# Are entanglement correlations truly random?

• I
Gold Member
I have to say that I don't follow what point you are trying to make so I'll leave it there.
To put it simply: I am asking if the sources of a correlation are 100% random.

You are more than welcome to participate, which I would like, however, I respect any decision you make thereabout.

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Gold Member
Similarly, if you pick only the "1" bits out of each sample and match them up with each other, you are making a meaningless comparison.

How you meant it doesn't change the fact that it's meaningless. See above.
What I mean is that the experiment(al setup) is the "cherrypicker" in this case, in my consideration.

To put it simply: I am asking if the sources of a correlation are 100% random.

You are more than welcome to participate, which I would like, however, I respect any decision you make thereabout.
I assume you mean 'deviate from randomness. This big topic is part of standard statistical theory and the Wiki articles are a good introduction.
https://en.wikipedia.org/wiki/Statistical_randomness
and this is useful and mentions higher concepts like spectral decompositions and Hadamard transformations.
https://en.wikipedia.org/wiki/Randomness_tests

This is not part of quantum theory. Correlations in QT play a different but very important role.

PeterDonis
Mentor
2020 Award
By random I mean that, in the binary case, the limit of l to ∞ of the probability of getting a fragment of n identical bits in a random string of length l is ##(\frac{1}{2})^{n-1}##.

Where are you getting this definition of "random" from?

I am asking if the sources of a correlation are 100% random.

Obviously it depends on the sources.

What I mean is that the experiment(al setup) is the "cherrypicker" in this case, in my consideration.

The experimental setup certainly tells you what comparison between two bit strings is meaningful. But you seemed to be saying that any such comparison was meaningful, because you were talking about rearranging how the two bit strings are compared with each other (by, for example, shifting one bit string relative to the other and then comparing). If you do that, you aren't doing what the experimental setup tells you to do; you're doing something different, and meaningless. That's what I meant by cherry-picking the data.

PeterDonis
Mentor
2020 Award
This is probably an example of my limited knowledge of English.

I think the entire topic of this thread might be an artifact of your limited knowledge of English. That's why I keep asking what you mean by the word "random"; I don't think you mean what that word usually means in English.

Perhaps it would help to ask the question a different way: why do you care whether "the sources of a correlation are 100% random"? What would it tell you if the answer was yes? What would it tell you if the answer was no?

Gold Member
Where are you getting this definition of "random" from?
From the data of my computer code The experimental setup certainly tells you what comparison between two bit strings is meaningful. But you seemed to be saying that any such comparison was meaningful, because you were talking about rearranging how the two bit strings are compared with each other (by, for example, shifting one bit string relative to the other and then comparing). If you do that, you aren't doing what the experimental setup tells you to do; you're doing something different, and meaningless. That's what I meant by cherry-picking the data.
Well, I can reassure you that was not my angle of approach. The cherry-picking part I introduced to illustrate how improbable it is to get a correlation out of pure randomness.
I think the entire topic of this thread might be an artifact of your limited knowledge of English. That's why I keep asking what you mean by the word "random"; I don't think you mean what that word usually means in English.
That's not entirely fair - I think it is a matter of starting point.

PeterDonis
Mentor
2020 Award
how improbable it is to get a correlation out of pure randomness.

But you still haven't really explained what you mean by this.

• Zafa Pi
Gold Member
But you still haven't really explained what you mean by this.
Well, I am afraid I can't do better than this currently. I will ponder some more.

• Zafa Pi and Mentz114
Well, I am afraid I can't do better than this currently. I will ponder some more.
You should stsrt by finding out the customary meanings of randomness and also how to calculate a correlation.

• Zafa Pi
Suppose we have two truly random sources A and B that generate bits ('0' or '1') synchronously. If we measure the correlation between the respective bits generated, we find a random, ie no, correlation.

Now suppose A and B are two detectors that register polarization-entangled photons passing respective polarization filters. We can define bits as 'detection'='1' and 'no detection'='0'. A and B individually yield random results. However, there is in almost every case a non-zero correlation, depending on the angle of the filters.

So my question would then be: since the detections of the entangled particles often exhibit a different correlation than truly random sources, are the detections purely random in case of entanglement? (or do they only seem random?)
A major problem in getting your question answered is that your terminology is sloppy, in fact truly sloppy.
Define "truly random"!
Where are you getting this definition of "random" from?
You failed to make a definition. The terms "random" and "truly random" are neither used nor defined in probability texts. And after reading more of your posts it is not clear to me what you mean.

Let me give a simple concrete QM example:
Given an entangled pair from state √½(|00⟩ + |11⟩), we let A measure one of the pair at angle 0º, i.e. with measurement operator/observable ##Z =\begin{pmatrix}1&0\\0&-1 \end {pmatrix}##.
We let B measure the other at 30°, i.e. with observable ##½Z + √¾X =\begin{pmatrix}½&√¾\\√¾&-½\end{pmatrix}##.

The joint probability density of (A,B) is (1,1) with prob ⅜, (1,-1) with prob ⅛, (-1,1) with prob ⅛, (-1,-1) with prob ⅜. (1 & -1 are eigenvalues of the observables)
We see A and B agree with prob = ¾ = cos²30º, as usual.
The correlation coefficient is ½.
The marginal density of A is 1 with prob ½, -1 with prob ½. Same for B. A and B are not independent.

All of this is justified by repeated trials in the lab.

Gold Member
I thought I remembered the basics, but this morning my meds demand my brain, so I would have to look it up.

