As organizations begin planning for a post-quantum cryptography (PQC) future, one foundational element is often overlooked: the random number generator. In our guide to PQC and encryption readiness, we explain why secure cryptography depends on high-quality entropy.
In this post, we take a closer look at the difference between True Random Number Generators (TRNGs) and Pseudo-Random Number Generators (PRNGs)—and why this distinction is critical in a quantum-threatened world.
Random numbers are the bedrock of cryptographic security. They’re used in:
In a post-quantum world, cryptographic algorithms demand more entropy, more frequently, and with greater unpredictability than ever before. This is especially true when preparing for attacks from cryptographically relevant quantum computers (CRQCs), which can exploit any pattern or weakness in pseudo-random number sequences.
| Feature | PRNG (Pseudo-Random) | TRNG(True Random) |
| Source | Deterministic algorithm + seed | Physical phenomena (e.g., Brownian motion) |
| Repeatability | Yes, if seed is known | No |
| Entropy Quality | Limited | Maximum theoretical entropy |
| Performance | Fast | Slower (but more secure) |
| Cryptographic Security | Vulnerable to prediction | Unpredictable, quantum-safe |
PRNGs simulate randomness using deterministic algorithms. If the seed isn’t sufficiently random — or worse, if it’s predictable — attackers can compromise entire cryptographic systems. This problem is not hypothetical. In fact, research from Mining Your Ps and Qs (2012) exposed how entropy failures in network devices allowed attackers to factor thousands of RSA and DSA private keys due to poor PRNG implementations.
Post-quantum cryptography algorithms require significantly higher volumes of high-quality randomness than classical ones. Unfortunately, most existing entropy sources — especially PRNGs and traditional hardware RNGs — struggle to meet this demand.
As outlined in the [Physical Entropy at Scale White Paper (2025)]:
“PQC algorithms require significantly more high-quality random numbers… PRNGs and hardware RNGs struggle to meet these demands reliably and efficiently.”
This growing entropy bottleneck creates performance, cost, and security risks. And for edge devices or embedded systems — where entropy pools are often shallow or compromised — the risks are multiplied.
Unlike PRNGs, True Random Number Generators derive entropy directly from unpredictable physical processes. At Real Random, we use Brownian motion capture — the natural, chaotic movement of particles in fluid — to generate high-density, optically verifiable entropy.
This approach ensures:
As explained in the [Physical Entropy at Scale White Paper (2025)]:
“Real Random’s TRNG design captures Brownian motion using precision-engineered sensors… delivering the maximum entropy density required for robust PQC implementations.”
With quantum-era threats on the horizon, organizations must rethink where and how entropy is generated. Real Random’s approach supports:
This flexibility makes true randomness accessible in the environments where it’s needed most — without the operational burden of legacy or quantum-lab RNGs.
Let’s say you’re a global industrial firm preparing for PQC deployment across millions of IoT devices. If each endpoint relies on a PRNG seeded during boot (often with shallow entropy), the entire network could be exposed to Store Now, Decrypt Later attacks — where encrypted traffic is harvested today, stored then broken later with quantum computing power.
But with distributed TRNG deployments at the edge — or streaming entropy via EaaS — you gain forward secrecy and PQC integrity at scale.
If you haven’t addressed entropy quality in your security stack, you’re not ready for PQC. Learn more about post-quantum cryptography and why entropy is the foundation of future-proof encryption.
