We present two quantum algorithms which generate a random sequence of integers modula a given N ∈ ℕ in which the expected frequency distribution over the set ℤN is different from the uniform distribution.
In this thesis we study the generation of randomness from quantum mechanical sources: quantum random number generators (QRNGs). Such devices are thought to provide a better quality of randomness than is possible with classical devices, but the issue is in need of more rigorous study. In Chapter 1 we provide the necessary background for the thesis from both computability and probability theory. We then present and extend some recent results providing a more theoretical grounding for the indeterminism in quantum mechanics. In particular, we show that sequences of quantum random bits are incomputable in the strongest sense: no bit can be provably computed in advance. In Chapter 2 we study in detail the use of von Neumann normalisation for QRNGs producing both finite strings and infinite sequences of bits; such normalisation methods are necessary in order to counter for experimental imperfections. We show that, in the case of a constantly biased source, this normalisation gives the desired uniform distribution. The effect of this normalisation on the (in)computability of the generated sequences is studied, and it is shown that algorithmic randomness and Borel normality are preserved, but e-randomness (and thus incomputability) is, in general, not. Experimental bounds for the extent of departure from the uniform distribution in the non-ideal case of a slowly drifting bias are derived. In Chapter 3 we propose a new QRNG which uses entangled photon pairs and is certified to produce incomputable bits by value indefiniteness; this is the first QRNG with explicit certification by a physical principle. The effects of various experimental imperfections are discussed in detail, and care is taken to make the proposed QRNG as robust as is possible to these. A robust method based on XORing the produced bitstrings together is proposed to further improve the quality of the distribution produced by the QRNG. Various improvements and optimisations to this scheme are discussed.
The stochastic nature of photocurrent generation can be used as a source of entropy for various applications in the field of cybersecurity. Cybersecurity software solutions in the field of software engineering, particularly steganography, require random number sequences (RNS) in their algorithms. Standard methods provided by frameworks and programming languages often fail to ensure sufficient randomness. Physical devices measuring stochastic processes, such as beta radiation counters, can provide true unpredictability but are impractical to maintain. As an alternative, a photodiode can be used as an RNS source by measuring its photocurrent. Experiments under near-total darkness (0.1 lux) and uniform illumination (200 lux) confirmed that the current values follow a Gaussian distribution and demonstrate high unpredictability within a defined range. By means of software engineering, a set of continuous photocurrent measurements can be transformed into a sequence of integer random numbers following a Poisson distribution. An experimental setup was developed to investigate this hypothesis and to evaluate the quality of the generated sequences with respect to cybersecurity requirements. This approach enables the development of a cost-effective, accessible, and cryptographically secure hardware random number generator without relying on bulky or expensive equipment.