Scalable ultrafast random bit generation using wideband chaos-based entropy sources

TL;DR

This paper presents a novel chaos-based optical entropy source with over 100 GHz bandwidth, achieving ultrafast true random bit generation at rates exceeding 6 Tb/s through parallel channels, suitable for secure communications and high-performance computing.

Contribution

The authors introduce a broadband chaos-based entropy source using optical heterodyning, enabling ultrafast random bit generation with linear scalability across multiple channels.

Findings

Single-channel entropy rate of 1.86 Tb/s

Achieved 6.144 Tb/s with four channels

No observable interchannel correlation

Abstract

The exponential growth of data transmission and processing speeds in modern digital infrastructure requires entropy sources capable of producing large volumes of true randomness for information security. Chaotic emissions from semiconductor lasers are attractive in this context because of their fast dynamics and nonrepetitive behavior. Their spectral bandwidth, however, is typically limited to several tens of gigahertz, which constrains the achievable entropy rate and makes ultrafast random bit generation difficult without substantial post-processing. Here, we demonstrate a chaos-based entropy source that employs optical heterodyning between the chaotic emission from a semiconductor laser and an optical frequency comb, yielding a bandwidth exceeding 100 GHz and an experimentally verified single-channel entropy rate of 1.86 Tb/s. By directly extracting multiple bits from the digitized output of the entropy source, we achieve a single-channel random bit generation rate of 1.536 Tb/s, while four-channel parallelization reaches 6.144 Tb/s with no observable interchannel correlation. This linear scalability suggests that aggregate throughput could reach hundreds of terabits per second with additional parallel channels. The broadband, low-overhead photonic architecture presented here provides a viable route to real-time, ultrafast random bit generation with broad implications for secure communications, high-performance AI computing, and large-scale data analytics.