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Journal of Lightwave Technology

Seed Renewable Parallel and Real-Time Toeplitz Post-Processing for QRNG

Fading Lin, Wenbo Ge, Zhijie Song, Xinxuan Cui, Yanqiang Guo, Xiaomin Guo, Liantuan Xiao

Published Aug 19, 2024• 7 citations
Publisher

Abstract

As a universal-hashing function, Toeplitz hashing is migrated in QRNG from privacy amplification of QKD to execute information-theoretically provable true randomness extraction. Though random seed needed for constructing a Toeplitz matrix is longer than the output string, Toeplitz-hashing extractor is still much more favored in practical implementation of QRNG because its relative simple structure and parallel operation property compared to the other universal-hashing strong extractor, Trevisan's extractor, especially in the booming integration realization of randomness extractor via FPGA. But two origin issues have been put off for long in the random number post-processing in FPGA besides chasing speed: collision probability threaten owing to small matrix and security parameter growth due to seed reuse. In this work, seed update and collision probability are both paid close attention when realize real-time parallel post-processing of CV-QRNG, who has a pretty practical prospect as well as greatest demand on post-processing both because of whose wide detection bandwidth and multibit discretization. We propose a sub-seed generation scheme based on reading and writing between two levels of memories in FPGA, which effectively avoid the consumption of logic resources. One seeds pool is built in storage element of the FPGA and distinct random seed is chosen for each post-processing instance by a random selection process. And the seeds pool is swiftly updated via PCIe interface once security parameter reaches a pre-set threshold. Furthermore, by elaborate layout of Toeplitz matrixes and two-layer parallel pipeline algorithm with TDM designed delicately, four-channel parallel seed renewable Toeplitz post-processing with matrix of about${\mathbf{1700}\mathbf {\, \times \, }\mathbf{2500}}$is realized with a real-time random number yield of 11.3 Gbps in one medium-configuration FPGA. In order to guarantee the robustness of the hardware-based parallel Toeplitz post-processing, timing is optimized by register replication, global clock buffers and data cache. This investigation fills an obvious hole in randomness extraction and provides a referential technique for privacy amplification.

