Learning-based quantum state reconstruction using biased quantum state distributions

Sanjaya Lohani; Joseph M. Lukens; Daniel E. Jones; Ryan T. Glasser; Thomas A. Searles; Brian T. Kirby; Brian T. Kirby

doi:10.1364/CLEO_AT.2022.AW4P.2

Conference on Lasers and Electro-Optics
Technical Digest Series (Optica Publishing Group, 2022),
paper AW4P.2
•https://doi.org/10.1364/CLEO_AT.2022.AW4P.2

Learning-based quantum state reconstruction using biased quantum state distributions

Sanjaya Lohani, Joseph M. Lukens, Daniel E. Jones, Ryan T. Glasser, Thomas A. Searles, and Brian T. Kirby

CLEO: Applications and Technology 2022

San Jose, California United States
15–20 May 2022
ISBN: 978-1-957171-05-0

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Quantum Technology & Quantum Computing (AW4P)

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S. Lohani, J. M. Lukens, D. E. Jones, R. T. Glasser, T. A. Searles, and B. T. Kirby, "Learning-based quantum state reconstruction using biased quantum state distributions," in Conference on Lasers and Electro-Optics, Technical Digest Series (Optica Publishing Group, 2022), paper AW4P.2.

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Abstract

We derive the Dirichlet concentration parameters for mixtures of Haar-random pure states that recover mean purities equal to standard measures, demonstrating how tailored distributions attain appreciable performance advantages in machine-learning-based and Bayesian quantum state reconstruction.

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