While most usecases of Quantum Computing are infeasible due to limited resources in the currently immature quantum computers, quantum sampling and random number generation have already reached quantum advantage and commercialization respectively. So I wanted to explore this avenue.

What it does

A monte carlo generator that uses random numbers from a QRNG to model functions. A couple of distributions from which sampling can be done are also provided.

How we built it

A quantum and numpyy backed (for reference ) have been given to which which the monte carlo simulator passes calls when it wants to sample from a preselected distribution. The sampling in the quantum case is done on 10 qubits (upto 3 decimal places ) and then scaled into the required range.

Challenges we ran into

Simulating other circuits beside uniform distribution was difficult, however the mentors pointed me to insightful resources that helped me use qiskit aqua package to generate the necessary circuits.

Accomplishments that we're proud of

Unlike most current qrng ( ) that seed a numpy RNG to work, here all sampling is done directly from circuits corresponding to the distribution needed. This provides with the most authentic quantum probability distributions

What we learned

This project opened my eyes to the vast possibility in quantum sampling and meteorology that hasn't gotten as much attention as the other famous quantum algorithms.

What's next for Quantum Random Generator Augmented MonteCarlo Simulator

More distributions of probability are to be added and visualization segment that enables checking accuracy.

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