vswhere VS python-qubit-setup

But often maintainers also upload Releases with builds of their software, e.g. like here: https://github.com/microsoft/vswhere/releases

Finding where & how to use an installed VS instance (or selecting one) in automated tooling is solved by the criminally unknown, MIT licensed, MS supported, redistributable, vswhere tool: https://github.com/microsoft/vswhere

/ // This file was about 400 lines before we started adding these comments. // You might think that's way too much code to do something as simple // as finding a few library and executable paths. I agree. However, // Microsoft's own solution to this problem, called "vswhere", is a // mere EIGHT THOUSAND LINE PROGRAM, spread across 70 files, // that they posted to github unironically. // // I am not making this up: https://github.com/Microsoft/vswhere

I wrote the whole UI for controlling my quantum computing experiments in PyQT, it was super easy and very performant, my former lab is still using it to control all their quantum mechanics experiments. Before that they used LabView but even though Python is a bit more cumbersome to write interfaces in it's much more powerful for data analysis and management, so it quickly replaced LabView. The code's on Github [1,2]. Also, it's quite easy to write low-level libraries in C or C++ (or Rust, I guess) for stuff that needs to be really fast, you can e.g. easily share a memory buffer with numpy to pass data to Python, which we did for data acquisition tasks that were not performant enough in Python.

1: https://github.com/adewes/python-qubit-setup

Here's some code I wrote 12 years ago, which implements Grover's algorithm on a superconducting quantum computer [1]. This is not using a quantum algorithm compiler (which didn't exist at the time), all pulses are handcrafted. In principle it's rather simple, for each operation you want to perform you add the necessary pulse sequence, which can be either microwave pulses on each of the qubits (which drive the qubits along the x- or y-axis Bloch sphere), or voltage pulses in the qubit fluxlines (which drive the qubits along the z-axis of the Bloch sphere and/or put them in resonance with each other so they can interact). Finally, you would add readout pulses to read the qubit state at the end of the algorithm.

The pulse sequences then get loaded into arbitrary waveform generators and many other parameters are set like microwave generator frequencies. Typically you also perform a tune-up of the system to determine the correct timings and qubit frequencies (as all microwave components and the qubits themselves tend to slightly drift over time).

Today it's much more complicated as you can optimize each gate sequence individually and in conjunction to get higher fidelity, but in principle you can build up any algorithm uses these basic blocks above.

1: https://github.com/adewes/python-qubit-setup/blob/master/scr...

A long time (10 years) ago I wrote some custom C++ code to perform relatively simple arithmetic operations on very large arrays, which was considerably slower in numpy for some reason [1]. As I said that was long ago so maybe Numpy has improved by now, but back then there was a significant penalty when doing these operations directly in Python using Numpy, even though they were only a handful of things (e.g. calculate asin(x)+bcos(y) on some large array), so I didn't expect much overhead from going back and forth between F.

1: https://github.com/adewes/python-qubit-setup/blob/master/lib...