- To give a really simple simulation of a quantum mechanics system so that people who already know javascript have a chance to "get" quantum mechanics without necessarily having to understand the equations. The equations will help, though.
- To answer the question of "how do you make the simulations".

Quantum mechanics can be simulated with quite simple programs. Understanding what they do might help learn quantum mechanics, and it might help physicists understand how to simulate the maths. This article is going to start easy and get harder. If it's too easy, skip ahead.

Let us rewrite that, introducing a timestep time, $\delta t$

$$\psi_{\hbox{after timestep}} \simeq \psi_{\hbox{before}} - i \delta t \left(-\frac{\partial^2 }{\partial x^2}\psi + ax^2\psi\right).$$ You can write $$\frac{\partial^2 }{\partial x^2}\psi(x) \simeq \frac{ \psi(x+1) - 2 \psi(x) + \psi(x-1) }{\delta x^2}.$$ These two approximations give us a simple timestepping function. This is not the only (or best) timestepping function that solves the problem, but perhaps it's one of the simplest. You, the reader, should try to understand the function below. Hopefully, it will make sense with the two equations above.// This returns an empty wavefunction of length n. function wavefunction(n){ var psi = []; for(var i = 0; i < n; i++){ psi[i] = {real: 0, imag: 0} } return psi; // for example [{real:0, imag:0}, {real:0, imag:0}, ...] } // This takes the starting wavefunction, and returns the wavefunction a short time later. function timestep(psi) { // This is how many units of time we're going to try to step forward. var dt = 0.002; var n = psi.length; // This is the wavefunction we're going to return eventually. var ret = wavefunction(n); // We miss off the first and last elements because it looks at the element to the // left and right of this point. for(var i = 1; i < n-1; i++) { // This is the x that is in the equation above. var x = (i-n/2) // This is the potential at this point. var V = x*x * 0.0015; // a here = 0.0015 // We start from the original wavefunction (and later add (dt * dpsi/dt) to it). ret[i].real = psi[i].real; ret[i].imag = psi[i].imag; // This is the kinetic energy applied to psi. var KPsi = { real: psi[i].real * 2 - psi[i-1].real - psi[i+1].real, imag: psi[i].imag * 2 - psi[i-1].imag - psi[i+1].imag }; // This is the potential, applied to psi var VPsi = { real: psi[i].real * V, imag: psi[i].imag * V }; // This is the whole right hand side of the schrodinger equation. var rhsReal = KPsi.real + VPsi.real; var rhsImag = KPsi.imag + VPsi.imag; // This adds it, multiplied by dt, and multiplied by i. // The multiplication by i is what swaps the real and imaginary parts. ret[i].real += rhsImag * dt; ret[i].imag -= rhsReal * dt; } return ret; } // This returns the initial state of the simulation. function init(){ var n = 128; var psi = wavefunction(n); for(var i = 0; i < n; i++){ psi[i].real = Math.exp(-(i-20)*(i-20)/(5*5))*0.75; psi[i].imag = 0; } return psi; }The simulation above is the probability density of an electron in an $x^2$ potential. This is similar to if it was in a bowl, and was rolling backwards and forwards in that bowl.

The simulation works, but it has problems:

- The total amount of probability grows with time.
- If you time the oscillation, it's not quite right: It's actually moving slightly too slowly when it's at its fastest
- There's dispersion: The oscillation doesn't stay focussed. It should: this particular potential is specially chosen to make the oscillation stay focussed.

We're going to have to look at the original equation again.

A different way to convert it into a timestep is to do this.

$$\psi_2 = \hbox{Timestep}_T(\psi_1)$$ $$\psi_1 = \hbox{Timestep}_V(\psi_0)$$

Here, $\psi_0$ is the wavefunction before the timestep.

$\hbox{Timestep}_V$ steps forward with according to the following equation: $$i \frac{\partial \psi}{\partial t} = ax^2\psi.$$

$\hbox{Timestep}_T$ steps forward with according to the following equation: $$i \frac{\partial \psi}{\partial t} = -\frac{\partial^2}{\partial x^2}\psi$$

$\hbox{Timestep}_V$ is actualy really easy to implement, because: $$\hbox{Timestep}_V(\psi) = e^{-iax^2\delta t}\psi.$$

$\hbox{Timestep}_T$ can be implemented by noting that if $\psi = e^{ikx}$, then: $$\hbox{Timestep}_T(\psi) = e^{-ik^2\delta t}\psi.$$

And there's good news: We can implement this by fourier transforming, then multiplying by $e^{i k^2}$, and then inverse fourier transforming. The fourier transform code is here:FFT.js.

// This returns an empty wavefunction of length n. function wavefunction(n){ var psi = []; for(var i = 0; i < n; i++){ psi[i] = {real: 0, imag: 0} } return psi; // for example [{real:0, imag:0}, {real:0, imag:0}, ...] } // This takes the starting wavefunction, and returns the wavefunction a short time later. function timestep(psi) { // This is how many units of time we're going to try to step forward. var dt = 0.02; psi = timestepV(psi, dt); psi = timestepT(psi, dt); return psi; } function timestepV(psi, dt) { var n = psi.length; for(var i = 0; i < n; i++) { // This is the x that is in the equation above. var x = (i-n/2) // This is the potential at this point. var V = x*x * 0.0015; var theta = dt * V; var c = Math.cos(theta); var s = Math.sin(theta); var re = psi[i].real * c - psi[i].imag * s; var im = psi[i].imag * c + psi[i].real * s; psi[i].real = re; psi[i].imag = im; } return psi; } function timestepT(psi, dt){ psi = FFT.fft(psi); var n = psi.length; for(var i = 1; i < n/2; i++) { var k = 2 * 3.1415927 * i / n; var theta = k * k * dt; var c = Math.cos(-theta); var s = Math.sin(-theta); var re = psi[i].real * c - psi[i].imag * s; var im = psi[i].imag * c + psi[i].real * s; psi[i].real = re; psi[i].imag = im; var j = n - i; re = psi[j].real * c - psi[j].imag * s; im = psi[j].imag * c + psi[j].real * s; psi[j].real = re; psi[j].imag = im; } return FFT.fft(psi); } // This returns the initial state of the simulation. function init(){ var n = 128; var psi = wavefunction(n); for(var i = 0; i < n; i++){ psi[i].real = Math.exp(-(i-20)*(i-20)/(5*5))*0.75; psi[i].imag = 0; } return psi; }This runs much better. It's faster, mainly because the timestep was longer. But the timestep can be longer because the timestep is more accurate. Likewise, you can get away with a coarser grid, and still get the same accuracy.

The next page looks at 2-dimensional simulations:

## Atomistic Simulation of MetalsThis presents an interactive simulation of atoms making up a nanoscopic particle of metal. |

## How to simulate fractal ice crystal growth in PythonThis presents python code to draw snowflakes, simulating a diffusion process with Fourier transforms. |

## Quantum Mechanics for programmersA quick demo showing how to make simulations of simple quantum mechanics systems in javascript. |

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