DOI
10.1109/jlt.2024.3445327

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As a universal-hashing function, Toeplitz hashing is migrated in QRNG from privacy amplification of QKD to execute information-theoretically provable true randomness extraction. Though random seed needed for constructing a Toeplitz matrix is longer than the output string, Toeplitz-hashing extractor is still much more favored in practical implementation of QRNG because its relative simple structure and parallel operation property compared to the other universal-hashing strong extractor, Trevisan's extractor, especially in the booming integration realization of randomness extractor via FPGA. But two origin issues have been put off for long in the random number post-processing in FPGA besides chasing speed: collision probability threaten owing to small matrix and security parameter growth due to seed reuse. In this work, seed update and collision probability are both paid close attention when realize real-time parallel post-processing of CV-QRNG, who has a pretty practical prospect as well as greatest demand on post-processing both because of whose wide detection bandwidth and multibit discretization. We propose a sub-seed generation scheme based on reading and writing between two levels of memories in FPGA, which effectively avoid the consumption of logic resources. One seeds pool is built in storage element of the FPGA and distinct random seed is chosen for each post-processing instance by a random selection process. And the seeds pool is swiftly updated via PCIe interface once security parameter reaches a pre-set threshold. Furthermore, by elaborate layout of Toeplitz matrixes and two-layer parallel pipeline algorithm with TDM designed delicately, four-channel parallel seed renewable Toeplitz post-processing with matrix of about1700 × 2500{\mathbf{1700}\mathbf {\, \times \, }\mathbf{2500}}1700×2500is realized with a real-time random number yield of 11.3 Gbps in one medium-configuration FPGA. In order to guarantee the robustness of the hardware-based parallel Toeplitz post-processing, timing is optimized by register replication, global clock buffers and data cache. This investigation fills an obvious hole in randomness extraction and provides a referential technique for privacy amplification.
Quantum random number generators (QRNG) based on continuous variable (CV) quantum fluctuations offer great potential for their advantages in measurement bandwidth, stability and integrability. More importantly, it provides an efficient and extensible path for significant promotion of QRNG generation rate. During this process, real-time randomness extraction using information theoretically secure randomness extractors is vital, because it plays critical role in the limit of throughput rate and implementation cost of QRNGs. In this work, we investigate parallel and real-time realization of several Toeplitz-hashing extractors within one field-programmable gate array (FPGA) for parallel QRNG. Elaborate layout of Toeplitz matrixes and efficient utilization of hardware computing resource in the FPGA are emphatically studied. Logic source occupation for different scale and quantity of Toeplitz matrices is analyzed and two-layer parallel pipeline algorithm is delicately designed to fully exploit the parallel algorithm advantage and hardware source of the FPGA. This work finally achieves a real-time post-processing rate of QRNG above 8 Gbps. Matching up with integrated circuit for parallel extraction of multiple quantum sideband modes of vacuum state, our demonstration shows an important step towards chip-based parallel QRNG, which could effectively improve the practicality of CV QRNGs, including device trusted, device-independent, and semi-device-independent schemes.
Quantum systems are particularly suited for generating true randomness due to their inherent unpredictability, which can be justified on physical principles. However, practical implementations of Quantum RNGs (QRNGs) are always subject to noise, or uncontrollable influences, diminishing the quality of raw randomness produced. This necessitates post-processing to convert raw output into genuine randomness. In current QRNG implementations, the critical issue of seed updating is often overlooked, risking security vulnerabilities due to increased security parameters when seeds are reused in post-processing, and frequent seed updates fail to yield net randomness, while reusing seeds relies on the assumption that the original sequence inputs are independent.In this work, we have provided a specific scheme for seed updates that balances practicality and security, exploring the parallel and real-time implementation of multiple seed real-time updating toeplitz hash extractors in an FPGA to achieve parallel QRNGs, focusing on efficient hardware computation resource use. Through logic optimization, we achieved a greater number of parallel channels and a post-processing matrix size three times larger than previous works on the same FPGA platform, utilizing fewer logic resources. This resulted in a higher rate of random number generation and enhanced security. Furthermore, with the use of higher-performance ADCs, we attained a random number production rate exceeding 20Gbps.High-speed random number transfer and seed updating were achieved using the PCIe high-speed interface.This marks a significant step toward chip-based parallel QRNGs, enhancing the practicality of CV QRNGs in trusted, device-independent, and semi-device-independent scenarios.
