  Recent research has demonstrated the usefulness of neural networks as
variational ansatz functions for quantum many-body states. However,
high-dimensional sampling spaces and transient autocorrelations confront these
approaches with a challenging computational bottleneck. Compared to
conventional neural networks, physical-model devices offer a fast, efficient
and inherently parallel substrate capable of related forms of Markov chain
Monte Carlo sampling. Here, we demonstrate the ability of a neuromorphic chip
to represent the ground states of quantum spin models by variational energy
minimization. We develop a training algorithm and apply it to the transverse
field Ising model, showing good performance at moderate system sizes ($N\leq
10$). A systematic hyperparameter study shows that scalability to larger system
sizes mainly depends on sample quality, which is limited by temporal parameter
variations on the analog neuromorphic chip. Our work thus provides an important
step towards harnessing the capabilities of neuromorphic hardware for tackling
the curse of dimensionality in quantum many-body problems.
