Quantum-accelerated imaging of N stars

TL;DR

This paper introduces quantum-accelerated imaging (QAI), a method that significantly reduces measurement time for resolving closely spaced stars by adaptively optimizing measurements based on information theory, with broad applications beyond astronomy.

Contribution

The paper presents a novel quantum-accelerated imaging technique that adaptively learns optimal measurements to drastically improve imaging speed for multiple incoherent sources.

Findings

Estimates star positions 10-100 times faster than traditional methods.

Achieves quantum acceleration through adaptive measurement learning.

Scalable to large numbers of incoherent point sources.

Abstract

Imaging point sources with low angular separation near or below the Rayleigh criterion is 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 a single-photon detector array. We estimate the position, brightness and the number of unknown stars $10\sim100$ times faster than direct imaging with the same aperture. QAI is scalable to 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.