On the average-case hardness of BosonSampling

Abstract: BosonSampling is a popular candidate for near-term quantum advantage, which has now been experimentally implemented several times. The original proposal of Aaronson and Arkhipov from 2011 showed that classical hardness of BosonSampling is implied by a proof of the "Gaussian Permanent Estimation" conjecture. This conjecture states that $e^{-n\log{n}-n-O(\log n)}$ additive error estimates to the output probability of most random BosonSampling experiments are $#P$-hard. Proving this conjecture has since become the central question in the theory of quantum advantage. In this work we make progress by proving that $e^{-n\log n -n - O(n^\delta)}$ additive error estimates to output probabilities of most random BosonSampling experiments are $#P$-hard, for any $\delta>0$. In the process, we circumvent all known barrier results for proving the hardness of BosonSampling experiments. This is nearly the robustness needed to prove hardness of BosonSampling -- the remaining hurdle is now "merely" to show that the $n^\delta$ in the exponent can be improved to $O(\log n).$ We also obtain an analogous result for Random Circuit Sampling. Our result allows us to show, for the first time, a hardness of classical sampling result for random BosonSampling experiments, under an anticoncentration conjecture. Specifically, we prove the impossibility of multiplicative-error sampling from random BosonSampling experiments with probability $1-e^{-O(n)}$, unless the Polynomial Hierarchy collapses.