Semiconductor Emitters in Entropy Sources for Quantum Random Number Generation

Abstract

Random number generation (RNG) is needed for a myriad of applications ranging from secure communication encryption to numerical simulations to sports and games. However, generating truly random numbers can be elusive. Pseudorandom bit generation using computer algorithms provides a high random bit generation rate. Nevertheless, the reliance on predefined algorithms makes it deterministic and predictable once initial conditions are known. Relying on physical phenomena (such as measuring electrical noise or even rolling dice) can achieve a less predictable sequence of bits. Furthermore, if the physical phenomena originate from quantum effects, they can be truly random and completely unpredictable due to quantum indeterminacy. Traditionally, physical RNG is significantly slower than pseudorandom techniques. To meet the demand for high‐speed RNG with perfect unpredictability, semiconductor light sources are adopted as parts of the sources of randomness, i.e., entropy sources, in quantum RNG (QRNG) systems. The high speed of their noise, the high efficiency, and the small scale of these devices make them ideal for chip‐scale QRNG. Here, the applications and recent advances of QRNG are reviewed using semiconductor emitters. Finally, the performance of these emitters is compared and discuss their potential in future technologies.

Low-coherence semiconductor light sources: devices and applications

“…Photons, as fundamental particles of light, can possess inherent randomness in their generation and behavior. This intrinsic randomness has made photons a prime candidate for generating true random numbers, and their utilization has been a cornerstone of quantum random number generation (QRNG) [196][197][198] . While lasers have historically dominated this domain, lowcoherence light sources are emerging as a promising alternative, offering new avenues to harness the inherent unpredictability of photons.…”

“…In the scenario where r 1 ¼r 2 , it describes the temporal coherence, g ð1Þ t; τ ð Þ, representing the fluctuation behavior over the time interval from the measurement moment t to t þ τ at a certain location. For stationary light, it depends only on the time difference, τ, and it can be 57,145,196,239,240 . Reproduced with permission from Zheng, G. et al 145 .…”

“…Fig.1| Applications of the low-coherence light sources compared with the incoherent light sources (left middle part) and high-coherence light sources (right middle part)57,145,196,239,240 . Reproduced with permission from Zheng, G. et al145 .…”

Assessing the quality of random number generators through neural networks

“…Random number generators (RNGs) are widely used in many applications including cryptographycally secured communications, industrial testing, Monte Carlo simulations, massive data processing, lotteries, quantitative finance, fundamental physics tests, etc [1][2][3][4]. The security analysis of RNGs is a vital issue in classical and quantum cryptographic systems in which secure unpredictable keys are necessary.…”