QuanEstimation: An open-source toolkit for quantum parameter estimation
Mao Zhang, Huai-Ming Yu, Haidong Yuan, Xiaoguang Wang, Rafa{\l}, Demkowicz-Dobrza\'nski, Jing Liu

TL;DR
QuanEstimation is an open-source toolkit that facilitates quantum parameter estimation by providing various bounds and optimization methods, enabling efficient and practical scheme design in quantum metrology.
Contribution
This paper introduces QuanEstimation, a comprehensive Python-Julia toolkit that integrates multiple bounds and optimization techniques for quantum parameter estimation.
Findings
Enables efficient optimization of quantum schemes
Supports various mathematical bounds and methods
Facilitates practical quantum metrology scheme design
Abstract
Quantum parameter estimation promises a high-precision measurement in theory, however, how to design the optimal scheme in a specific scenario, especially under a practical condition, is still a serious problem that needs to be solved case by case due to the existence of multiple mathematical bounds and optimization methods. Depending on the scenario considered, different bounds may be more or less suitable, both in terms of computational complexity and the tightness of the bound itself. At the same time, the metrological schemes provided by different optimization methods need to be tested against realization complexity, robustness, etc. Hence, a comprehensive toolkit containing various bounds and optimization methods is essential for the scheme design in quantum metrology. To fill this vacancy, here we present a Python-Julia-based open-source toolkit for quantum parameter estimation,…
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Taxonomy
TopicsQuantum Information and Cryptography · Quantum Computing Algorithms and Architecture · Scientific Measurement and Uncertainty Evaluation
