Parthasarathy Srinivasan and Tapas Pramanik, Oracle Corporation, USA

Quantum Random Number generation(Qrng) provides a superior alternative than classical Random Number Generation (Crng) and the two experiments outlined in this work provide validation of this premise. The first experiment utilizes Random Numbers generated using Qrng and CRng to provide data samples as input to an Evolutionary Algorithm (namely Differential Evolution) , which mutates and thresholds these samples using the known rastrigin and rosenbrock functions and evolves the solution pool towards convergence. Rigorous statistical analysis employing p-values is applied to the convergence data to prove that Qrng is indeed Qualitatively superior to Crng (Qrng surpasses Crng by a factor of 2). These results are complemented with yet another experiment wherein the Qrng and Crng samples are generated and statistically compared with, yet another tool namely bottleneck distance , which leads to a logical conclusion consistent with the one obtained in the first experiment (Qrng again surpasses Crng by the same factor of 2 in the range of statistical distances obtained from employing the two Rng methods).

Bottleneck distance, p-value, Evolutionary Algorithm, Takens Embedding