What are the Differences Between Quantum Random Number Generators and Random Number Generators?

Random number generators have always existed in the natural world in certain phenomena such as radioactive decay. Recently, they became associated with the man-made algorithm versions that exist online with a wide range of applications. Quantum random number generators aren’t as well-known in the mainstream, and the devices use quantum mechanical processes to generate random numbers that are difficult to predict.

Caption: A video about random numbers.

What is a Random Number Generator?

Random number generators are computer programs that generate numbers with no discernible pattern or predictability. RNG is used in a variety of applications, including cryptography, statistical analysis, and gaming. The most obvious place to find them is the online casino industry, which relies on them for fairness. It was first introduced when games like roulette were digitized, as sites needed a way to draw the numbers fairly. It’s still used in virtual roulette games like American Roulette, but it isn’t needed in live games like Lightning Roulette because these use wheels in the real world.

RNG can be divided into two types. You can find true random number generators, which exist in the real world, and pseudo-random number generators, which are man-made. True random number generators generate numbers from physical sources of randomness. These include atmospheric noise, radioactive decay, and thermal noise. These sources are unpredictable and produce truly random numbers.

Pseudo-random number generators, on the other hand, generate numbers that appear random but are pre-determined. The numbers generated by a pseudo-random number generator are not truly random, as they are based on the algorithm used. However, it is virtually impossible to predict the order of numbers.

The quality of a pseudo-random number generator depends on the algorithm used, and some algorithms are more predictable than others. For example, the linear congruential generator, which is one of the oldest and simplest algorithms, is relatively easy to predict. In contrast, the Mersenne Twister algorithm is widely used and has been shown to be highly unpredictable.

Caption: A video of a randomized number generator.

What are the Main Uses of Random Number Generators?

Random Number Generators have become ubiquitous in modern society and are used in an ever-expanding range of applications. One of the most important applications of RNG is in cryptography, where they are used to generate keys for encryption and decryption. In fact, the National Institute of Standards and Technology has developed a set of guidelines for RNG to be used in cryptographic applications, known as the NIST Special Publication 800-90. NIST recommends the use of entropy sources such as radioactive decay, thermal noise, or atmospheric noise for true random number generation.

Another significant application of RNG is in simulation and modeling, particularly in the field of computer science. RNG is used to simulate the randomness found in natural phenomena, such as the movement of particles or the distribution of genetic traits. In fact, RNG is so important to scientific simulations that researchers have developed a set of statistical tests to evaluate the quality of an RNG. One popular set of tests is the Diehard Tests, which evaluate the statistical properties of an RNG and are used to ensure that they produce high-quality random numbers.

RNG is also widespread in finance, where it is used to generate random fluctuations in the stock market and other financial markets. The use of RNG in finance has grown substantially in recent years as high-frequency trading algorithms have become more prevalent. These algorithms use RNG to generate random numbers that are used to make automated trading decisions in a matter of microseconds. In fact, according to a report by the TABB Group, over 50% of all trades on U.S. stock exchanges are made by computer algorithms now.

RNG is also highly effective in gaming in creating the element of chance and unpredictability that makes games exciting. RNG is used to simulate dice rolls, card shuffles, and other chance events in video games and online casinos. In fact, online casinos rely heavily on RNG to ensure that their games are fair and random.

How does RNG Compare to QRNG, and How do They Differ?

Random number generators and quantum random number generators both generate random numbers, but they differ in the methods they use to do so. RNG is based on mathematical algorithms or physical processes, while QRNG relies on quantum mechanical processes.

One key difference between RNG and QRNG is the level of randomness and unpredictability of the numbers generated. RNG generates random numbers by using algorithms or physical processes, which may be predictable to an extent. This predictability means that the random numbers generated by an RNG are not truly random but rather pseudorandom. In contrast, QRNG generates random numbers using the inherent randomness of quantum mechanics, which is truly unpredictable.

Another difference between RNG and QRNG is their resistance to attacks. RNG can be vulnerable to attacks by hackers who can analyze the algorithm used to generate random numbers and predict future numbers. This can compromise the security of applications that rely on RNG, such as cryptography. In contrast, QRNG is considered more secure, as it generates truly random numbers that are not predictable, even with advanced attacks. This makes them particularly useful in applications where security is paramount.

The physical implementation of RNG and QRNG also differs. RNG can be implemented using software or hardware devices that are relatively simple and inexpensive to produce. QRNG, on the other hand, requires specialized hardware that is much more complex and expensive to produce. QRNG typically uses lasers or other quantum optical systems that require precise control and isolation from the environment to maintain the quantum state of the system.

RNG has proven to be incredibly useful in the modern age, and there’s no disputing the way it has been implemented well across a range of applications. However, it may one day be replaced by QRNG, which is more effective and truly random. Currently, though, the latter process is impactable and expensive to produce in most industries.