  As the bridge between the analog world and digital computers,
analog-to-digital converters are generally used in modern information systems
such as radar, surveillance, and communications. For the configuration of
analog-to-digital converters in future high-frequency broadband systems, we
introduce a revolutionary architecture that adopts deep learning technology to
overcome tradeoffs between bandwidth, sampling rate, and accuracy. A photonic
front-end provides broadband capability for direct sampling and speed
multiplication. Trained deep neural networks learn the patterns of system
defects, maintaining high accuracy of quantized data in a succinct and adaptive
manner. Based on numerical and experimental demonstrations, we show that the
proposed architecture outperforms state-of-the-art analog-to-digital
converters, confirming the potential of our approach in future
analog-to-digital converter design and performance enhancement of future
information systems.
