Quantum measurement tomography with mini-batch stochastic gradient descent

• URL: http://arxiv.org/abs/2511.15682v1
• Date: Wed, 19 Nov 2025 18:30:15 GMT
• Title: Quantum measurement tomography with mini-batch stochastic gradient descent
• Authors: Akshay Gaikwad, Manuel Sebastian Torres, Anton Frisk Kockum,
• Abstract summary: Drawing inspiration from gradient-descent methods developed for data processing in quantum state tomography [hrefhttps://iopscience.iop.org/article088/2058-9565/ae0baaQuantum Sci.Technol.textbf10 045055 (2025)] and quantum process tomography [hrefhttps://journals.aps.org/prl/abstract103/PhysRevLett.130.150402Phys.Rev.Lett.textbf130, 150402 (2023)], we
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Drawing inspiration from gradient-descent methods developed for data processing in quantum state tomography [\href{https://iopscience.iop.org/article/10.1088/2058-9565/ae0baa}{Quantum Sci.~Technol.~\textbf{10} 045055 (2025)}] and quantum process tomography [\href{https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.130.150402}{Phys.~Rev.~Lett.~\textbf{130}, 150402 (2023)}], we introduce stochastic gradient descent (SGD) algorithms for fast quantum measurement tomography (QMT), applicable to both discrete- and continuous-variable quantum systems -- thus completing the tomography trio. A measurement device or detector in a quantum experiment is characterized by a set of positive operator-valued measure (POVM) elements; the goal of QMT is to estimate these operators from experimental data. To ensure physically valid (positive and complete) POVM reconstructions, we propose two distinct parameterization schemes within the SGD framework: one leveraging optimization on a Stiefel manifold and one based on Hermitian operator normalization via eigenvalue scaling. Within the SGD-QMT framework, we further investigate two loss functions: mean squared error, equivalent to L2 or Euclidean norm, and average negative log-likelihood, inspired by maximum likelihood estimation. We benchmark performance against state-of-the-art constrained convex optimization methods. Numerical simulations demonstrate that, compared to standard methods, our SGD-QMT algorithms offer significantly lower computational cost, superior reconstruction fidelity, and enhanced robustness to noise. We make a Python implementation of the SGD-QMT algorithms publicly available at \href{https://github.com/agtomo/SGD-QMT}{github.com/agtomo/SGD-QMT}.

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