Self-guided quantum state learning for mixed states

Ahmad Farooq; Muhammad Asad Ullah; Junaid ur Rehman; Kyesan Lee; Hyundong Shin

doi:10.1007/s11128-022-03585-8

Self-guided quantum state learning for mixed states

Ahmad Farooq, Muhammad Asad Ullah, Junaid ur Rehman, Kyesan Lee^*, Hyundong Shin^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

Abstract

We provide an adaptive learning algorithm for tomography of general quantum states. Our proposal is based on the simultaneous perturbation stochastic approximation algorithm and applies to mixed qudit states. The salient features of our algorithm are efficient post-processing in dimension d of the state, robustness against measurement and channel noise, and improved infidelity performance as compared to the contemporary adaptive state learning algorithms. A higher resilience against measurement noise makes our algorithm suitable for noisy intermediate-scale quantum applications.

Original language	English
Article number	243
Journal	Quantum Information Processing
Volume	21
Issue number	7
DOIs	https://doi.org/10.1007/s11128-022-03585-8
State	Published - Jul 2022
Externally published	Yes

Bibliographical note

Publisher Copyright:
© 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

Keywords

Gram–Schmidt
Mutually unbiased bases
Quantum state tomography
Simultaneous perturbation stochastic approximation

ASJC Scopus subject areas

Electronic, Optical and Magnetic Materials
Statistical and Nonlinear Physics
Theoretical Computer Science
Signal Processing
Modeling and Simulation
Electrical and Electronic Engineering

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10.1007/s11128-022-03585-8

Cite this

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AU - Lee, Kyesan

AU - Shin, Hyundong

PY - 2022/7

Y1 - 2022/7

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AB - We provide an adaptive learning algorithm for tomography of general quantum states. Our proposal is based on the simultaneous perturbation stochastic approximation algorithm and applies to mixed qudit states. The salient features of our algorithm are efficient post-processing in dimension d of the state, robustness against measurement and channel noise, and improved infidelity performance as compared to the contemporary adaptive state learning algorithms. A higher resilience against measurement noise makes our algorithm suitable for noisy intermediate-scale quantum applications.

KW - Gram–Schmidt

KW - Mutually unbiased bases

KW - Quantum state tomography

KW - Simultaneous perturbation stochastic approximation

UR - https://www.scopus.com/pages/publications/85134217160

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ER -

Self-guided quantum state learning for mixed states

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

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Cite this