1: https://github.com/adewes/python-qubit-setup/blob/master/lib...
Op is better off recompiling numpy to target his specific hardware thank trying to speed it up using rust. This would be to try different underlying math implementations that are linked with numpy ( blas, mkl, eigen). Each of these haha several internal simulation frameworks to optimize not just the actual instructions but memory layout for various math kernels.
* Numba: https://numba.pydata.org/
* JAX: https://jax.readthedocs.io/en/latest/notebooks/quickstart.ht...
E.g. I’d expect for small/medium then re-writing as a fold over the input arrays would be fastest because you’d only traverse once & everything would fit in cache.
However if the 3 arrays combined size is larger than L1 cache, I’d be strongly tempted to bet on the naïve 3 operation approach to be faster. You’d save so much time on cache line flush / reload activities.
But then if 2 arrays are larger than L3 i’d expect going back to the fold / single traversal to win again because the iterating 2 arrays at a time behaviour is no longer any different to iterating 3 at a time.
Untested hypothesis.
I'd recommend using Numba first, though.