Sampled subblock hashing for large-input randomness extraction

Randomness extraction is an essential postprocessing step in practical quantum cryptography systems. When statistical fluctuations are taken into consideration, the requirement of large-input data size could heavily penalize the speed and resource consumption of the randomness-extraction process, thereby limiting the overall system performance. In this work, we propose a sampled subblock hashing approach to circumvent this problem by randomly dividing the large-input block into multiple subblocks and processing them individually. Through simulations and experiments, we demonstrate that our method achieves an order-of-magnitude increase in system throughput while keeping the resource utilization low. Furthermore, our proposed approach is applicable to a generic class of quantum cryptographic protocols that satisfy the generalized entropy-accumulation framework, presenting a highly promising and general solution for high-speed postprocessing in quantum cryptographic applications such as quantum key distribution and quantum random number generation.