In the last two decades, the rapid development of quantum technology established random number generation via quantum processes as an efficient and reliable method to obtain genuinely nondeterministic random numbers.
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Random numbers play an essential role in many fields, such as secure telecommunication, cybersecurity, stochastic computer simulations, statistics, and cryptography.
People have been using random numbers for millennia in games of chance. Tossing a coin or a piece of bone evolved into rolling dice with pips that still provide random numbers for gaming and gambling today. The unpredictable yet equal probability associated with the random numbers provides enhanced security for the next-generation information technologies, such as big data, cloud computing, and the internet of things.
The random numbers guarantee fair outcomes in lotteries and are essential for the aptly-named 'Monte Carlo' simulation method (acknowledging the gambling roots of the random number generation) that can solve otherwise intractable scientific problems.
Random Numbers That Are Not Random
In many applications, methods that simulate the statistics of certain number distributions can be considered random. However, these algorithmically generated numbers mimic the random distribution and are not truly random numbers. Methods that generate random-like numbers starting from a small string of bits, called a seed, via deterministic algorithms are called pseudorandom number generators or PRNGs.
Such PRNGs could produce numbers obeying any random distribution. The standard practice is to provide a uniform number distribution from which users can derive the most suitable number distribution for the required application using well-known transformations. However, the random number sequences generated by PRNG always have a particular level of predictability, limited only by the amount of available computational power.
Such limitations compromise the security of any encryption protocols based on such algorithms, making the PRNGs far from ideal for cryptographic applications.
How to Generate a Truly Random Number?
True random number generators (TRNGs) derive random numbers from classical physical processes considered unpredictable (for all practical purposes). The unpredictability may arise from uncontrollable degrees of freedom (noise) or from systems with chaotic behavior (like throwing a dice).
TRNGs based on noise or jitter in electronic circuits can be very affordable and compact. The downside is that the behavior of these classical TRNGs, although very complex (their random numbers are likely to pass stringent randomness tests), is not entirely random as they predictably interact with the environment. Besides, such TRNGs are difficult to model, meaning that it is impossible to ensure that they are operating properly and not being manipulated. Thus, it is still a challenge to create a good TRNG and nearly impossible to certify it.
Further Reading: Why Don’t We See the Heisenberg Uncertainty Principle in the Everyday World?
Exploiting the Unpredictable Nature of Quantum Mechanics
The development of quantum mechanics in the first half of the 20th century led to a conceptual departure from classical mechanics and introduced the notion that the measurement outcome of a quantum process can be intrinsically random. The probabilistic nature of the wavefunctions (that describe the quantum phenomena) means that a measurement result can never be predicted better than blindly guessing.
The intrinsic randomness and unpredictability of quantum physics have been confirmed over and over again by theoretical and experimental research throughout the 20th century.
Quantum Physics Generates True Random Numbers
The first practical quantum random number generators (QRNGs), developed during the second half of the 20th century, were based on the detection of radioactive decay events. Although they produced truly random numbers, these QRNGs were cumbersome and the use of radioactive materials was a health hazard. In 2001, the Swiss company ID Quantique introduced the first commercial optical QRNG. Since then, this type of relatively low-cost, compact, and reliable QRNGs became the dominant source of randomness for a wide range of modern cryptographic applications.
There are several different implementations of the optical QRNGs but they all comprise a light source (laser or LED), a transmission element, where the random process takes place, and a detection system (usually single-photon detectors) that records the outcome of the random process. The random process can involve detecting the path of a single photon after a beam splitter, single-photon arrival time, the vacuum fluctuations of an optical field, or the phase fluctuations in spontaneous light emission.
Integrated Optoelectronic Devices for Fast and Low-Cost QRNGs
In the simplest implementation of an optical QRNG, a single photon is directed toward a partially reflecting mirror. According to the rules of quantum mechanics, the photon has a chance of being reflected or going through the mirror. The photon effectively is a superposition of its reflected and transmitted states until it is detected by one of the two photodetectors monitoring the reflected and transmitted optical paths (each of these events can be associated with either '0' or '1'). If the transmissivity of the mirror is as close as possible to 50%, then the setup acts as an unbiased source of randomness.
In reality, single-photon QRNGs offer a relatively low speed of number generation and suffer from a dead time after a detection event, during which they are less sensitive to detecting a new photon. Recent developments in optoelectronics paved the way toward the development of the next-generation optical QRNGs that can offer a substantially higher speed of operation, reaching hundreds of Mb/s or even Gb/s.
Latest Generation of QRNGs
The latest products of companies like ID Quantique, Quside, and QuintessenceLabs represent integrated platforms directly converting photon-counting detection, phase-fluctuation, or vacuum-fluctuation events into digital output.
Real-time post-processing and randomness monitoring algorithms are integrated into the platform ensuring that the conversion from quantum randomness to the actual random numbers is straightforward and unaffected by other unaccounted physical processes that could increase predictability or thwart security.
The ever-increasing requirements for data protection and the online security threats from the exponentially growing computing power available for new-algorithm attacks require high-quality, high-speed, and stable random number generation services.
To answer the demand, special cloud-based providers of randomness-as-a-service offer application programming interfaces (APIs) that combine random numbers from different types of QRNGs. In this case, as long as at least one of the devices provides true randomness, the applications are secure, thus offering superior security.
References and Further Reading:
Ma, X., et al. (2016) Quantum random number generation. npj Quantum Inf 2, 16021. Available at: https://doi.org/10.1038/npjqi.2016.21
Herrero-Collantes, M., and Garcia-Escartin, J. C., (2016) Quantum Random Number Generators. arXiv:1604.03304vh. Available at: https://arxiv.org/abs/1604.03304v2
J. Tucker (2021). Certifiable quantum random number generation picks up the pace. [Online]. Physics World. Available at: https://physicsworld.com/a/certifiable-quantum-random-number-generation-picks-up-the-pace (Accessed on 14 December 2021)
Huang, L., et al. (2021) Quantum random number cloud platform. npj Quantum Inf 7, 107. Available at: https://doi.org/10.1038/s41534-021-00442-x