Sampled sub-block hashing for large input randomness extraction

TL;DR

This paper introduces a sampled sub-block hashing method that significantly improves the throughput of randomness extraction in quantum cryptography by dividing large inputs into smaller parts, enabling faster processing with low resource use.

Contribution

The paper proposes a novel sampled sub-block hashing technique that enhances speed and efficiency in quantum randomness extraction, applicable to various quantum cryptographic protocols.

Findings

Achieves an order-of-magnitude increase in system throughput

Maintains low resource utilization during processing

Applicable to a broad class of quantum cryptographic protocols

Abstract

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