  Recently, Information Retrieval community has witnessed fast-paced advances
in Dense Retrieval (DR), which performs first-stage retrieval with
embedding-based search. Despite the impressive ranking performance, previous
studies usually adopt brute-force search to acquire candidates, which is
prohibitive in practical Web search scenarios due to its tremendous memory
usage and time cost. To overcome these problems, vector compression methods
have been adopted in many practical embedding-based retrieval applications. One
of the most popular methods is Product Quantization (PQ). However, although
existing vector compression methods including PQ can help improve the
efficiency of DR, they incur severely decayed retrieval performance due to the
separation between encoding and compression. To tackle this problem, we present
JPQ, which stands for Joint optimization of query encoding and Product
Quantization. It trains the query encoder and PQ index jointly in an end-to-end
manner based on three optimization strategies, namely ranking-oriented loss, PQ
centroid optimization, and end-to-end negative sampling. We evaluate JPQ on two
publicly available retrieval benchmarks. Experimental results show that JPQ
significantly outperforms popular vector compression methods. Compared with
previous DR models that use brute-force search, JPQ almost matches the best
retrieval performance with 30x compression on index size. The compressed index
further brings 10x speedup on CPU and 2x speedup on GPU in query latency.
