  We show that belief propagation combined with ordered statistics
post-processing is a general decoder for quantum low density parity check codes
constructed from the hypergraph product. To this end, we run numerical
simulations of the decoder applied to three families of hypergraph product
code: topological codes, fixed-rate random codes and a new class of codes that
we call semi-topological codes. Our new code families share properties of both
topological and random hypergraph product codes, with a construction that
allows for a finely-controlled trade-off between code threshold and stabilizer
locality. Our results indicate thresholds across all three families of
hypergraph product code, and provide evidence of exponential suppression in the
low error regime. For the Toric code, we observe a threshold in the range
$9.9\pm0.2\%$. This result improves upon previous quantum decoders based on
belief propagation, and approaches the performance of the minimum weight
perfect matching algorithm. We expect semi-topological codes to have the same
threshold as Toric codes, as they are identical in the bulk, and we present
numerical evidence supporting this observation.
