arXiv Machine Learning July 28, 2026 By Gerhard Hellstern, Danyal Maheshwari, Martin Zaefferer, Martin Braun, Tanja D\"ohler

Variational Quantum Conditional Boltzmann Machines for Time-Series Forecasting: Architectures, Symmetric Hyperparameter Evaluation, and a Nonlinear Benchmark

Read the original on 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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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