As we explore the fascinating world of quantum mechanics—the science that powers quantum technologies—let’s start with one of the simplest, yet most powerful applications: true randomness.

At first glance, a number like 905230 might look utterly meaningless. What could possibly be the use of a number that has no pattern, no logic, and no apparent purpose?
Surprisingly, random numbers are everywhere. They play crucial roles in statistics, cryptography, gaming, and even scientific simulations.

Let’s start with something simple: surveys.
Imagine you want to find out how many people aged 20–50 experience side effects from a vaccine. You could ask every person on Earth in that age group—but that’s impossible. Instead, you pick a random sample of people and use that data to draw conclusions.

This idea of randomness appears even in everyday life. Think about how your grandma checks if the rice is cooked: she picks a few grains from the pot, tastes them, and decides. If her sample was always from the top layer, she might be misled—but by taking a random handful, she gets a fair sense of the whole. So taking a true random sample is the key.

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Randomness also makes lotteries fair. Every ticket must have an equal chance to win. If the draw isn’t truly random, one group gains an unfair advantage. And beyond these simple examples, randomness is absolutely critical in cryptography, where it protects digital information, and in simulations, where it ensures unbiased results.

So that raises the question:
How do we actually pick a random number?

Let’s start with math. If you’ve written code before, you’ve probably used a “Random Number Generator (RNG).” Maybe in Python you’ve called something like random() and trusted it to give you a random value.

But here’s the secret: most of these aren’t truly random at all.

They rely on an initial “seed” value—say 327—that’s fed into an algorithm. The algorithm performs a series of operations: multiply, add, subtract, maybe raise it to a power, and produces another number. If you use the same seed, you’ll always get the same result.

You can try this yourself:

Run it twice—you’ll get the same “random” number each time.

To make things seem more random, programs often use things like the system time, mouse movement, or the electrical noise in your computer as the seed. But these are still pseudo-random numbers—numbers that only appear random, because the underlying process is deterministic.

Even AIs do this. If you ask many AI models to “pick a random number between 1 and 50,” a surprising number of them choose 27. Please check this interesting post on Medium for details.

So we ask again:
Where do we find true randomness?

If math can’t do it, maybe physics can.
The simplest physical random generator is something ancient—a dice. Throw it, and the result seems random enough. Casinos have made fortunes on this principle, using dice, roulette wheels, and card shuffles. But as anyone who’s ever been lost money in a casino knows, physical randomness can be cheated.

You can also use more sophisticated classical systems. For example, imagine an extremely sensitive wind-speed detector placed on a hill. The wind fluctuates constantly, and the last few digits of its reading, say, 14.4850349674200498650976, keep changing unpredictably. You could use those digits to generate random numbers.

Yet, even in these cases, patterns lurk beneath. The atmosphere has correlations, and the sensor has biases. These are chaotic systems, but still fundamentally deterministic. If you knew all the initial conditions, you could, in theory, predict the outcome. So even these are not truly random.

In the last episode we ended with a question: is nature truly random, or is it simply our ignorance? As you might have guessed, evidence so far suggests that nature really is random. The proof of this was so remarkable and significant that it earned a Nobel Prize in physics just a few years ago—but that’s a topic for another day.

Consider a single photon—one tiny particle of light—passing through a 50/50 beam splitter just like in the previous episode. Quantum mechanics tells us that the photon has exactly a 50% chance of being transmitted and a 50% chance of being reflected. Before we measure it, it’s in a superposition of both states. Only when we observe it does it “choose” one.

And the result is fundamentally random.
Not because we don’t know enough, not because our measurement tools are crude, but because the laws of physics themselves say it cannot be predicted.

So, we can define a simple rule:

Send millions of photons one by one, and you get a random binary string like:

Convert that binary string into a number (read here how to convert a binary to Number), and voilà—you have a truly random number, guaranteed by quantum physics.

This is the core idea behind Quantum Random Number Generators (QRNGs). They use quantum processes—like photon measurements, electron spins, or even quantum vacuum fluctuations—to produce randomness that’s fundamentally unpredictable.

Companies and research labs are building compact QRNG chips that can fit into computers and smartphones, producing random numbers on the fly. Some are even available through cloud services, letting anyone access quantum-generated randomness over the internet. So far, we’ve seen data storage and computing offered as cloud services, but now even random number generation is available in the cloud. This is sometimes referred to as “entropy as a service.”

It’s one of the simplest yet most profound demonstrations of quantum mechanics in action—a reminder that at its very core, the universe is not entirely deterministic. Sometimes, even nature likes to roll the dice.

Post Script:

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