Most of us have encountered pseudo number terms at some point during our academics and more increasingly in the professional settings. Most common ways of generating pseudo numbers are by using some Hash function (or cryptographic hashing algorithm) or by some proprietary deterministic algorithms. Quantum computing has abilities to influence almost all industries/technologies and thus, in the post quantum era, the underlying security of real world systems can be greatly enhanced using quantum techniques.
Quantum random number generator (QRNG) advances over Pseudo-random number generator (PRNG)
The generation of true random numbers using quantum processes is vital for existing and next Gen technologies. We try to show that by:
- Highlighting shortcoming of current random generation methods
- Using Quantum pseudo number generation in a visual real world example
Highlighting shortcoming of current random generation methods
- Pseudo-random number generator (PRNG)
PRNG uses a certain pre-defined deterministic algorithm. Although PRNGs can offer highly unbiased random numbers, they cannot be used for applications that require information security for the following two reasons:
- Owing the deterministic nature of underlying algorithm, the PRNG-generated sequences are unpredictable so long as there is limitations of computational power such as in case of existing classical systems
- In most cases, the random seeds could be “the seconds” of the computer’s clock. However, random seeds are predictable, which is required to define the initial state of a PRNG and which results in the limit of the amount of entropy thereby compromising the security of the encryption protocol
2. Hardware random number generator (Hardware RNG)
There are no suspected benefits of Hardware RNG compared with PRNG, but it is assumed that a higher degree of confidence is associated with the randomness using a hardware method and therefore its widespread adoption. Theoretically hardware RNG is an attractive option because it generates randomness from physical processes that are (somehow) totally unpredictable. It can overcome speculations of insufficient entropy. However as great alternative as it sounds, it requires systems and assumptions which are practically impossible
To study QRNG and compare its properties with PRNG, we have analyzed a significant batch generated via PRNG and QRNG. Read more
Though QRNG is demonstrably superior to PRNG, it still cannot be mirrored for true random number generators. However, random numbers generated using QRNG can be used for application purposes because of the uniformity with strong theoretical and practical demonstrations.
Using Quantum random number generation in a visual real world example
We have developed a platform for users to fill randomly generated colors using a quantum random color generator. The platform allows users to refill, upload and download the generated image. The supported input image format is Scalable Vector Graphics (SVG). SVG is an XML-based markup language for describing two-dimensional based vector graphics and allowing graphics manipulation.
The user can upload a svg image or can select a preexisting image. The refill action invokes the quantum random color generator and fills the SVG vectors (elements) with those random colors. The users have an option to call the quantum random color generator again or can download/share the filled SVG image. For example, figure 3 is an organized color palette of 8×8 grid graphic that transforms into a chaotic graphic when quantum generated random colors are filled. It is a demonstration of true randomness achieved using QRNG. The color set of 64 unique colors is generated (source ANU labs) and filled as shown in figure 4. Quantum Guru image filler application selects 64 unique color from the available 16.8 million quantum generated random colors.
The application is powered by ANU
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