Quantum relative entropy regularization for quantum state tomography

Fuente: arXiv
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Main Authors: Oberender, Florian, Hohage, Thorsten
Format: Preprint
Published: 2026
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author Oberender, Florian
Hohage, Thorsten
author_facet Oberender, Florian
Hohage, Thorsten
contents The density matrix is a positive semidefinite operator of trace 1 characterizing the state of a quantum system. We consider the inverse problem to reconstruct such density matrices from indirect measurements, also known as quantum state tomography. To solve such inverse problems in high or infinite dimensional settings, we study variational regularization using the quantum relative entropy as penalty functional. Quantum relative entropy is an analog of the well-known maximum entropy functional with compositions of functions replaced by the spectral functional calculus. The main aim of this paper is to establish the regularizing property of this scheme. As a crucial intermediate step, we establish lower semi-compactness of the penalty functional with respect to the weak-$*$-topology. Moreover, we compute the subgradient, proximal operator, and conjugate functional of the quantum relative entropy on finite dimensional spaces. This enables us to apply iterative algorithms from convex optimization to solve the regularized problems numerically. To show the validity and practical value of our results, we apply our theory to the examples of Photon-Induced Near-field Electron Microscopy (PINEM) and to optical homodyne tomography.
format Preprint
id arxiv_https___arxiv_org_abs_2603_04922
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle Quantum relative entropy regularization for quantum state tomography
Oberender, Florian
Hohage, Thorsten
Numerical Analysis
65J20, 81P18, 81P17, 65K10
The density matrix is a positive semidefinite operator of trace 1 characterizing the state of a quantum system. We consider the inverse problem to reconstruct such density matrices from indirect measurements, also known as quantum state tomography. To solve such inverse problems in high or infinite dimensional settings, we study variational regularization using the quantum relative entropy as penalty functional. Quantum relative entropy is an analog of the well-known maximum entropy functional with compositions of functions replaced by the spectral functional calculus. The main aim of this paper is to establish the regularizing property of this scheme. As a crucial intermediate step, we establish lower semi-compactness of the penalty functional with respect to the weak-$*$-topology. Moreover, we compute the subgradient, proximal operator, and conjugate functional of the quantum relative entropy on finite dimensional spaces. This enables us to apply iterative algorithms from convex optimization to solve the regularized problems numerically. To show the validity and practical value of our results, we apply our theory to the examples of Photon-Induced Near-field Electron Microscopy (PINEM) and to optical homodyne tomography.
title Quantum relative entropy regularization for quantum state tomography
topic Numerical Analysis
65J20, 81P18, 81P17, 65K10
url https://arxiv.org/abs/2603.04922