SIMULATING EXPONENTIAL TIMES TO FAILURE VIA DETERMINISTIC AND QUANTUM RANDOM NUMBERS GENERATORS: A COMPARATIVE STUDY

Random number generators are fundamental tools in stochastic simulation. This study investigates the generation of exponentially distributed failure times using the inverse transform method, which is widely used in reliability engineering, across five distinct generators. The goal is to assess the influence of quantum entropy sources on generation quality and to evaluate their advantages over deterministic classical algorithms. To this end, the comparison encompasses two classical generators, a Linear Congruential Generator and Python's default PCG64, alongside three quantum-based generators: Qiskit's Aer Simulator, the IBM Quantum Cloud Platform, and an in-house QRNG currently under development. The generated sequences were statistically validated through the Anderson-Darling test, the memoryless property, tail behavior analysis, and MTTF conformity, providing a comprehensive basis for comparing the performance of each generator.