John Paisley

Entangled quantum circuits can significantly hinder generalization, leading to worse performance than non-entangled circuits with the same number of parameters.

Calibration, not compilation, is the key to ensuring statistical accuracy in probabilistic programs generated by language models, with detection rates soaring to 97% when using Bayesian workflows.

Trajectory-guidance solvers can lead to up to 800 times larger errors compared to simple source reweighting, fundamentally reshaping our understanding of flow-based inverse problem solving.

Manipulating the effective dimension of quantum kernels can enhance generalization and accuracy in quantum vision models, revealing a surprising benefit of noise injection.

Abstaining from uncertain claims is wasteful when additional visual evidence can be efficiently acquired, and our BCEA approach proves it can enhance both reliability and coverage in LVLMs.

Preference optimization objectives, despite their diversity, can be steered towards disentangled dynamics that avoid suppressing the chosen response alongside the rejected one, simply by satisfying a "disentanglement band" condition.