Quantum entanglement is often called spooky/surprising/unintuitive, but they key question is to understand why.
To understand that, you have to understand why it is fundamentally impossible for the entangled particle pair be in a predefined state according to experiments done e.g. where one is deterministically yes and the other deterministically down.
In other words, why local hidden-variable theory is not valid.
How to generate entangled particles:
- particle decay, notably pair production
- for photons, notably: spontaneous parametric down-conversion, e.g.: www.youtube.com/watch?v=tn1sEaw1K2k "Shanni Prutchi Construction of an Entangled Photon Source" by HACKADAY (2015). Estimatd price: 5000 USD.
Bell's Theorem: The Quantum Venn Diagram Paradox by minutephysics (2017)
Source. Contains the clearest Bell test experiment description seen so far.
It clearly describes the photon-based 22.5, 45 degree/85%/15% probability photon polarization experiment and its result conceptually.
It does not mention spontaneous parametric down-conversion but that's what they likely hint at.
Done in Collaboration with 3Blue1Brown.
Question asking further clarification on why the 100/85/50 thing is surprising: physics.stackexchange.com/questions/357039/why-is-the-quantum-venn-diagram-paradox-considered-a-paradox/597982#597982
Bell's Inequality I by ViaScience (2014)
Source. Quantum Entanglement & Spooky Action at a Distance by Veritasium (2015)
Source. Gives a clear explanation of a thought Bell test experiments with electron spin of electron pairs from photon decay with three 120-degree separated slits. The downside is that he does not clearly describe an experimental setup, it is quite generic.Quantum Mechanics: Animation explaining quantum physics by Physics Videos by Eugene Khutoryansky (2013)
Source. Usual Eugene, good animations, and not too precise explanations :-) youtu.be/iVpXrbZ4bnU?t=922 describes a conceptual spin entangled electron-positron pair production Stern-Gerlach experiment as a Bell test experiments. The 85% is mentioned, but not explained at all.Quantum Entanglement: Spooky Action at a Distance by Don Lincoln (2020)
Source. This only has two merits compared to Video 3. "Quantum Entanglement & Spooky Action at a Distance by Veritasium (2015)": it mentions the Aspect et al. (1982) Bell test experiment, and it shows the continuous curve similar to en.wikipedia.org/wiki/File:Bell.svg. But it just does not clearly explain the bell test.
Quantum Entanglement Lab by Scientific American (2013)
Source. The hosts interview Professor Enrique Galvez of Colgate University who shows briefly the optical table setup without great details, and then moves to a whiteboard explanation. Treats the audience as stupid, doesn't say the keywords spontaneous parametric down-conversion and Bell's theorem which they clearly allude to. You can even them showing a two second footage of the professor explaining the rotation experiments and the data for it, but that's all you get.The City of London is an obscene thing. Its existence goes against the will of the greater part of society. All it takes is one glance to see how it is but a bunch of corruption. See e.g.: The Spiders' Web: Britain's Second Empire.
- Compiler toolchains generate and read ELF files.Sane compilers should use a separate standalone library to do the dirty work. E.g., Binutils uses BFD (in-tree and canonical source).
- Operating systems read and run ELF files.Kernels cannot link to a library nor use the C stlib, so they are more likely to implement it themselves.This is the case of the Linux kernel 4.2 which implements it in th file
fs/binfmt_elf.c.
- Specialized libraries. Examples:
Number 9 drawn with mouse on GIMP by Ciro Santilli (2023)
Note that:
- the images must be drawn with white on black. If you use black on white, it the accuracy becomes terrible. This is a good very example of brittleness in AI systems!
- images must be converted to 32x32 for
lenet.onnx, as that is what training was done on. The training step converted the 28x28 images to 32x32 as the first thing it does before training even starts
We can try the code adapted from thenewstack.io/tutorial-using-a-pre-trained-onnx-model-for-inferencing/ at lenet/infer.py:and it works pretty well! The program outputs:as desired.
cd lenet
cp ~/git/LeNet-5/lenet.onnx .
wget -O 9.png https://raw.githubusercontent.com/cirosantilli/media/master/Digit_9_hand_drawn_by_Ciro_Santilli_on_GIMP_with_mouse_white_on_black.png
./infer.py 9.png9We can also try with images directly from Extract MNIST images.and the accuracy is great as expected.
infer_mnist.py lenet.onnx mnist_png/out/testing/1/*.pngFirst published by Fourier in 1807 to solve the heat equation.
By cranks:
- www.thehighestofthemountains.com/ has some diagrams. It is unclear how they were obtained, except that they were made over the course of 5 years by a "Space Shuttle Engineer", classic crank appeal to authority. The author belives that brain function is evidence of intelligent design.
Allows us to draw with JavaScript pixel by pixel! Great way to create computational physics demos!
Here is an animation demo with some useful controls:
HTML snippet:
new class extends OurbigbookCanvasDemo {
init() {
super.init('hello');
this.pixel_size_input = this.addInputAfterEnable(
'Pixel size',
{
'min': 1,
'type': 'number',
'value': 1,
}
);
}
draw() {
var pixel_size = parseInt(this.pixel_size_input.value);
for (var x = 0; x < this.width; x += pixel_size) {
for (var y = 0; y < this.height; y += pixel_size) {
var b = ((1.0 + Math.sin(this.time * Math.PI / 16)) / 2.0);
this.ctx.fillStyle =
'rgba(' +
(x / this.width) * 255 + ',' +
(y / this.height) * 255 + ',' +
b * 255 +
',255)'
;
this.ctx.fillRect(x, y, pixel_size, pixel_size);
}
}
}
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