Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

arXiv:1407.7426 · Scientific Reports

Reconstructing high-dimensional two-photon entangled states via compressive sensing

Francesco Tonolini, Susan Chan, Megan Agnew, Alan E. Lindsay, Jonathan Leach

Published Oct 13, 2014• 42 citations
arXivHTMLPDF

Abstract

Accurately establishing the state of large-scale quantum systems is an important tool in quantum information science; however, the large number of unknown parameters hinders the rapid characterisation of such states, and reconstruction procedures can become prohibitively time-consuming. Compressive sensing, a procedure for solving inverse problems by incorporating prior knowledge about the form of the solution, provides an attractive alternative to the problem of high-dimensional quantum state characterisation. Using a modified version of compressive sensing that incorporates the principles of singular value thresholding, we reconstruct the density matrix of a high-dimensional two-photon entangled system. The dimension of each photon is equal to d = 17, corresponding to a system of 83521 unknown real parameters. Accurate reconstruction is achieved with approximately 2500 measurements, only 3% of the total number of unknown parameters in the state. The algorithm we develop is fast, computationally inexpensive, and applicable to a wide range of quantum states, thus demonstrating compressive sensing as an effective technique for measuring the state of large-scale quantum systems.

DOI
10.1038/srep06542

Related papers

Multidimensional Quantum State Tomography with Compressed Sensing Method

Shikang Li, Xue Feng, Wei Zhang +1 more

Quantum state tomography (QST) is an executable procedure, allowing complete reconstruction of a quantum state. As a basic tool, QST is widely employed in quantum information research. However, many projective measurements on identical copies of the quantum state under test are required in standard QST, which is heavily time-consumed. Here, we perform reconstruction of density matrix with dimensionality of $15\times 15$ by utilizing compressed sensing method at a sampling ratio of 0.35. According to the experimental results, the fidelity could be as high as 93.0%~99.4% and 73.8% for coherent laser beams and heralded single photon sources, respectively.

IEEE International Conference on Signal, Information and Data Processing (ICSIDP)•Dec 1, 2019•0 citations

Quantum State Tomography via Compressed Sensing

David Gross, Yi-Kai Liu, Steven T. Flammia +2 more

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.

arXiv:0909.3304•Physical Review Letters•Oct 4, 2010•1,097 citations

Optimal estimation of parameters of an entangled quantum state

Salvatore Virzì, Alessio Avella, F Piacentini +4 more

Two-photon entangled quantum states are a fundamental tool for quantum information and quantum cryptography. A complete description of a generic quantum state is provided by its density matrix: the technique allowing experimental reconstruction of the density matrix is called quantum state tomography. Entangled states density matrix reconstruction requires a large number of measurements on many identical copies of the quantum state. An alternative way of certifying the amount of entanglement in two-photon states is represented by the estimation of specific parameters, e.g., negativity and concurrence. If we have a priori partial knowledge of our state, it's possible to develop several estimators for these parameters that require lower amount of measurements with respect to full density matrix reconstruction. The aim of this work is to introduce and test different estimators for negativity and concurrence for a specific class of two-photon states.

Journal of Physics Conference Series•May 1, 2017•2 citations

Quantum State Tomography via Compressed Sensing

Jincheng Zhang

Quantum state tomography (QST) is a crucial technique for characterizing the state of quantum systems. However, traditional QST requires a large number of measurements, leading to significant experimental overhead in terms of time, resources, and data acquisition. This work proposes a novel approach to QST by leveraging compressed sensing (CS) techniques. CS allows for accurate reconstruction of the quantum state from a significantly reduced set of measurements, thereby mitigating the experimental burden. We employ sparsity regularization, a key component of CS, to minimize the number of measurements needed. The theoretical framework and the underlying principles are presented, focusing on the mathematical formulation of the problem and the optimization strategies involved. This method offers a pathway to more efficient and cost-effective QST, particularly for complex quantum states or when limited experimental resources are available. We demonstrate the potential of this approach through a detailed mathematical analysis.

•Sep 5, 2026•0 citations

Statistical and Algorithmic Foundations of Probing Quantum Systems with Compressive Measurements: A Review

Zhen Qin, Michael B. Wakin, Zhihui Zhu

Quantum state tomography (QST) is a fundamental task in quantum information science that aims to reconstruct unknown quantum states from measurement data. However, the exponential growth of Hilbert-space dimension with system size makes full tomography of general quantum states statistically and computationally prohibitive. This challenge has motivated extensive research on structured quantum state tomography, where prior structure, such as low-rankness, tensor-network representations, shallow quantum circuits, and neural quantum states, can substantially reduce the effective degrees of freedom and enable scalable recovery. In this review, we provide a unified perspective on QST for structured quantum states through three closely related themes: compact state representations, measurement design, and computational algorithms. After reviewing common models for structured quantum states, we survey existing work on geometric preservation properties of measurement frameworks, ranging from informationally complete POVMs to randomized measurements, and their implications for sample complexity. On the algorithmic side, we review optimization methods for reconstructing structured quantum states from empirical measurements. By connecting QST with broader principles from compressive sensing, matrix sensing, and structured inverse problems, this survey highlights common theoretical foundations underlying sample complexity, measurement efficiency, and scalable recovery.

quant-pharXiv:2605.27191•May 27, 2026•0 citations

Tomography of the quantum state of photons entangled in high dimensions

Megan Agnew, Jonathan Leach, Melanie G. McLaren +2 more

Systems entangled in high dimensions have recently been proposed as important tools for various quantum information protocols, such as multibit quantum key distribution and loophole-free tests of nonlocality. It is therefore important to have precise knowledge of the nature of such entangled quantum states. We tomographically reconstruct the quantum state of the two photons produced by parametric downconversion that are entangled in a $d$-dimensional orbital angular momentum basis. We determine exactly the density matrix of the entangled two-qu$d$it state with $d$ ranging from 2 to 8. The recording of higher-dimensional states is limited only by the number of data points required and therefore the length of time needed to complete the measurements. We find all the measured states to have fidelities and linear entropies that satisfy the criteria required for a violation of the appropriate high-dimensional Bell inequality. Our results therefore precisely characterize the nature of the entanglement, thus establishing the suitability of such states for applications in quantum information science.

