Constantine Quantum Technologies' RNG package

This is Constantine Quantum Technologies's solution to ORCA Computing's challenge Random number generation using boson sampling.

Team

• Abdellah Tounsi
• Amina Sadik
• Mohamed Messaoud Louamri
• Nacer eddine Belaloui
• Wafa Makhlouf

Getting started

Installation

To install the package, run:

$ pip install cqt_rng

Usage

To start generating random numbers, you need to pick an entropy source and a post-processor. There are 3 simulated quantum entropy sources:

• BosonSampler
• UniversalQCSampler
• ShiSFSampler

And 2 real quantum entropy sources:

• BorealisSampler
• IBMQSampler

We also implemented three postprocessors:

• VonNeumannPP
• CQTPP
• NoPostProcess

Once you have chosen you entropy source and post-processor, you just need 2 lines of code (without counting the imports 😅) to generate random numbers:

from cqt_rng import RNG
from cqt_rng.post_processors import VonNeumannPP
from cqt_rng.entropy_sources import BosonSampler

rng = RNG(BosonSampler(), VonNeumannPP())
rng.generate(length = 1024)

You can also use your custom entropy sources by implementing them as subclasses of the abstract class EntropySource, and also implement your own postprocessors as subclasses of the abstract class PostProcessor.

For more example, please refer to the documentation.

More on RNG

An in-depth explanation of why we need RNG, the theory behind it, and what the package does can be found in the challenge/demo.ipynb notebook (which you can launch directly on google colab by clicking the button below).