Quantum state tomography with disentanglement algorithm

Fuente: arXiv
Guardado en:
Detalles Bibliográficos
Autor principal: Yao, Juan
Formato: Preprint
Publicado: 2023
Materias:
Acceso en línea:
Etiquetas: Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
_version_ 1866912107714314240
author Yao, Juan
author_facet Yao, Juan
contents In this work, we report on a novel quantum state reconstruction process based on the disentanglement algorithm. Using variational quantum circuits, we disentangle the quantum state to a product of computational zero states. Inverse evolution of the zero states reconstructs the quantum state up to an overall phase. By sequentially disentangling the qubit one by one, we reduce the required measurements with only single qubit measurement. Demonstrations with our proposal for the reconstruction of the random states are presented where variational quantum circuit is optimized by disentangling process. To facilitate experimental implementation, we also employ reinforcement learning for quantum circuit design with limited discrete quantum gates. Our method is universal and imposes no specific ansatz or constrain on the quantum state.
format Preprint
id arxiv_https___arxiv_org_abs_2310_06273
institution arXiv
publishDate 2023
record_format arxiv
spellingShingle Quantum state tomography with disentanglement algorithm
Yao, Juan
Quantum Physics
In this work, we report on a novel quantum state reconstruction process based on the disentanglement algorithm. Using variational quantum circuits, we disentangle the quantum state to a product of computational zero states. Inverse evolution of the zero states reconstructs the quantum state up to an overall phase. By sequentially disentangling the qubit one by one, we reduce the required measurements with only single qubit measurement. Demonstrations with our proposal for the reconstruction of the random states are presented where variational quantum circuit is optimized by disentangling process. To facilitate experimental implementation, we also employ reinforcement learning for quantum circuit design with limited discrete quantum gates. Our method is universal and imposes no specific ansatz or constrain on the quantum state.
title Quantum state tomography with disentanglement algorithm
topic Quantum Physics
url https://arxiv.org/abs/2310.06273