Performance improvement in `SimHashIterator`

The current `SimHashIterator` works by finding the most significant active bit, and then calculating the number of `SimHash` buckets that will exist from that bit to the end of the filter. This means we are iterating `(most_significant_bit / SIM_BUCKET_SIZE) + SIM_BUCKETS` buckets.

The problem here is that in the higher order bits the sparsity is very high, meaning that there will potentially be many buckets where the values we're mixing into our `SimHash`es will just be zeros, and given any value XOR `0` is just that value this is wasted computation.

We could move to use the `BitChunks` iterators which batch up our chunks into a `u64` and an index of that block of bits, skipping ranges full of zeros. This will introduce issues where if our `SimHash` bucket straddles multiple `BitChunks`, which can be solved by peeking the next chunk, making sure that the index is contiguous with our current chunk.

Right now `SIM_BUCKET_SIZE` is always `6` bits but if we allowed fully arbitrary `SimHash` bucket sizes this could get slightly gnarly because we'd need to be able to peek an arbitrary number of `BitChunk`s ahead of the current position.

The following is a line chart plot of the proportion of empty `bit_range`s mixed into the SimHashes using pseudorandomly generated geofilters with `0..100_000` items added to them.

The ratio reduces very sharply after a couple of inserts but remains relatively high since newly added items have some chance of landing in a very significant bucket which introduces a load of zero gaps before the next one bit.

Comienza localizando SimHashIterator, BitChunks y SIM_BUCKET_SIZE en el repositorio; después, lee cómo se forman actualmente los buckets. Investiga cómo BitChunks puede omitir rangos de ceros y cómo se manejaría un bucket que abarque varios chunks. Se considera terminado cuando se reduzca la mezcla de rangos vacíos sin cambiar los resultados de los buckets de SimHash.

Escrito por el modelo de indexación a partir del texto del issue.