Quantum Receiver Enhanced by Adaptive Learning

Quantum receivers are essential components for quantum information processing (QIP), aimed at capturing information embedded in quantum states far more efficiently than any conventional classical receiver allows. To date, only a handful of quantum-receiver structures have been laid out to approach the ultimate performance bounds in their respective QIP tasks, and their advantage in realistic operational environments is hindered by noise, turbulence, and other imperfections. Moreover, a traditional analytic approach is incapable of customizing quantum-receiver structures due to the vast parameter space of the optimization.

Data science offers tools to address complex data-processing problems subject to large parameter spaces. Here, we formulate an architecture of quantum receiver enhanced by adaptive learning (QREAL) to undertake complex QIP tasks. QREAL is implemented in a fiber-optics-based QIP hardware platform to undertake quantum-state discrimination tasks, including decoding binary phase-shift keying and six-state quadrature amplitude modulation. We show that QREAL outperforms any known quantum-receiver structure in the presence of noise and other imperfections. This work would open a new avenue toward customized QIP modules for a wide range of quantum-enhanced capabilities.