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Quantum Information Processing
Published in: Quantum Information Processing 10/2021

01-10-2021

Adaptive quantum state tomography with iterative particle filtering

Authors: Syed Muhammad Kazim, Ahmad Farooq, Junaid ur Rehman, Hyundong Shin

Published in: Quantum Information Processing | Issue 10/2021

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Abstract

Several Bayesian estimation-based heuristics have been developed to perform quantum state tomography (QST). Their ability to quantify uncertainties using region estimators and include a priori knowledge of the experimentalists makes this family of methods an attractive choice for QST. However, specialized techniques for pure states do not work well for mixed states and vice versa. In this paper, we present an adaptive particle filter (PF)-based QST protocol which improves the scaling of fidelity compared to nonadaptive Bayesian schemes for arbitrary multi-qubit states. This is due to the protocol’s unabating perseverance to find the states’ diagonal bases and more systematic handling of enduring problems in popular PF methods relating to the subjectivity of informative priors and the invalidity of particles produced by resamplers. Numerical examples and implementation on IBM quantum devices demonstrate improved performance for arbitrary quantum states and the application readiness of our proposed scheme.
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Metadata
Title
Adaptive quantum state tomography with iterative particle filtering
Authors
Syed Muhammad Kazim
Ahmad Farooq
Junaid ur Rehman
Hyundong Shin
Publication date
01-10-2021
Publisher
Springer US
Published in
Quantum Information Processing / Issue 10/2021
Print ISSN: 1570-0755
Electronic ISSN: 1573-1332
DOI
https://doi.org/10.1007/s11128-021-03267-x

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