Introduction: Three Types of RNGs Random numbers are widely used in many aspects of modern technologies including scientific and engineering simulations [1], cryptography [2], artificial intelligence [3], gaming and finance. These tasks require random numbers that are truly unpredictable, which cannot be guaranteed in classical processes. The most widely used random number generators are pseudo-random number generators (PRNGs), which rely on mathematical algorithms and the presence of a secure seed. Even though some carefully designed PRNGs can pass statistical randomness tests, they are fundamentally predictable and deterministic. Therefore, once the seed is disclosed and computational power is strong, the random numbers generated by PRNGs are fully deterministic [1]. As such there is a need for a new type of random number generator capable of producing true random numbers. These type of true random number generators (TRNGs) utilise random physical processes to generate true random numbers. The random processes in TRNGs usually refer to classical physical processes like thermal noise and atmospheric turbulence [4]. Even though these TRNGs turn out to be more secure than PRNGs due to their inherent chaotic dynamics, the fundamental physics behind these classical processes is still deterministic while the unpredictability comes from the complexity of physical system [4]. Thanks to the inherent random nature of quantum mechanics, quantum random number generators (QRNGs) can, in principle, yield true random numbers which are mathematically unpredictable and nondeterministic without assumptions on devices, for example, QRNGs based on Bell tests [4, 5]. Compared to other quantum technologies, QRNGs are usually simpler and more mature, and are ready to be implemented to solve real-life challenges. FIG. 1. The Cisco QRNG architecture. The two-step randomness extraction implemented on the FPGA guarantees all classical correlations have been removed. ©Outshift by Cisco 2024 Quantum Random Number Generator 2