A supplemental investigation of non-linearity in quantum generative models with respect to simulatability and optimization

arXiv:2302.00788v2 Announce Type: replace-cross
Abstract: Recent work has demonstrated the utility of introducing non-linearity through repeat-until-success (RUS) sub-routines into quantum circuits for generative modeling. As a follow-up to this work, we investigate two questions of relevance to the quantum algorithms and machine learning communities: Does introducing this form of non-linearity make the learning model classically simulatable due to the deferred measurement principle? And does introducing this form of non-linearity make the overall model's training more unstable? With respect to the …

abstract algorithms arxiv circuits communities cs.lg cs.ne generative generative modeling generative models investigation machine machine learning modeling optimization quant-ph quantum questions repeat success through type utility work