A data-driven framework using normalizing flows predicts the rate and kinematic distributions of dark photon and millicharged particle production directly from measured dilepton events.
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A flow-matching generative model trained on CoLBT-hydro data conditionally generates marginal final-state hadron spectra from jet-induced hydro responses in 0-10% Pb+Pb collisions at 5.02 TeV, matching training data statistics with approximately six orders of magnitude computational speedup.
QFAN generates calorimeter shower images block-by-block with a reusable 3-qubit circuit, reproducing pixel distributions and correlations on simulators and IBM hardware as a proof of principle.
BRICKS creates compositional neural Markov kernels via hybrid transformers and Riemannian Flow Matching on product manifolds to enable zero-shot simulation of radiation-matter interactions across arbitrary material distributions.
Compares ensemble, Bayesian, and evidential regression approaches for uncertainty quantification in amplitude surrogates and shows they detect localized training data issues.
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Quantum Feature Amplification Network (QFAN) as An Autoregressive Quantum Generative Model
QFAN generates calorimeter shower images block-by-block with a reusable 3-qubit circuit, reproducing pixel distributions and correlations on simulators and IBM hardware as a proof of principle.