Random prime number generation is crucial for the implementation of several encryption and signature protocols in cryptographic applications. Recently, Quantum Random Number Generators (QRNGs) have emerged as a solution to obtain information-theoretically secure randomness, setting them apart from algorithmic generators. Nonetheless, their output is still prone to deterioration under a flawed implementation and thus requires continuous statistical validation. This process is uniquely challenging for prime number generators since they cannot be submitted to the traditional testing suites, which are primarily designed to test uniform pseudorandom sources. Here, we enable direct statistical testing of prime Random Number Generators (RNGs) by extending a validation framework based on an equiprobable binning of the prime distribution with a predictive machine learning model, which can independently learn correlations in the prime output. The validity of this framework is then verified through extensive cryptanalysis of a prime QRNG based on quadrature measurements of the vacuum state. The developed model successfully identifies flawed configurations of the prime QRNG for output lengths up to 128 bits. This assessment was additionally extended to a purely classical scheme derived from electronic noise measurements. Although no increase in the model’s predictive capacity is seen, our testing framework can nonetheless demonstrate that this classical source produces a conclusively biased prime distribution. In turn, the implemented QRNG remains resilient against this type of cryptanalysis.

In this work, a reliable and low computational õ complexity frame synchronization method for quantum key distribution (QKD) systems is proposed. The proposed method is based on the computation of the quantum bit error rate (QBER) and requires no more hardware than the one used for qubit coding and decoding. In a initial stage, pre-shared and received sequences are shifted until a minimum QBER value is reached. In a second stage, to confirm that the frames are correctly positioned, and to ensure that during key transmission a correct association of each received bit is made with the corresponding bit sent by the transmitter, a pre-determined number of inverted frames is sent, rising the QBER value to maximum values. As the number of inverted frames is a pre-agreed number, after detecting the first inverted frame it is possible to determine exactly when the secret key transmission will start. We use a polarization-encoding QKD testbed to assess the overall performance of the method. With the proposed frame synchronization method, the synchronization is achieved after roughly 104clock cycles, despite the concerns imposed by the low probability detection inherent to the QKD systems.