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quant-ph2025
End-to-end Optimization of Single-Shot Quantum Machine Learning for Bayesian Inference
Theodoros Ilias, Fangjun Hu, Marti Vives +1
We introduce an end-to-end optimization strategy for quantum machine learning that directly targets performance under finite measurement resources, where learning objectives are de…
math-ph2025
Generalized Time-Coarse Graining via an Operator Cumulant Expansion
Leon Bello, Tal Rubin, Wentao Fan +2
We introduce a general framework for deriving effective dynamics from arbitrary time-dependent generators, based on a systematic operator cumulant expansion. Unlike traditional app…