Abstract Random Number Generators (RNGs) are devices whose utility spans from cryptography to gambling. Depending on the source of the random seed, RNGs are classified as Pseudo-Random Number Generators (PRNGs), which are based on mathematical algorithms, True Random Number Generators (TRNGs) sourced through seemingly random physical processes, and finally, Quantum Random Number Generators (QRNGs) which harness the intrinsic randomness of measured outcomes of quantum states. To ensure that the output of a QRNG is private and nearly uniform, the raw data undergoes a strong extraction process such as Toeplitz or Trevisan Hashing. The extraction process needs to be extremely efficient to achieve high-speed random number generation. The present work demonstrates state-of-the-art methods for performing information-theoretically provable randomness extraction using variations of the Toeplitz Hashing algorithm. These algorithms are implemented on various hardware for comprehensive analysis. Subsequently, these methods are applied to two raw data sets from a PRNG source and a quantum source. A new benchmark of 19.5 Gbps for Randomness Extraction using the Fast Fourier Transform-based Toeplitz Strong Extractor is demonstrated in this work. The implementation of the algorithm on a GPU-based system demonstrates notably enhanced speed, marking a significant leap beyond the existing state-of-the-art implementations.
Quantum Random Number Generators (QRNGs) serve as high entropy sources for Quantum Key Distribution (QKD) systems. The raw data from quantum sources however require post-processing to generate a nearly uniform distribution. This work presents a state-of-the-art implementation of the Toeplitz Strong Extractor on an FPGA, achieving a benchmark extraction speed of 26.57 Gbps. A detailed implementation flow of the post-processing on FPGA is presented along with the various execution speeds obtained based on different randomness extraction ratios. Raw data from an in-house phase noise-based QRNG is processed on the FPGA using the implementation and the output is validated via the NIST STS 2.1.2 statistical randomness tests suite.
Randomness extraction is indispensable for quantum random number generators, serving to eliminate bias and potential information leakage from raw measurement data. Conventional extractors operate in a block-wise fashion, requiring the complete accumulation of raw data before processing. To circumvent the latency and buffering overheads that hinder real-time random number generation, recent work introduced a stream-cipher implementation for the randomness extractor based on the Toeplitz matrix hashing. In this work, we generalize this stream-processing paradigm to the broader family of randomness extractors based on (almost dual) universal2_22​ random hashing. Specifically, we shift the computational burden from a time-consuming block-wise post-processing stage into an offline pre-processing stage that generates a pseudo-random mask. This allows the raw data to be processed by the mask on the fly using a simple bitwise exclusive-OR operation. Crucially, we prove that this stream implementation strictly preserves the security guarantees of the original block-wise protocols. We detail the transformation of three typical constructions -- based on standard Toeplitz, circulant, and modified Toeplitz matrices -- from block to stream implementations, and benchmark their practical performance using realistic quantum experimental data. We anticipate our framework will enhance the efficiency of real-time quantum cryptographic systems.
Continuous-variable quantum random number generator (cv-QRNG) has attracted much attention due to its convenient state preparation and high measurement bandwidth. Chip-size integration of this type of QRNG is expectable because all components involved have been integrated on a single chip recently. Most of the existing schemes, including all existing commercial schemes, usually use a once-and-for-all approach to evaluate quantum entropy. In this work, we propose a double-level parallel cv-QRNG scheme that integrates real-time phase-space monitoring and entropy evaluation. By using dynamic threshold monitoring and self-adapting scaling of Toeplitz matrix, the security and generation rate of QRNG can be enhanced simultaneously. Experimentally, a parallel extraction system of vacuum state double quadratures and multiple sideband modes is constructed based on heterodyne, providing sufficient raw data for high-precision and high-speed tomography reconstruction of quantum entropy source and parallel extraction of QRNG. Based on the statistical analysis of data under long-term stable operation of the system, dynamic KLD-sensitive security threshold for statistical distribution of Husimi-Q function of the entropy source is established. When a weak chaotic field is injected to simulate a thermal state attack, the KLD value jumps and quickly deviates from the steady state baseline, manifesting a sensitive identification of the attack. It is worth pointing out that the threshold parameter can be dynamically optimized according to the security requirements of actual application scenarios. An FPGA-based real-time feedback Toeplitz-hash extractor employs a maximum matrix bit-width truncation method to dynamically adjust Toeplitz matrix parameters. This optimization reduces the maximum extraction ratio interval from 6% to 1.8%, with all intervals below 1% for extraction ratios ≤76%, significantly mitigating entropy losses caused by discrete adjustment of the Toeplitz matrix, and achieving a minimum extraction ratio of 16.9%. This flexibility enables the system to accurately control the response sensitivity of abnormal signals while maintaining the real-time generation of quantum random bits. Finally, real-time generation rate of 17.512 Gbit/s is attained with security parameters at the level of 10–50 and the generated random numbers passed NIST SP 800-22, Diehard, and TestU01 standard tests. This research provides a technical path for real-time assessment of entropy source security in QRNG. The proposed scheme has good integrability and scalability, presenting a feasible solution for QRNG to enter the application stage.