In fact, on the second last page of the paper, they discuss this very problem. There is a clear correlation between performance and increasing sequence lengths for the Q-K=V model. While limited to a tight n=3 sample between 512, 1024, 2048 lengths, the degradation decreases from 5.4% to 2.2% as context is increased, suggesting that it is unlikely shorter sequences are the reason K=V performs acceptably.

In addition, replicating Jameson et al. (AIAA 1981-1259) [1], is a worthwhile, more advanced follow up, great if you want to get into serious CFD development eventually.

[1] http://aero-comlab.stanford.edu/Papers/jameson.aiaa.1981-125...

When I started out, I was under the assumption that I had to understand at least the undergraduate real analysis curriculum before I could grasp quantum algorithms. In reality, for the main QC algorithms you see discussed, you don't need to understand completeness; you can just treat a Hilbert space as a finite-dimensional vector space with a complex inner product.

For those unfamiliar with said concepts from linear algebra, there is a playlist [1] often recommended here which discusses them thoroughly.

[1] https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2x...