March 2026

Conference Paper

Unorthodox Parallelization for Bayesian Quantum State Estimation

By:
Nguyen, Hanson H.; Law, Kody; Lukens, Joseph M
Page Number:
4238939
Book Title:
Proceedings of CLEO: Fundamental Science 2025
Publication Date:
March 2026
Publisher Location:
Optica Publishing Group, District of Columbia, United States of America
Conference Name:
CLEO: Fundamental Science 2025
Conference Location:
Long Beach, California, United States of America
Conference Sponsor:
Optica

Abstract

Bayesian inference enables informationally efficient quantum state tomography (QST) yet is challenging to scale computationally. We demonstrate a parallelizable Bayesian QST method that, although unorthodox, proves remarkably practical, attaining significant speedups in multiqubit state estimation.