Efficient and Robust Methods for Quantum Tomography
Charles H. Baldwin

TL;DR
This paper introduces new, efficient, and noise-robust quantum tomography methods that leverage prior information and quantum constraints, making state and process verification more practical for large-scale quantum systems.
Contribution
The paper presents novel quantum tomography techniques that are more resource-efficient and robust, specifically tailored for systems near ideal quantum states and processes, utilizing positivity constraints.
Findings
New methods reduce resource requirements for QT.
Methods demonstrate robustness to noise and model imperfections.
Experimental validation confirms effectiveness of the proposed techniques.
Abstract
The development of large-scale platforms for quantum information requires new methods for verification and validation of quantum behavior. Quantum tomography (QT) is the standard tool for diagnosing quantum states, process, and readout devices by providing complete information. However, QT is limited since it is expensive to not only implement experimentally, but also requires heavy classical post-processing of data. In this dissertation, we introduce new methods for QT that are more efficient to implement and robust to noise and errors, thereby making QT a more practical tool for current quantum information experiments. The crucial detail that makes these new, efficient, and robust methods possible is prior information about the quantum system. This prior information is prompted by the goals of most experiments in quantum information, which require pure states, unitary processes, and…
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Taxonomy
TopicsQuantum Information and Cryptography · Quantum Mechanics and Applications · Cold Atom Physics and Bose-Einstein Condensates
