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Probability Current in Quantum Mechanics In nonrelativistic 1D quantum mechanics, consider the probability of finding a particle in the interval \([x_1, x_2]\): \[ P_{x_1,x_2} = \int_{x_1}^{x_2} |\Psi(x,t)|^2\, \text{d}x. \] It is interesting to look at how this probability changes in time. Taking a time derivative, and using the Schrodinger equation \[ i \hbar \partial_t \Psi = \frac{-\hbar^2}{2m} \partial_x^2 \Psi + V \Psi \] to simplify things, we get (assuming a real potential) \[ \frac{d P_{x_1, x_2}}{dt} = \frac{\hbar}{2mi}\int_{x_1}^{x_2} \left[ (\partial_x^2 \Psi^*) \Psi - (\partial_x^2 \Psi) \Psi^* \right] \, \text{d}x \] where since \[ \left[ (\partial_x^2 \Psi^*) \Psi - (\partial_x^2 \Psi) \Psi^* \right] = \partial_x \left[ (\partial_x \Psi^*) \Psi - (\partial_x \Psi) \Psi^*\right], \] we can write this as \[ \frac{d P_{x_1, x_2}}{dt} = J(x_1, t) - J(x_2, t), \] where \[ J(x,t) = \frac{\hbar}{2mi} (\Psi^* \partial_x \Psi - \Psi \partial_x \Psi^*) \] is called the “probability current.

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I once read, though I cannot now recall where, that an appropriate purgatory for theologians would be forcing them to read all of the words ever written on the Filioque.

If that is so, then an appropriate purgatory for physicists would be forcing them to read all of the words ever written on the interpretation of quantum mechanics.

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I am teaching undergraduate quantum mechanics for the first time this semester. One thing I have discovered is that it is very easy to make mistakes when talking about quantum mechanics. Not mathematical mistakes (the math is fairly straightforward), but conceptual mistakes in the interpretation of the mathematics. I was therefore pleased to read a recent paper by Blake Stacey entitled “Misreading EPR: Variations on an Incorrect Theme.” The “EPR” in the title stands for Einstein-Podolsky-Rosen (Einstein and his two postdocs at the time), and is used as shorthand for a famous thought-experiment these three published in 1935.

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What is the equation of a straight line in the complex plane? There are many different forms, but I want to look at some of the simplest ones. If you know the slope \(m \in \mathbb{R}\) and intercept \(b \in \mathbb{R}\) of the line, you can write an equation in parametric form \[ z = x + i (m x + b) ,\] where \(x \in \mathbb{R}\) and \(z \in \mathbb{C}\).

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Two random variables \(X\) and \(Y\) can be conditionally independent given the value of a third random variable \(Z\), while remaining dependent variables not given \(Z\). I came across this idea while reading a paper called “The Wisdom of Competitive Crowds” by Lichtendahl, Grushka-Cockayne, and Pfeifer (abstract here). I’m sure it is a familiar idea to those with more of a formal background in statistics than me, but it was the first time I had seen it.

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