Bayesian quantum state reconstruction with a learning-based tuned prior

Sangita Regmi; Ashley N. Blackwell; Amirali Khannejad; Sanjaya Lohani; Joseph M. Lukens; Joseph M. Lukens; Ryan T. Glasser; Brian T. Kirby; Brian T. Kirby; Thomas A. Searles

doi:10.1364/QUANTUM.2023.QM4B.3

Optica Quantum 2.0 Conference and Exhibition
Technical Digest Series (Optica Publishing Group, 2023),
paper QM4B.3
•https://doi.org/10.1364/QUANTUM.2023.QM4B.3

Bayesian quantum state reconstruction with a learning-based tuned prior

Sangita Regmi, Ashley N. Blackwell, Amirali Khannejad, Sanjaya Lohani, Joseph M. Lukens, Ryan T. Glasser, Brian T. Kirby, and Thomas A. Searles

Quantum 2.0 2023

Denver, Colorado United States
18–22 June 2023
ISBN: 978-1-957171-27-2

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QIS Theory (QM4B)

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S. Regmi, A. N. Blackwell, A. Khannejad, S. Lohani, J. M. Lukens, R. T. Glasser, B. T. Kirby, and T. A. Searles, "Bayesian quantum state reconstruction with a learning-based tuned prior," in Optica Quantum 2.0 Conference and Exhibition, Technical Digest Series (Optica Publishing Group, 2023), paper QM4B.3.

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Abstract

We demonstrate machine-learning-enhanced Bayesian quantum state tomography on near-term intermediate-scale quantum hardware. Our approach to selecting prior distributions leverages pre-trained neural networks incorporating measurement data and en-ables improved inference times over standard prior distributions.

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