Speaker
Description
Neural quantum states provide expressive variational representations of quantum many-body wavefunctions. However, their practical performance depends on how well they can sample the relevant configurations from an exponentially large Hilbert space. Conventional Markov chain Monte Carlo methods can mix slowly between separated high-probability regions, particularly in frustrated and strongly correlated systems.
We introduce a hybrid variational framework in which a continuous normalising flow learns an auxiliary sampling distribution over a discretised effective subspace, while an independent variational ansatz learns the wavefunction amplitudes. This separates the task of finding the relevant support from the task of estimating the amplitudes within it. The normalising flow can therefore explore non-local regions of configuration space without relying on a sequence of local updates.
We apply the method to the square-lattice J1–J2 Heisenberg model and compare it with conventional Metropolis sampling using matched variational ansätze and optimisation settings. Our results show that flow-assisted sampling remains competitive across the system sizes studied and improves variational ground-state estimation in several regimes. This suggests that generative-model-assisted sampling can provide a useful practical approach to quantum many-body simulation.