Sampled sub-block hashing for large input randomness extraction
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866911829267054592 |
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| author | Ng, Hong Jie Kon, Wen Yu Primaatmaja, Ignatius William Wang, Chao Lim, Charles |
| author_facet | Ng, Hong Jie Kon, Wen Yu Primaatmaja, Ignatius William Wang, Chao Lim, Charles |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2308_02856 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Sampled sub-block hashing for large input randomness extraction Ng, Hong Jie Kon, Wen Yu Primaatmaja, Ignatius William Wang, Chao Lim, Charles Quantum Physics 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. |
| title | Sampled sub-block hashing for large input randomness extraction |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2308.02856 |