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QRNG Guide

Learn QRNG

A practical guide to quantum random number generation, entropy, and trustworthy random number sources.

Fundamentals

What is a quantum random number generator?

A quantum random number generator, or QRNG, is a hardware system that generates random numbers from a quantum physical process. Unlike software pseudo-random number generators, a QRNG uses physical entropy as its source of unpredictability.

Figure 1 — PRNG vs TRNG vs QRNGA PRNG expands a seed with an algorithm, a TRNG measures a physical process, and a QRNG measures a physical process whose unpredictability comes from quantum behaviour.
Figure 2 — From physical process to cryptographic random bitsRaw measurements are never used directly: they are validated, conditioned into uniform bits, and continuously health-checked before any key material is derived.

Tap either diagram to enlarge it.

See how this works inside silicon
Try it

Guess the next number

Two machines hand out numbers, one at a time. One follows a recipe. The other flips a quantum coin. Watch which one you could guess ahead of time.

Machine A — follows a recipe

Press play to start…

After a few numbers you can see the loop. Once you know the recipe, you know every number it will ever give — including the ones it has not given yet.

Guessed correctly: watching…

Machine B — flips a quantum coin

Press play to start…

There is no recipe to learn. Each number comes from a physical event that has no answer until it happens, so the best anyone can do is a coin-flip guess.

Guessed correctly: 0/0

Why this matters

A secret key is just a long string of these numbers. If the numbers came from Machine A, anyone who works out the recipe can rebuild the key. If they came from Machine B, there is no recipe to work out. That is the whole point of a quantum random number generator.

Machine B in this demo uses your browser's secure random numbers. A real QRNG replaces that with a measured physical quantum process.

Why It Matters

Why randomness matters for security

Encryption keys, session tokens, authentication challenges, secure communications, and post-quantum cryptography all depend on unpredictable random numbers. If the entropy source is weak, the security system can fail even if the cryptographic algorithm is strong.

Keys and tokens

Cryptographic keys, nonces, and session tokens are only as strong as the randomness used to generate them.

Secure communications

Protocols that establish confidentiality, authenticity, and forward secrecy rely on unpredictable values at every handshake.

Post-quantum readiness

Modern and post-quantum schemes still depend on high-quality entropy to seed key generation and protect long-lived secrets.

Inside the Silicon

How can silicon generate quantum entropy?

Inside silicon, tiny charge events occur unpredictably. A CMOS-native QRNG measures physical fluctuations associated with these charge events, checks that the signal behaves like a valid entropy source, removes classical artifacts, and conditions the measured entropy into cryptographic random bits.

Figure 3 — Quantum entropy inside standard siliconThe unpredictability comes from charge behaviour inside the silicon itself; the surrounding circuit measures it, discards classical artifacts, and conditions what remains.

Figure 3 is described in full in the alt text.

Simplified pipeline
  1. Step 1Physical entropy source
    A quantum process inside silicon that produces unpredictable signals.
  2. Step 2Measurement
    The signal is read by on-chip electronics.
  3. Step 3Validation checks
    Continuous checks confirm the source behaves as a valid entropy source.
  4. Step 4Conditioning
    Classical noise and bias are removed so the output is uniform.
  5. Step 5Random bits
    High-quality random bits are produced for cryptographic use.
  6. Step 6Security applications
    Keys, nonces, tokens, secure communications, and more.

This is a public, educational description. It does not describe internal circuit details, device parameters, or extraction thresholds.

A Complementary Path

CMOS-native, not optics-dependent

Many QRNGs are explained through optical or photonic examples. QRNG.io also explains CMOS-native QRNG: an approach focused on standard silicon electronics, compact hardware, and integration into real systems.

Photonic QRNG remains an important and proven approach. CMOS-native QRNG is a complementary path focused on electronics-native integration.

Electronics-native

Built around standard silicon electronics, so the entropy source lives close to where the random numbers are consumed.

Compact hardware

Suited to evaluation boards, embedded modules, and integration into existing hardware designs.

Integration-oriented

Designed to fit alongside the cryptographic stack, not as a separate optical instrument in a different rack.

QRNG Approaches

Different approaches to quantum entropy

Photonic QRNG is an important and proven approach. CMOS-native QRNG is a complementary path focused on compact hardware, standard silicon electronics, and integration into real systems.

Figure 4 — Photonic and CMOS-native QRNGBoth approaches end at the same place — validated random bits. They differ in the physics they measure and in how easily the source fits inside ordinary electronics.
ApproachEntropy sourceTypical implementationStrengthsEngineering considerationsBest-fit applications
Photonic QRNGOptical quantum effects: photon detection, optical shot noise, laser phase noise.Often uses optical components, packaging, alignment, or photonic integration.Proven, well-studied, widely deployed in QRNG products today.Optical packaging, alignment tolerances, and photonic integration complexity.Datacenter modules, scientific use, established cryptographic infrastructure.
Electron-based QRNG
Future-facing
Quantum electronic effects used as the underlying entropy source.Electronics-native, designed for semiconductor and PCB integration.Compact form factor, embedded suitability, scalable manufacturing potential.An emerging direction — characterization, certification, and ecosystem maturation are ongoing.Embedded systems, OEM devices, chip-scale security, scalable deployment.

"The future of QRNG is not only about generating quantum entropy. It is about making quantum entropy practical, observable, integrable, and deployable."

Trust

What makes a QRNG trustworthy?

A trustworthy QRNG is more than a random-looking output. The physical entropy source must be characterized, monitored, and conditioned. Statistical tests on the output alone are not enough — they can detect some failures, but they cannot prove that the underlying physics is sound.

QRNG trust stack: physical entropy source, characterization, classical noise rejection, health tests, conservative entropy estimation, conditioning, and continuous monitoring.
The layers that make a QRNG trustworthy

Physical entropy source

The randomness must come from a real physical process, not from software.

Characterization

The source is studied so that its behavior and limits are understood.

Classical noise rejection

Predictable classical contributions are filtered out so only quantum-derived entropy is used.

Health tests

The system continuously checks that the source is still behaving as expected.

Conservative entropy estimation

Estimates of how much entropy is produced should err on the safe side.

Conditioning

Raw entropy is processed into uniform random bits suitable for cryptographic use.

Continuous monitoring

Operation is monitored over time, not only validated once at the factory.

In short: a QRNG should be designed so that randomness is grounded in physics, observable in operation, and conservatively processed before being used by cryptographic systems.

Keep reading

Understand entropy and the standards

Continue with the source, its conservative entropy estimate, and the NIST framework that connects the layers.

Or look up a term in the QRNG glossary
Free engineering toolLocal NIST SP 800-90B entropy assessment kitRun min-entropy assessments on your own datasets, on your own computer. Nothing is uploaded to QRNG.io.

Part of the QRNG.io quantum randomness knowledge hub.

QRNG FAQ

Frequently asked questions