Work

A practical and efficient approach for Bayesian quantum state estimation

Year: 2020

Type: article

Abstract: Bayesian inference is a powerful paradigm for quantum state tomography, treating uncertainty in meaningful and informative ways. Yet the numerical challenges associated with sampling from complex probability distributions hampers Bayesian tomography in practical settings. In this article, we introduce an improved, self-contained approach for Bayesian quantum state estimation. Leveraging advances in machine learning and statistics, our formulation relies on highly efficient preconditioned Crank–Nicolson sampling and a pseudo-likelihood. We theoretically analyze the computational cost, and provide explicit examples of inference for both actual and simulated datasets, illustrating improved performance with respect to existing approaches.

Source: New Journal of Physics

DOI: 10.1088/1367-2630/ab8efa

Authors: Joseph M Lukens, Kody J H Law, Ajay Jasra, Pavel Lougovski

Institutions: Oak Ridge National Laboratory, University of Manchester, King Abdullah University of Science and Technology

Language: English

Topic: Quantum Computing Algorithms and Architecture

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,610

Funders: U.S. Department of Energy, Battelle, Advanced Scientific Computing Research, Oak Ridge National Laboratory