  Estimating the expected value of an observable appearing in a non-equilibrium
stochastic process usually involves sampling. If the observable's variance is
high, many samples are required. In contrast, we show that performing the same
task without sampling, using tensor network compression, efficiently captures
high variances in systems of various geometries and dimensions. We provide
examples for which matching the accuracy of our efficient method would require
a sample size scaling exponentially with system size. In particular, the high
variance observable $\mathrm{e}^{-\beta W}$, motivated by Jarzynski's equality,
with $W$ the work done quenching from equilibrium at inverse temperature
$\beta$, is exactly and efficiently captured by tensor networks.
