme brightness ratio. We also present results on source estimation and localization in the presence of atmospheric turbulence. QAI shows significantly improved estimation in position, brightness, and number of targets for all SDA challenges. In particular, we demonstrate up to 2.5 times better performance in source detection than highly optimized direct imaging in extreme scenarios like stars with a 1000 times brightness ratio. With over 10 000 simulations, we verify the increased resolution of our approach compared to conventional state-of-the-art direct imaging paving the way towards quantum optics approaches for SDA.
Imaging point sources with low angular separation near or below the Rayleigh criterion are important in astronomy, e.g., in the search for habitable exoplanets near stars. However, the measurement time required to resolve stars in the sub-Rayleigh region via traditional direct imaging is usually prohibitive. Here we propose quantum-accelerated imaging (QAI) to significantly reduce the measurement time using an information-theoretic approach. QAI achieves quantum acceleration by adaptively learning optimal measurements from data to maximize Fisher information per detected photon. Our approach can be implemented experimentally by linear-projection instruments followed by single-photon detectors. We estimate the position, brightness, and the number of unknown stars 10∼100 times faster than direct imaging with the same aperture. QAI is scalable to a large number of incoherent point sources and can find widespread applicability beyond astronomy to high-speed imaging, fluorescence microscopy, and efficient optical read-out of qubits.
Low angular separation point sources are vital in astronomy, such as the search for exoplanets. Traditional imaging is time-consuming, but we propose quantum-accelerated imaging (QAI) to reduce measurement time using an information-theoretic approach. QAI maximizes Fisher information per detected photon by adaptively learning optimal measurements from data. Using linear-projection instruments and single-photon detectors, we estimate the position, brightness, and number of unknown stars 10-100 times faster than direct imaging. QAI is scalable and applicable in high-speed imaging, fluorescence microscopy, and optical read-out of qubits beyond astronomy.
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Detecting terrestrial exoplanets in the habitable zones of nearby stars remains a critical challenge. Such planets can be 10810^8108 to 101010^{10}1010 times fainter than their host stars and lie at diffraction-limited angular separations, where starlight strongly obscures the companion signal. Here we present an adaptive quantum measurement method for estimating the number, positions, and brightnesses of mutually incoherent point sources in the sub-Rayleigh, ultra-high-contrast regime, operating at contrasts down to 10−810^{-8}10−8 -- five orders of magnitude beyond previous quantum imaging approaches to exoplanet detection. The method adopts a spatial-mode basis that is updated to maximize the quantum Fisher information per detected photon. Estimation is performed by maximum likelihood in log-brightness coordinates, and the source count is determined by Bayesian-information-criterion (BIC) model selection directly from photon-count statistics, without a tunable detection threshold. For point sources within sub-Rayleigh separations and with brightness ratios spanning eight orders of magnitude, the method reconstructs complete scenes with a mean success rate of 72.5%72.5\%72.5%. Furthermore, it is robust to misalignment, maintaining a 71.3%71.3\%71.3% success rate under offsets of up to six pixels. These results demonstrate that terrestrial exoplanets can be detected below the Rayleigh limit, a regime previously inaccessible to direct imaging.
Quantum illumination offers a significant theoretical advantage for target detection in high background noise environments. However, its practical deployment in free-space channels is hindered by atmospheric turbulence. Stochastic fluctuations in atmospheric turbulence inevitably degrade the quantum signature, rendering the real-time evaluation of quantum advantage under such dynamic conditions a critical yet unresolved challenge. To circumvent the reliance on costly direct turbulence measurements, we propose a physics-driven framework that integrates Kolmogorov-Arnold networks directly bridge macroscopic meteorological observations with microscopic quantum channel dynamics. Trained on 105,120 samples from 12 climatically diverse sites and validated on 26,280 unseen samples from three extreme boundary conditions (arid continental, tropical maritime, high-altitude plateau), our approach establishes a physically consistent mapping from standard meteorological variables to the temporal evolution of the quantum advantage. This end-to-end system dynamically quantifies the degradation of quantum advantage across diverse turbulence conditions. Our results provide a rigorous theoretical and data-driven pathway for environmental adaptation, facilitating the transition of quantum radar networks from proof-of-principle demonstrations to all-weather operational systems.