The Flow has not summarised this story yet — read it at arXiv Machine Learning.
arXiv:2607. 24065v1 Announce Type: cross Abstract: In this study, we developed and evaluated four conditional energy-based forecasting architectures: a classical Gaussian-Bernoulli CRBM, a hybrid quantum-classical QCRBM, a full-register QQRBM, and a lag-feature QFeatureQRBM with complete derivations of their conditional distributions, Contrastive-Divergence gradients, and hybrid training, bridging the energy-based formulation and the implementation-level quantum computation.
arXiv:2606. 13912v1 Announce Type: cross Abstract: Neural-network quantum states (NQS) are a leading variational tool for quantum many-body physics, yet their optimization is fragile whenever the ground state carries a non-trivial sign or complex phase structure, a situation generic to gauge fields, broken time-reversal symmetry, and fermionic statistics.
arXiv:2608.31117v1 Announce Type: cross Abstract: Generative models have become central across science and industry, from image and text synthesis to the design of molecules and materials. Quantum ge...
The paper introduces a new transition kernel for Restricted Boltzmann Machines that operates over the sequence of models used in Deep Tempering. This kernel employs a round‑trip structure, allowing nonlocal moves in a single transition while keeping the RBM sequence unchanged. Experiments demonstrate that it achieves higher sampling quality with fewer transitions than both blocked Gibbs sampling and Deep Tempering, and it stabilizes learning by reducing training failures.
The paper introduces NS‑Flows, a flow‑based nested sampling method that replaces Markov‑chain updates with a conditional normalizing flow trained on live sets. By applying this technique to a Lennard‑Jones particle system, the authors achieve over two orders of magnitude fewer energy evaluations and a roughly one‑third reduction in wall‑clock time compared to traditional nested sampling. The study also shows that the flow’s generation efficiency varies non‑monotonically along the annealing trajectory, providing a diagnostic of the system’s internal mode complexity and identifying liquid‑like ensembles as the most challenging for current flow architectures.
arXiv:2512. 19819v2 Announce Type: replace-cross Abstract: One of the primary applications of classical Boltzmann machines is generative modeling, wherein the goal is to tune the parameters of a model distribution so that it closely approximates a target distribution.