  Gaussian boson sampling is originally proposed to show quantum advantage with
quantum linear optical elements. Recently, several experimental breakthroughs
based on Gaussian boson sampling pointing to quantum computing supremacy have
been presented. However, due to technical limitations, the outcomes of Gaussian
boson sampling devices are influenced severely by photon loss. Here, we present
an efficient and practical method to reduce the negative effect caused by
photon loss. With no hardware modifications, our method takes the data
post-selection process that discards low-quality data according to our
criterion to improve the performance of the final computational results, say
part is better than whole. As an example, we show that the post-selection
method can turn a GBS experiment that would otherwise fail in a ``non-classical
test" into one that can pass that test. Besides improving the robustness of
computation results of current GBS devices, this photon loss mitigation method
may also benefit the further development of GBS-based quantum algorithms.
