Scalable tbps all-optical parallel true random number generation method

Abstract

A tbps all-optical parallel true random number producing method having ultra-strong scalability comprises the steps of utilizing an optical device to produce an ultra-short pulse sequence, conducting spectrum cutting on the obtained ultra-short pulse sequence to obtain N narrow-band ultra-short pulse sequences, then conducting power adjustment on the N narrow-band ultra-short pulse sequences to enable average powers of the sequences to equal, converting randomly fluctuating peak powers into different pulse states, encoding the peak powers as 1 if the peak powers are larger than pulse output of the average powers, encoding the peak powers as 0 if the peak powers are lower than pulse-free output of the average powers, achieving repeated-frequency N all-optical parallel true random number production and filtering out random numbers through N optical band pass filter elements. According to the tbps all-optical parallel true random number producing method, the limitation of inherent periodicity of a parallel pseudo random number generator is overcome, meanwhile at least 10000 independent parallel true random numbers are output, the scalability of an existing parallel true random number generator is improved by 3 to 4 magnitudes, and the demands for large-scale parallel computing and high-speed secure network communication are met.

Description

technical field [0001] The invention relates to a parallel true random number generation method, in particular to a scalable Tbps all-optical parallel true random number generation method, which is suitable for the fields of parallel Monte Carlo simulation, large-scale parallel computing and secure communication. Background technique [0002] Parallel Monte Carlo simulation has important applications in nuclear physics, computational chemistry, biomedicine, financial engineering, macroeconomics and other fields. The generation of high-speed parallel random numbers is the cornerstone of parallel Monte Carlo simulation. The accuracy of random numbers determines the accuracy of simulation, and the speed of random numbers restricts the speed of simulation. [0003] An ideal parallel random number generation method needs to meet the following conditions: First, it does not contain intra-stream correlation. That is, the random number sequence used on each processor must have high...

AI Technical Summary

Problems solved by technology

[0010] The purpose of the present invention is to provide a scalable Tbps all-optical parallel true random number generation method to solve the problems of insufficient code rate and poor scalability in the prior art, and to meet the requirements of modern Monte Carlo simulation, large-scale Practical requirements such as parallel computing and network security communication

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