pygfx VS python-qubit-setup

https://github.com/kushalkolar/fastplotlib

Alternatively, try pygfx for ThreeJS graphics in Python leveraging wgpu. It works great in Notebooks through notebook-rfb. https://github.com/pygfx/pygfx

If you're adventurous, figure out how to make pygfx work with webgpu via wasm

FYI you can already use webgpu directly in python, see https://github.com/pygfx/wgpu-py for webgpu wrappers and https://github.com/pygfx/pygfx for a more high level graphics library

Rather than using matplotlib, you could try either pygfx (https://github.com/pygfx/pygfx) or fastplotlib (https://github.com/kushalkolar/fastplotlib) to make higher performance graphics using Python.

However, it won't solve your problem of Python not being fast enough doing the calculations.

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...