Neural Non-Equilibrium Hamiltonian Monte Carlo for Corrected Boltzmann Sampling

Learned dynamical proposals can generate configurations without providing a tractable endpoint density. Nonequilibrium path probabilities offer a way to correct such proposals, but correction alone does not determine their ability to connect separated regions. We introduce Neural Non-Equilibrium Hamiltonian Monte Carlo (NHMC), which combines conditional momentum distributions with reversible, volume preserving dynamics. The forward--reverse path ratio gives the work used for training, importance weighting, normalizer estimation, and Metropolis correction. We then construct a configuration space round-trip kernel whose reverse and forward paths share an intermediate configuration. Conditional on that configuration, its path record update is independence Metropolis--Hastings. We compare its stationary inter-region flow with the flow obtained under exact conditional matching and bound their difference by the conditional path mismatch. This separates errors in the conditional proposal from dependence between the endpoint region and the intermediate configuration. Many well calculations test normalizers and mode probabilities; a controlled lattice φ4 calculation compares ideal and accepted inter-sector flows and measures sector relaxation. In two dimensional U(1), SU(2), and SU(3) gauge theories, corrected path weight estimates reproduce finite volume Wilson loops; U(1) topological susceptibilities are also compared with analytic predictions across several lattice volumes.