Quantum Mechanics Generates More Precise Random Numbers

  • Scientists generate absolute random numbers using quantum mechanics. 
  • The technique involves creating digital bits with particles of light, photons.
  • It could eventually improve cryptographic and security systems. 

Scientists at NIST (National Institute of Standards and Technology) have built a new technique to generate a more precious random number using quantum mechanics. Now the unpredictability of random numbers surpasses all previously used methods, enhancing cryptographic and security systems.

What’s the problem with existing systems, you asked? Well, they don’t generate a random number in absolute sense. A number randomly produced by the machine or software formulas can be undermined by numerous factors, including predictable sources of noise. You may run statistical tests, but no test on the outcome alone can guarantee that the outcome was unpredictable.

Random numbers are used billions of times on a daily basis for encrypting private information in electronic networks. But since no one can guarantee that the conventional source is really unpredictable, it limits the strength of security systems. It’s something like flipping a coin: it seems random, but one can tell the outcome if he traces the coin path as it tumbles.

However, the new method relies on quantum source and protocol. And researchers are pretty sure that no one can predict the quantum-based outcomes. Only a quantum machine could generate the statistical correlations between outputs and measurement choices.

How Does It Work?

The new technique involves creating digital bits (0s and 1s) with particles of light, photons. It is based on the previous NIST experiment “spooky action at a distance is real” that strongly supported a key prediction of quantum mechanics. The new work, however, produces a string of much more real random bits.

More specifically, the randomness generation uses a “loophole-free” Bell test, characterized by space-like separation and detection efficiency of the measurement stations during experimental trials.

Bell Inequalities 

The next thing to understand is Bell test, in which measurements are made on an entangled system with modules placed in two separate measurement stations. A choice is made at each station (between one of two measurement types).

If the measurement data violate certain scenarios called ‘Bell inequalities’ after multiple trials, then the data are certified to have randomness under weak assumptions.

All bits are unpredictable assuming two key points –

  1. Measurement settings are independent of the devices and existing classical data about them.
  2. In each experimental trial, the measurement outputs at each station are independent of configurations at the other station.

The first on is untestable, but since one can select measurement settings independently, it often invokes in the interpretation of several laws of physics and scientific experiments. The second point can be violated only if signals can be transferred faster than the speed of light.

Reference: Nature | doi:10.1038/s41586-018-0019-0 | NIST

Generating Random Number 

The process of random number generation can be divided into two steps – long string generation, and extraction.

First, researchers used spooky action experiment to create a lengthy string of bits via a Bell test. They calculated correlations between the properties of photon pairs. The timing factor makes sure that the correlations can’t be demonstrated by traditional processes like existing scenarios or exchange of data at less than the speed of light.

Quantum mechanics was verified using statistical testings, and these information enabled scientists to quantify the randomness in the lengthly string.

Image credit: Shalm / NIST

As you can see in the experimental setup, a laser beam hits a unique crystal and gets converted into photon pairs which are entangled. Photons are further computed to generate a string of absolute random numbers. 

However, the randomness couldn’t be spread properly throughout the string. For instance, almost all bits might be 1 with none or very few being 0. To get a uniform, small string with real randomness (in which every bit has 0.5 probability of being 1 or 0), researchers carries out second step – extraction.

They designed a special software to transform the Bell test data into a smaller, uniform string.

The overall method requires 2 independent strings (containing random bits, generated via traditional methods) to pick measurement configurations for Bell tests and to feed the software, which extracts the randomness from the initial data.

Read: What’s The Largest Known Prime Number | It’s 23 Million Digits Long

They gathered a total of 5 datasets, with the best one yielding 1,024 random bits that are uniformly distributed within 10-12, i.e. 1 trillionth of 1%.

Till date, this is the best method for physically generating randomness, thereby enhancing the security and a wide range of applications.

Written by
Varun Kumar

I am a professional technology and business research analyst with more than a decade of experience in the field. My main areas of expertise include software technologies, business strategies, competitive analysis, and staying up-to-date with market trends.

I hold a Master's degree in computer science from GGSIPU University. If you'd like to learn more about my latest projects and insights, please don't hesitate to reach out to me via email at [email protected].

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