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

Post-selection in noisy Gaussian boson sampling: part is better than whole