Efficient Quantum State Tomography with Mode-assisted Training

Autor:	Zhang, Yuan-Hang, Di Ventra, Massimiliano
Rok vydání:	2021
Předmět:	Quantum Physics
Druh dokumentu:	Working Paper
Popis:	Neural networks (NNs) representing quantum states are typically trained using Markov chain Monte Carlo based methods. However, unless specifically designed, such samplers only consist of local moves, making the slow-mixing problem prominent even for extremely simple quantum states. Here, we propose to use mode-assisted training that provides global information via the modes of the NN distribution. Applied to quantum state tomography using restricted Boltzmann machines, this method improves the quality of reconstructed quantum states by orders of magnitude. The method is applicable to other types of NNs and may efficiently tackle problems previously unmanageable. Comment: 12 pages, 13 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2112.14866 Zobrazit plný text záznamu View this record from Arxiv