Quantum Benchmarking (QB): Analysis of Matrices

This work is supplemental material for the white paper ``Feasibility of accelerating incompressible computational fluid dynamics simulations with error-corrected quantum computers''. This includes Python analysis of $F$-matrices (collision matrices) of LBM and the Carleman linearized $A$-matrix.

In example_A_norm.ipynb we establish an upper bound on the spectral norm of the $A$-matrix. This should be run first to save various matrices in .npz format.

In example_A_norm.py the actual spectral norm for a very small $A$-matrix is calculated. You can view the results in example_A_norm.results.txt instead of actually running the script. This was split out into a separate script because it tends to crash a default-sized Jupyter kernel.