  Constructing more expressive ansatz has been a primary focus for quantum
Monte Carlo, aimed at more accurate \textit{ab initio} calculations. However,
with more powerful ansatz, e.g. various recent developed models based on
neural-network architectures, the training becomes more difficult and
expensive, which may have a counterproductive effect on the accuracy of
calculation. In this work, we propose to make use of the training data to
perform variance extrapolation when using neural-network ansatz in variational
Monte Carlo. We show that this approach can speed up the convergence and
surpass the ansatz limitation to obtain an improved estimation of the energy.
Moreover, variance extrapolation greatly enhances the error cancellation
capability, resulting in significantly improved relative energy outcomes, which
are the keys to chemistry and physics problems.
