Example Use Cases and Applications of QRNG QRNG has the potential to become the de facto hardware-based random number generator for a wide range of applications, digital services, and devices used in our daily lives. QRNG for cryptography: Cryptographic protocols and algorithms rely on random numbers, and QRNG can provide a trusted source of true random numbers. This makes QRNG crucial for enabling trusted authentication and encryption, enhancing the security of our information, apps, and services. Therefore QRNG hardware can potentially become an integrated part of all our digital devices. QRNG for gaming and lottery: The gaming and lottery industries can immensely benefit from a true random number generator. The true randomness of QRNG is a crucial feature required by any gaming or lottery application to eliminate bias and prevent users from increasing their chances of winning by predicting biased outcomes. This guarantees fairness that is not possible for players to manipulate. QRNG for Monte Carlo simulations: Monte Carlo simulations provide a robust method for predicting outcomes and making informed decisions in complex systems. They have applications in a wide range of areas, including economics, finance, energy, and manufacturing. Monte Carlo simulations rely on random number generators, and the quality of the generated random numbers can significantly impact the accuracy and runtime of simulations. Therefore, QRNGs are valuable assets for implementing practical, useful, and accurate Monte Carlo simulations. ËÙ J. E. Gentle, Random number generation and Monte Carlo methods, Vol. 381 (Springer, 2003).ô ÕÙ N. Ferguson and B. Schneier, Practical cryptography, Vol. 141 (Wiley New York, 2003)Ù ÃÙ V. Vovk, A. Gammerman, and G. Shafer, Algorithmic learning in a random world, Vol. 29 (Springer, 2005)Ù ÐÙ X. Ma, X. Yuan, Z. Cao, B. Qi, and Z. Zhang, Quantum random number generation, npj Quantum Information 2, 1 (2016)Ù »Ù M. Herrero-Collantes and J. C. Garcia-Escartin, Quantum random number generators, Reviews of Modern Physics 89, 015004 (2017)Ù æÙ X. Ma, F. Xu, H. Xu, X. Tan, B. Qi, and H.-K. Lo, Postprocessing for quantum random- number generators: Entropy evaluation and randomness extraction, Physical Review A 87, 062327 (2013)Ù ³Ù C. Bruynsteen, T. Gehring, C. Lupo, J. Bauwelinck, and X. Yin, 100-gbit/s integrated quantum random number generator based on vacuum fluctuations, PRX quantum 4, 010330 (2023). ©Outshift by Cisco 2024 Quantum Random Number Generator 5