I can't see how an algorithm to lookup bit 'i' in a compressed bitmap of size 'n' can ever be constant time.
Using double here seems to be unsuitable for this kind of algorithm.
Also I don't see how your algorithm is constant time, unless you can write a bitmap class with a constant time operator[].
I wonder what is the benefit compared to RLE.
jooster's comment seems to be valid too
We benchmark against 10 other search APIs on fresh news queries. The median was around 1.2s; we came in around 166ms and scored highest on answer accuracy (89% vs 84% for the next cluster).
Latency matters because agents loop. A 1.2s first call eats the budget for follow-ups — you get one shot at framing the query. At sub-250ms the agent can actually search, read, reformulate, and search again.
Measuring this stuff carefully is something I've been at for a while. My ECIR 2019 paper (linked below) was an exhaustive study of 11 index compression methods across 5 query processing algorithms on standard collections — the codebase became PISA, which a lot of IR folks still use for research. Almost ten years later, the workload has changed completely (agents, not humans), but the benchmarking discipline is the same.
ECIR 2019 paper: https://www.antoniomallia.it/uploads/ECIR19c.pdf
Pisa Engine: https://github.com/pisa-engine/pisa
Full methodology and charts for Seltz: https://seltz.ai/blog/why-we-built-seltz