Gold Member
You failed to make a definition. The terms "random" and "truly random" are neither used nor defined in probability texts.
If the term is not defined in scientific literature, then why are you asking me, a layman, to define it? By the way, I gave one:
By random I mean that, in the binary case, the limit of l to ∞ of the probability of getting a fragment of n identical bits in a random string of length l is ##(\frac{1}{2})^{n-1}##. There are probably standard deviations one could run on this. My own knowledge of mathematics is too limited for that.
Anyway, I will think about what I mean by 'truly' random.

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PeterDonis
Mentor
2020 Award
If the term is not defined in scientific literature, then why are you asking me, a layman, to define it?

Because you used it. We need to know what you meant by it.

• Zafa Pi
By random I mean that, in the binary case, the limit of l to ∞ of the probability of getting a fragment of n identical bits in a random string of length l is (12)n−1(12)n−1(\frac{1}{2})^{n-1}. There are probably standard deviations one could run on this. My own knowledge of mathematics is too limited for that.
I don't know what you mean by fragment. Do you have a link?
If a1,a2, ... ,al with l=20 is a binary sequence, what is a fragment of 10 bits? Is it a subset of size 10? Is it a contiguous subset like a7,a8, ... ,a16? Or what?

By random I mean that, in the binary case, the limit of l to ∞ of the probability of getting a fragment of n identical bits in a random string of length l is (12)n−1(12)n−1(\frac{1}{2})^{n-1}. There are probably standard deviations one could run on this. My own knowledge of mathematics is too limited for that.
I now think you were trying to define a binary normal sequence, but failed.

"A sequence of bits is random if there exists no Program shorter than it which can produce the same sequence." ~ Kolmogorov
So obviously it is impossible to exhibit a Kolmogorov random sequence.

Neither normality or K-random imply one another. But all of this should be in the Probability section of PF. And none of this is relevant to QM.

Gold Member
Suppose I walk down the street, and each time I look to my right, a red car is passing. If I don't look, I don't know which color the passing cars have.

So the correlation between me looking and a red car passing is 100%.

So I assume the moments I look are random (A) and the cars passing have FAPP random colors (B).

So, in this case, with the correlation manifesting, are (A) and (B) "truly" random?

Since we generally do not see correlations like this always and everywhere, it should be, however not impossible, improbable to see this. So, I cannot determine whether there is a red car convention in town or not, since I don't know the counterfactual measurements (looking). So, would a string of red cars passing me still be random? After all it would require a red car convention. And if there is NO red car convention, would the string of cars passing still be truly random if the correlation with my looking direction would be 100% red cars? (Or, for that matter, would my peeking be random?)

The problem I see, is that if (A) and (B) are truly random, the measurements should be typical for what is reality. For example, based on my perceptions, I might say that in this street probably only red cars are allowed, while the counterfactual data is in contradiction with that.

You could also see it the other way round: I see typical cars passing, while when I'm not looking only red cars pass which I wouldn't know of. My assessment of the data might lead me to faulty conclusions.

So I think "randomness" is required to accurately assess reality.

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stevendaryl
Staff Emeritus
Suppose I walk down the street, and each time I look to my right, a red car is passing. If I don't look, I don't know which color the passing cars have.

So the correlation between me looking and a red car passing is 100%.

So I assume the moments I look are random (A) and the cars passing have FAPP random colors (B).

So, in this case, with the correlation manifesting, are (A) and (B) "truly" random?

There was a different thread in the "Set Theory, Logic, Probability and Statistics" forum on this topic. Random is relative to a model or theory. You can't know whether something is "truly" random unless you know what theory is correct. Which, of course, you can never know.

According to QM, the results of certain types of measurements are random, in the sense that QM doesn't propose any means of determining the values ahead of time. According to a different theory (maybe Bohmian mechanics), the results may not be random.

The facts you describe above is consistent with multiple explanations:
1. All the cars are red.
2. There are cars of other colors, but for whatever reason, you only have an impulse to look at a car when the car is red.
3. There are cars of other colors, but just by coincidence, you happened to look at the moments a red car is passing.
4. Etc.

Stephen Tashi
I flip a coin and it lands heads. Does it still make sense to describe the probability of a heads for that flip as p=.5, ten minutes after the fact? Does probability even exist for events in the past?

Both of these thoughts goes to a time relationship of randomness... does the standard treatment not take time into account?

The standard mathematical treatment of probability (which uses measure theory) says nothing about events actually happening. It doesn't have any axioms that say you can take random samples. It does not have a model of time as that notion is used in physics. So the standard mathematical theory does not deal with questions about a probability "before" or "after" some time or a probability that changes with the "actual" occurance of an event.

The standard techniques for applying probability theory to real life problems do assume that it is possible to take random samples and that events actually happen (or don't happen). In applications of probability theory the indexing set used in the abstract definition of "stochastic process" is often interpreted to be time in the physical sense.

The distinction between mathematical probability theory and interpretations that people make when applying it is blurred by the fact that only the most advanced texts on mathematical probability theory confine themselves to discussing that theory. The typical textbook on probability theory tries to be helpful by teaching both probability theory and its useful applications. For example, the "conditional probability" P(A|B) has a very abstract mathematical definition. However, typical textbooks present P(A}B) by interpreting it to mean "The probability of event A given that the event B has (actually) happened".

In mathematical probability theory, a specific sequence of numbers can be assigned a probability and it can be a member of a "sample space" on which a probability measure is defined. But there is no definition for a particular sequence of numbers being "random" or "not random". In mathematical probability theory, there is a definition for two random variables to be correlated However there is no definition for two specific sequences of numbers to be correlated. In this thread, there is the usual confusion involving numerical calculations done on specific sets of numbers to estimate mathematical correlation versus the mathematical definition of correlation.

Attempts have been made to create mathematical notions of randomness for specific sequences of numbers. These attempts are not "standard" mathematical probability theory.

When discussing physics, people are making their own interpretations of mathematical probability theory.

• DrChinese