Adaptive Quantum-Thermal Imaging

Team members: Dr. Fanglin Bao, Dr. Adran Rubio-Lopez, Hyunsoo Choi

Quantum-accelerated imaging of N stars

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. These stars are thermal sources so traditional quantum super-resolution approaches of isolated fluorescent sources based on anti-bunching are not viable. 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∼100  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.