Total Variation Distance for Product Distributions is $\mathrm{\#}\mathsf{P}$-Complete

We show that computing the total variation distance between two product distributions is $\mathrm{\#}\mathsf{P}$-complete. This is in stark contrast with other distance measures such as Kullback-Leibler, Chi-square, and Hellinger, which tensorize over the marginals leading to efficient algorithms.