Understanding Bell's Inequality and QM with \vec{n}

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SUMMARY

This discussion focuses on the proof that Quantum Mechanics (QM) does not satisfy Bell's inequality, referencing works by Sakuri (1985) and Townsend (2000). The key concept involves the unit vector \vec{n} defined as \(\sin \theta \cos\phi \hat{x} + \sin \theta \sin\phi \hat{y} + \cos \theta \hat{z}\), which is used to derive the eigenvectors of the operator \(\sigma \cdot \vec{n}\). The eigenvectors are explicitly given as \(|+_n \rangle\) and \(|-_n \rangle\), with the discussion emphasizing the importance of understanding the dot product of Pauli matrices with the components of \vec{n} to derive the matrix representation of \(\sigma \cdot \vec{n}\).

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Thrice
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I'm trying to understand a part of the text where they prove QM doesn't satisfy Bell's inequality. I get how he derives the inequality. Apparently it's same as Sakuri (1985) and Townsend (2000). Problem is I lose him as soon as he starts the main part. Quoting almost directly,

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Consider a qubit oriented in an arbitrary direction. Consider a unit vector [tex]\vec{n} = \sin \theta \cos\phi \hat{x} + \sin \theta \sin\phi \hat{y} + \cos \theta \hat{z}[/tex]. The eigenvectors of [tex]\sigma \cdot \vec{n}[/tex] are..

[tex]|+_n \rangle = \cos \frac{\theta }{2} |0 \rangle + e^{i\phi }\cos \frac{\theta }{2} |1 \rangle[/tex]

[tex]|-_n \rangle = \cos \frac{\theta }{2} |0 \rangle - e^{i\phi }\cos \frac{\theta }{2} |1 \rangle[/tex]

He then goes on to interpret [tex]|\langle 0|+_n\rangle |^2[/tex] as a probability & i can follow from there. I just don't get the bit I posted. The hint is to consider the x & y axes, eg to get the eigenvectors of [tex]\sigma \cdot \hat{x}[/tex] set [tex]\theta =\pi /2[/tex] & [tex]\phi =0[/tex]

Thanks.
 
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The key is to understand what [tex]\sigma \cdot \vec{n}[/tex] means. This is a dot product of matrices (Pauli matrices) times scalars (components of [tex]\vec{n}[/tex]. Let's just look at the first component. You will take the x Pauli matrix times by the x component of [tex]\vec{n}[/tex] to get [tex]\sigma_{x} n_{x}=\left(\begin{array}{cc}0 & 1 \\ 1 & 0\end{array} \right) \sin \theta \cos\phi=\left(\begin{array}{cc}0 & \sin\theta\cos\phi \\ \sin\theta\cos\phi & 0\end{array} \right)[/tex]. You do the same for the y and z components and add them all up to get your matrix, [tex]\sigma \cdot \vec{n}[/tex]. Find the eigenvectors of it and you should get what you gave from the book.
 

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