Regularizing least squares quantum state tomography with classical shadows

Conference · Mon Sep 01 04:00:00 EDT 2025

DOI:https://doi.org/10.1364/CLEO_FS.2025.FF120_6· OSTI ID:3002404

Zhu, Zhihui ^[1]; Lukens, Joseph ^[2]; Kirby, Brian T ^[3]

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1. Ohio State University
2. ORNL
3. U.S. Army Research Laboratory, Adelphi, MD

Classical shadows herald remarkable opportunities for resource-efficient quantum estimation. Although superficially disconnected from traditional inference methods, we show how classical shadows fit under a larger umbrella of least squares regularization, revealing tradeoffs with related methods.

• Research Organization: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: USDOE
• DOE Contract Number: AC05-00OR22725
• OSTI ID: 3002404
• Country of Publication: United States
• Language: English