Opt Lett. 2021 Jul 1;46(13):3045-3048. doi: 10.1364/OL.430404.
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.
在天文学中，对近或低于瑞利准则的低角分离点源进行成像非常重要，例如，在恒星附近寻找可居住的系外行星。然而，通过传统的直接成像来解析亚瑞利区域中的恒星所需的测量时间通常是不可行的。在这里，我们提出了量子加速成像（QAI），通过信息论方法显著减少测量时间。QAI 通过自适应地从数据中学习最佳测量来实现量子加速，以最大化每个探测光子的 Fisher 信息。我们的方法可以通过线性投影仪器和单光子探测器来进行实验实现。我们估计位置、亮度和未知星的数量比直接成像快 10∼100 倍，具有相同的孔径。QAI 可以扩展到大量非相干点源，并且除了天文学之外，还可以在高速成像、荧光显微镜和量子比特的高效光学读出等方面得到广泛应用。
