Application of true randomness

In summary, computers are not able to simulate true randomness, but use complex functions to create pseudo-randomness. Incorporating quantum randomness into technology could achieve true unpredictable randomness, but it would depend on how it is used. Simply using it to generate a seed would not make the output truly random.
  • #1
Xori
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My understanding is that today's computers aren't able to simulate true randomness, only complex functions that appear random but can be predicted if someone knows how they work.

Is there any way to incorporate the "true" randomness of QM into technology to achieve true unpredictable randomness?
 
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  • #2
It would be better if you first googled "quantum random number generator" (and at least read, for example, the wikipedia hit), and then afterward asked any further questions you might have here.
 
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  • #3
I guess that phrase is what I was looking for. Googling "random" was kind of fuitile :( Thanks
 
  • #4
I must say that the skill I gained most at college was learning what to type in the google search box. That has helped me more than anything I learned (especially since a lot of what I learned was from what I searched on google...).

Then there's also blackle.com and cantfindongoogle.com. Endless hours of searching enjoyment.

As for the topic, I'd like to explain a bit about computer randomness. The basic idea is that the computer takes some seed (some sequence of 1s and 0s) and performs some operation on it. The output is the pseudo-random sequence of 1s and 0s (easily converted to a pseudo-random number or numbers or words, etc). It's not techniquely random because the operation is a deterministic process -- give it the same seed again, and it will output the same pseudo-random sequence again. Even if the source for the seed is or seems random, the operation treats it the same as a non-random seed.

However, it is practically random. That is, given the sequence up to a certain point, there's no way to predict the next number in the sequence without knowledge of the seed and operation. If the output is long enough, it's around 50/50 1s and 0s. Each number seems independent of all the others.

There are also variations on this theme, but they're not really important for discussing how random a deterministic algorithm could be. I admit that I don't yet know how a quantum random generator would work (I have to use google after this post...), but if it used the quantum randomness alone, then it would be random. If it only used the quantum randomness to generate a seed, then we'd be just as pseudo-random as we are now.
 

Related to Application of true randomness

1. What is the importance of true randomness in scientific research?

True randomness is crucial in scientific research as it allows for the unbiased selection of samples and the elimination of any potential biases, which can skew results. It also ensures that the results are not influenced by any external factors, making them more reliable and accurate.

2. How is true randomness achieved in experiments?

True randomness is typically achieved through the use of random selection methods, such as random number generators or the use of randomization techniques. These methods ensure that each sample or participant has an equal chance of being selected, without any bias.

3. Can true randomness be simulated?

No, true randomness cannot be simulated as it is a characteristic of natural phenomena and cannot be replicated by man-made systems. However, pseudo-randomness can be generated through algorithms, but it is not truly random as it follows a predetermined pattern.

4. How does true randomness impact statistical analysis?

True randomness is essential in statistical analysis as it ensures that the collected data is representative of the population and allows for the application of statistical tests and methods. Without true randomness, statistical analysis would not be accurate or reliable.

5. What are the potential limitations of using true randomness in experiments?

One limitation of using true randomness is that it can be time-consuming and costly, especially in large-scale experiments. Additionally, there is always a small chance that the random selection may not be truly random, leading to potential errors in the results. It is important for researchers to carefully design and implement randomization methods to minimize these limitations.

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