Many-body localized hidden generative models

Autor:	Zhong, Weishun, Gao, Xun, Yelin, Susanne F., Najafi, Khadijeh
Rok vydání:	2022
Předmět:	Quantum Physics Condensed Matter - Disordered Systems and Neural Networks Condensed Matter - Statistical Mechanics Computer Science - Machine Learning Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	Born machines are quantum-inspired generative models that leverage the probabilistic nature of quantum states. Here, we present a new architecture called many-body localized (MBL) hidden Born machine that utilizes both MBL dynamics and hidden units as learning resources. We show that the hidden units act as an effective thermal bath that enhances the trainability of the system, while the MBL dynamics stabilize the training trajectories. We numerically demonstrate that the MBL hidden Born machine is capable of learning a variety of tasks, including a toy version of MNIST handwritten digits, quantum data obtained from quantum many-body states, and non-local parity data. Our architecture and algorithm provide novel strategies of utilizing quantum many-body systems as learning resources, and reveal a powerful connection between disorder, interaction, and learning in quantum many-body systems. Comment: 13 pages, 11 figures; added references
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2207.02346 Zobrazit plný text záznamu View this record from Arxiv