Physical Review A•Dec 2, 2011•163 citations
Accurately establishing the state of large-scale quantum systems is an important tool in quantum information science; however, the large number of unknown parameters hinders the rapid characterisation of such states, and reconstruction procedures can become prohibitively time-consuming. Compressive sensing, a procedure for solving inverse problems by incorporating prior knowledge about the form of the solution, provides an attractive alternative to the problem of high-dimensional quantum state characterisation. Using a modified version of compressive sensing that incorporates the principles of singular value thresholding, we reconstruct the density matrix of a high-dimensional two-photon entangled system. The dimension of each photon is equal to d = 17, corresponding to a system of 83521 unknown real parameters. Accurate reconstruction is achieved with approximately 2500 measurements, only 3% of the total number of unknown parameters in the state. The algorithm we develop is fast, computationally inexpensive, and applicable to a wide range of quantum states, thus demonstrating compressive sensing as an effective technique for measuring the state of large-scale quantum systems.
Quantum state tomography (QST) is an executable procedure, allowing complete reconstruction of a quantum state. As a basic tool, QST is widely employed in quantum information research. However, many projective measurements on identical copies of the quantum state under test are required in standard QST, which is heavily time-consumed. Here, we perform reconstruction of density matrix with dimensionality of 15×1515\times 1515×15 by utilizing compressed sensing method at a sampling ratio of 0.35. According to the experimental results, the fidelity could be as high as 93.0%~99.4% and 73.8% for coherent laser beams and heralded single photon sources, respectively.
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.
Two-photon entangled quantum states are a fundamental tool for quantum information and quantum cryptography. A complete description of a generic quantum state is provided by its density matrix: the technique allowing experimental reconstruction of the density matrix is called quantum state tomography. Entangled states density matrix reconstruction requires a large number of measurements on many identical copies of the quantum state. An alternative way of certifying the amount of entanglement in two-photon states is represented by the estimation of specific parameters, e.g., negativity and concurrence. If we have a priori partial knowledge of our state, it's possible to develop several estimators for these parameters that require lower amount of measurements with respect to full density matrix reconstruction. The aim of this work is to introduce and test different estimators for negativity and concurrence for a specific class of two-photon states.
Quantum state tomography (QST) is a crucial technique for characterizing the state of quantum systems. However, traditional QST requires a large number of measurements, leading to significant experimental overhead in terms of time, resources, and data acquisition. This work proposes a novel approach to QST by leveraging compressed sensing (CS) techniques. CS allows for accurate reconstruction of the quantum state from a significantly reduced set of measurements, thereby mitigating the experimental burden. We employ sparsity regularization, a key component of CS, to minimize the number of measurements needed. The theoretical framework and the underlying principles are presented, focusing on the mathematical formulation of the problem and the optimization strategies involved. This method offers a pathway to more efficient and cost-effective QST, particularly for complex quantum states or when limited experimental resources are available. We demonstrate the potential of this approach through a detailed mathematical analysis.
Quantum state tomography (QST) is a fundamental task in quantum information science that aims to reconstruct unknown quantum states from measurement data. However, the exponential growth of Hilbert-space dimension with system size makes full tomography of general quantum states statistically and computationally prohibitive. This challenge has motivated extensive research on structured quantum state tomography, where prior structure, such as low-rankness, tensor-network representations, shallow quantum circuits, and neural quantum states, can substantially reduce the effective degrees of freedom and enable scalable recovery. In this review, we provide a unified perspective on QST for structured quantum states through three closely related themes: compact state representations, measurement design, and computational algorithms. After reviewing common models for structured quantum states, we survey existing work on geometric preservation properties of measurement frameworks, ranging from informationally complete POVMs to randomized measurements, and their implications for sample complexity. On the algorithmic side, we review optimization methods for reconstructing structured quantum states from empirical measurements. By connecting QST with broader principles from compressive sensing, matrix sensing, and structured inverse problems, this survey highlights common theoretical foundations underlying sample complexity, measurement efficiency, and scalable recovery.
Systems entangled in high dimensions have recently been proposed as important tools for various quantum information protocols, such as multibit quantum key distribution and loophole-free tests of nonlocality. It is therefore important to have precise knowledge of the nature of such entangled quantum states. We tomographically reconstruct the quantum state of the two photons produced by parametric downconversion that are entangled in a ddd-dimensional orbital angular momentum basis. We determine exactly the density matrix of the entangled two-qudddit state with ddd ranging from 2 to 8. The recording of higher-dimensional states is limited only by the number of data points required and therefore the length of time needed to complete the measurements. We find all the measured states to have fidelities and linear entropies that satisfy the criteria required for a violation of the appropriate high-dimensional Bell inequality. Our results therefore precisely characterize the nature of the entanglement, thus establishing the suitability of such states for applications in quantum information science.