  Computing accurate yet efficient approximations to the solutions of the
electronic Schr\"odinger equation has been a paramount challenge of
computational chemistry for decades. Quantum Monte Carlo methods are a
promising avenue of development as their core algorithm exhibits a number of
favorable properties: it is highly parallel, and scales favorably with the
considered system size, with an accuracy that is limited only by the choice of
the wave function ansatz. The recently introduced machine-learned
parametrizations of quantum Monte Carlo ansatzes rely on the efficiency of
neural networks as universal function approximators to achieve state of the art
accuracy on a variety of molecular systems. With interest in the field growing
rapidly, there is a clear need for easy to use, modular, and extendable
software libraries facilitating the development and adoption of this new class
of methods. In this contribution, the DeepQMC program package is introduced, in
an attempt to provide a common framework for future investigations by unifying
many of the currently available deep-learning quantum Monte Carlo
architectures. Furthermore, the manuscript provides a brief introduction to the
methodology of variational quantum Monte Carlo in real space, highlights some
technical challenges of optimizing neural network wave functions, and presents
example black-box applications of the program package. We thereby intend to
make this novel field accessible to a broader class of practitioners both from
the quantum chemistry as well as the machine learning communities.
