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Journal of Experimental and Theoretical Physics Letters

Reduction of Measurement Complexity at Rank Reduction for Tomography of Polarization Qutrits in the Natural Basis

Denis Chupakhin, N. A. Borshchevskaya, B. I. Bantysh, Konstantin G. Katamadze, S. P. Kulik, Yu. I. Bogdanov

Published Feb 1, 2026• 0 citations
Publisher

Abstract

Tomography of polarization qutrits in a measurement scheme in a natural polarization basis with a minimum number of tunable elements has been investigated. The information completeness of protocols for states of various ranks has been analyzed theoretically using the analysis of the information matrix and compressive tomography methods was carried out; the results have been confirmed experimentally. The existence of a protocol with five dimensions that provides a complete reconstruction of arbitrary (including mixed) states has been shown, and it has been found that schemes with a smaller number of dimensions provide the complete reconstruction of pure states. The obtained protocols demonstrate high accuracy of reconstructing test states under the conditions of a limited set of rotations of the measuring basis.

DOI
10.1134/s0021364025610036

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Tomography of polarization qutrits in a measurement scheme in a natural polarization basis with a minimum number of tunable elements has been investigated. The information completeness of protocols for states of various ranks has been analyzed theoretically using the analysis of the information matrix and compressive tomography methods was carried out; the results have been confirmed experimentally. The existence of a protocol with five dimensions that provides a complete reconstruction of arbitrary (including mixed) states has been shown, and it has been found that schemes with a smaller number of dimensions provide the complete reconstruction of pure states. The obtained protocols demonstrate high accuracy of reconstructing test states under the conditions of a limited set of rotations of the measuring basis.
Based on mutually unbiased measurements, an optimal tomographic scheme for the multiqutrit states is presented explicitly. Because the reconstruction process of states based on mutually unbiased states is free of information waste, we refer to our scheme as the optimal scheme. By optimal we mean that the number of the required conditional operations reaches the minimum in this tomographic scheme for the states of qutrit systems. Special attention will be paid to how those different mutually unbiased measurements are realized; that is, how to decompose each transformation that connects each mutually unbiased basis with the standard computational basis. It is found that all those transformations can be decomposed into several basic implementable single- and two-qutrit unitary operations. For the three-qutrit system, there exist five different mutually unbiased-bases structures with different entanglement properties, so we introduce the concept of physical complexity to minimize the number of nonlocal operations needed over the five different structures. This scheme is helpful for experimental scientists to realize the most economical reconstruction of quantum states in qutrit systems.
Intuitively, if a density operator has small rank, then it should be easier to estimate from experimental data, since in this case only a few eigenvectors need to be learned. We prove two complementary results that confirm this intuition. Firstly, we show that a low-rank density matrix can be estimated using fewer copies of the state, i.e. the sample complexity of tomography decreases with the rank. Secondly, we show that unknown low-rank states can be reconstructed from an incomplete set of measurements, using techniques from compressed sensing and matrix completion. These techniques use simple Pauli measurements, and their output can be certified without making any assumptions about the unknown state. In this paper, we present a new theoretical analysis of compressed tomography, based on the restricted isometry property for low-rank matrices. Using these tools, we obtain near-optimal error bounds for the realistic situation where the data contain noise due to finite statistics, and the density matrix is full-rank with decaying eigenvalues. We also obtain upper bounds on the sample complexity of compressed tomography, and almost-matching lower bounds on the sample complexity of any procedure using adaptive sequences of Pauli measurements. Using numerical simulations, we compare the performance of two compressed sensing estimators—the matrix Dantzig selector and the matrix Lasso—with standard maximum-likelihood estimation (MLE). We find that, given comparable experimental resources, the compressed sensing estimators consistently produce higher fidelity state reconstructions than MLE. In addition, the use of an incomplete set of measurements leads to faster classical processing with no loss of accuracy. Finally, we show how to certify the accuracy of a low-rank estimate using direct fidelity estimation, and describe a method for compressed quantum process tomography that works for processes with small Kraus rank and requires only Pauli eigenstate preparations and Pauli measurements.
We perform several numerical studies for our recently published adaptive compressive tomography scheme [D. Ahn et al., Phys. Rev. Lett. 122, 100404 (2019)], which significantly reduces the number of measurement settings to unambiguously reconstruct any rank-deficient state without any a priori knowledge besides its dimension. We show that both entangled and product bases chosen by our adaptive scheme perform comparably well with recently known compressed-sensing element-probing measurements, and also beat random measurement bases for low-rank quantum states. We also numerically conjecture asymptotic scaling behaviors for this number as a function of the state rank for our adaptive schemes. These scaling formulas appear to be independent of the Hilbert-space dimension. As a natural development, we establish a faster hybrid compressive scheme that first chooses random bases, and later adaptive bases as the scheme progresses. As an epilogue, we reiterate important elements of informational completeness for our adaptive scheme.
We establish methods for quantum state tomography based on compressed sensing. These methods are specialized for quantum states that are fairly pure, and they offer a significant performance improvement on large quantum systems. In particular, they are able to reconstruct an unknown density matrix of dimension d and rank r using O(rdlog²d) measurement settings, compared to standard methods that require d² settings. Our methods have several features that make them amenable to experimental implementation: they require only simple Pauli measurements, use fast convex optimization, are stable against noise, and can be applied to states that are only approximately low rank. The acquired data can be used to certify that the state is indeed close to pure, so no a priori assumptions are needed.