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arXiv:2607.06931

Exoplanet Detection Using Adaptive Quantum-Optimal Measurement

Hyunsoo Choi, Hyoung Won Baac, Zubin Jacob, Haejun Chung

Published Jul 8, 2026• 0 citations
arXivHTMLPDF

Abstract

Detecting terrestrial exoplanets in the habitable zones of nearby stars remains a critical challenge. Such planets can be \(10^8\) to \(10^{10}\) 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 \(10^{-8}\) -- 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 \(72.5\%\). Furthermore, it is robust to misalignment, maintaining a \(71.3\%\) 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.

DOI
10.48550/arxiv.2607.06931

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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.
The direct detection and characterization of exoplanets remains a grand challenge in astrophysics, primarily due to the overwhelming brightness contrast between a host star and its orbiting planets. Coronagraphy and interferometry are established techniques designed to mitigate this stellar glare, but their performance is fundamentally limited by classical noise sources, including shot noise and detector inefficiencies. This paper explores the burgeoning field of quantum-enhanced interferometry as a paradigm-shifting approach to overcome these classical limitations in the context of exoplanet coronagraphy. We review the principles of high-contrast imaging, the fundamental limits of classical interferometry, and introduce how quantum resources, such as entangled photons and quantum non-demolition measurements, can enable sensitivities beyond the standard quantum limit. Specifically, we investigate how quantum correlation techniques and super-resolution concepts can be integrated into advanced coronagraphic and interferometric architectures to significantly improve the signal-to-noise ratio for faint planetary signals. The proposed methodologies aim to leverage quantum metrology principles to reduce noise floors, enhance angular resolution, and suppress unwanted starlight, thereby paving the way for the direct imaging and spectroscopic characterization of Earth-like exoplanets in the habitable zones of distant stars. This theoretical framework provides a roadmap for future experimental endeavors and mission concepts that could unlock unprecedented capabilities in exoplanet research.
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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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Interferometric methods have been recently investigated to achieve sub-Rayleigh imaging and precision measurements of faint incoherent sources up to the ultimate quantum limit. Here we consider single-photon imaging of two point-like emitters of unequal intensity. This is motivated by the fact that pairs of natural emitters typically have unequal brightness, for example, binary star systems and exoplanets. We address the problem of estimating the transverse separation d or the relative intensity . Our theoretical analysis shows that the associated statistical errors are qualitatively different from the case of equal intensity. We employ multi-plane light conversion technology to implement Hermite–Gaussian (HG) spatial-mode demultiplexing (SPADE), and demonstrate sub-Rayleigh measurement of two emitters with a Gaussian point-spread function. The experimental errors are comparable with the theoretical bounds. The latter are benchmarked against direct imaging, yielding an improvement in the signal-to-noise ratio, which may be significant when the primary source is much brighter than the secondary one, for example, as for imaging of exoplanets.