Random number extraction from optically trapped Brownian oscillator using an iterative algorithm

True random number generators are in high demand for secure cryptographic algorithms. Unlike algorithmically generated pseudo-random numbers, they are unclonable and non-deterministic. In this paper, we extract the white noise from stochastic Brownian Markov trajectories and use it to generate random numbers that qualify NIST standard tests of randomness. We trap colloidal particles in water using optical tweezers and record its confined Brownian motion in real-time. Next, in a two-step process, we use the initial section of incoming data to train and calibrate our iterative algorithm on the trap stiffness and viscosity of the solution based on the autocorrelation and power spectrum properties of the noise; then, we extract random arrays from the next section of the data. Interestingly, we get the best random number sequence for the best calibration. We test the random number sequence, which we have obtained, using standard randomness tests and observing the randomness to improve with increasing sampling frequencies.1 In the next steps, we extend this method to a wider class of processes, such as an optically trapped particle modulated by a square pulse or an external colored noise generated by an Ornstein Uhlenbeck process – we estimate the timescale of both the modulation and viscous effect using our algorithm.