We do have some benchmark number at https://blog.vectorchord.ai/vector-search-over-postgresql-a-.... It varies on different dataset, but most cases it's 2x or more QPS comparing to pgvector's hnsw at same recall.
[1] https://cdn.hashnode.com/res/hashnode/image/upload/v17434120...
The core part is a quantization technique called RaBitQ. We can scan over the bit vector to have an estimation about the real distance between query and data. I'm not sure what do you mean by "miss" here. As the approximate nearest neighbor index, all the index including HNSW will miss some potential candidates.