Random Number Generation
Random Number Generation (AI generated image)

Random numbers (entropy) are important for everything from scientific modeling, to making video games interesting, to picking lottery winners. They are most important for cryptographic security — the security of your social media accounts, smartphone, the electric grid, school, work, and the government.

David Johnston’s textbook of random number generation tells us that “There can be no cryptographic security without secure, unpredictable random numbers … Unfortunately, in cryptography, random number generation has proved difficult to get right and there are many examples of cryptographic systems undermined by poor quality random number generation.”

So, where do we get those random numbers from?

There are 4 sources — software, classical hardware, and quantum hardware in two generations.

  1. Pseudo random number generation in software is fast and easy but unfortunately offers poor quality — the word pseudo in the name gives that away!
  2. Classical hardware — a hardware chip - is better quality but has the baggage of supply chain management, Size, Weight, Power, and Certification (SWaPC) concerns. It offers fair quality but typically has to be designed into boards.
  3. First generation quantum hardware used quantum rather than classical processes for random number generation a decade or more ago but relied on simpler, older statistical tests for provability. It offered better quality than (1) or (2). Its uptake was limited in a world defined by scaling software, where most platforms already contained (2).
  4. Second generation quantum uses an actual quantum computer to generate and also prove quality of random numbers, which are then distributed in software. They combine ease of use with the best quality in the industry. They can be added into existing software systems and compliment (2), without breaking certification or adding performance overhead.

Want to know more?

  • David Johnston’s textbook covers (1) or (2) well.
  • The challenges with legacy QRNG1 for (3) are discussed in a paper from the University of Kent.
  • The innovation in QRNG2 for (4) are discussed in a peer reviewed paper in the Quantum Journal for Quantum Science.
  • Random numbers are a foundational technology that underpins classical and NIST’s post quantum cryptography (PQC) algorithms.



Simon Hartley - Quantum, Cybersecurity, Mobility

Seasoned software executive with deep experience in scaling emerging and disruptive technologies.