Ciro Santilli @cirosantilli 37

Reaches 2 mK^[ref]. youtu.be/upw9nkjawdy?t=487 from Video "Building a quantum computer with superconducting qubits by Daniel Sank (2019)" mentions that 15 mK are widely available.

Used for example in some times of quantum computers, notably superconducting quantum computers. As mentioned at: youtu.be/uPw9nkJAwDY?t=487, in that case we need to go so low to reduce thermal noise.

Polynomial (possibly a multivariate polynomial) with integer coefficients.

Sometimes systems of Diophantine equations are considered.

Problems generally involve finding integer solutions to the equations, notably determining if any solution exists, and if infinitely solutions exist.

The general problem is known to be undecidable: Hilbert's tenth problem.

The Pythagorean triples, and its generalization Fermat's last theorem, are the quintessential examples.

TODO: in high level terms, why is QED more general than just solving the Dirac equation, and therefore explaining quantum electrodynamics experiments?

Also, is it just a bunch of differential equation (like the Dirac equation itself), or does it have some other more complicated mathematical formulation, as seems to be the case? Why do we need something more complicated than

The main high level insight seems to be that The Dirac equation does not work for more than one electron.

Bibliography:

where:

Remember that $\psi$ is a 4-vetor, gamma matrices are 4x4 matrices, so the whole thing comes down to a dot product of two 4-vectors, with a modified $\psi$ by matrix multiplication/derivatives, and the result is a scalar, as expected for a Lagrangian.

Like any other Lagrangian, you can then recover the Dirac equation, which is the corresponding equations of motion, by applying the Euler-Lagrange equation to the Lagrangian.

Lists:

Personal knowledge base software recommendation threads:

TODO look into those more:

Major downsides that most of those personal knowledge databases have:

Closed source dump:

Their txt formats are so crap!

E.g. for;a good one is:

The ( +)? is for the endlessly many quoted letters they have, which use four leading spaces per line as a quote marker.

God, I love this company so much.

Conda is like pip, except that it also manages shared library dependencies, including providing prebuilts.

This has made Conda very popular in the deep learning community around 2020, where using Python frontends like PyTorch to configure faster precompiled backends was extremely common.

It also means that it is a full package manager and extremely overbloated and blows up all the time. People should just use Docker instead for that kind of stuff: www.reddit.com/r/learnmachinelearning/comments/kd88p8/comment/keco07k/

You also have to buy a license to use their repos if you are part of a large-enough organization: stackoverflow.com/questions/74762863/are-conda-miniconda-and-anaconda-free-to-use-and-open-source