A truely random number machine

Mario Stipčević, Çetin Kaya Koç

C. R. A. Augusto, A. S. Ribeiro

We propose to address in a natural manner, the modular variable concept, which was introduced in 1969 by Aharonov, Pendleton and Petersen to explain certain non-local properties of Quantum Mechanics. We have argued elsewhere, with the use of some elementary number theoretic properties, that a nite dimensional tensor product of two spaces with relatively prime dimensions cannot be regarded as two truely separate degrees of freedom, but eectively, only as one single degree of freedom. We show in this present work that a physical application of this remarkable mathematical fact can be that of clarifying the notion of modular variables in Quantum Mechanics.

Michael Brooks

Wanlin Du, Ling Wang, Yuanzhe Zhu +1 more

Abstract Random numbers are critical to information security. Quantum random numbers are theoretically truly random and unpredictable. However, the measurement process of a quantum random number generator (QRNG) can be affected by environmental disturbances that compromise the integrity of the generated random numbers. A machine learning model is proposed to assess the stochasticity of the continuous variable QRNG under the influence of vacuum noise. The model is designed to detect the correlation between the randomness of the QRNG being corrupted under the influence of classical noise (electrical noise). In addition, our model detects a decrease in the randomness of the QRNG random numbers when the electrical noise intensity increases to a certain correlation. The results show that machine learning (ML) can be used as a measure of the quality of QRNG.

Tommaso Faorlin

The generation of good random numbers impacts basic research and applications beyond pure academic interests: random numbers are required for countless applications, such as cryptography and simulations. For most applications, it is of outmost importance to know if a set of numbers is truly random, pseudo-random or contains some residual correlations. Tensor networks are powerful data structures that spring from quantum many-body physics and are now increasingly applied to machine learning applications. This thesis plans to explore the intersection between these two fields, applying quantum-inspired machine learning to random number generation. We aim to perform and characterise novel statistical checks comparing and characterising the statistical quality of different number sets: correlated, pseudo-random, and quantum-random.

Zhiliang L. Yuan