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:1602.04194 · Physical Review Letters

Experimental Demonstration of Self-Guided Quantum Tomography

Robert J. Chapman, Christopher Ferrie, Alberto Peruzzo

Published Jul 21, 2016• 69 citations
arXivHTMLPDF

Abstract

Traditional methods of quantum state characterization are impractical for systems of more than a few qubits due to exponentially expensive postprocessing and data storage and lack robustness against errors and noise. Here, we experimentally demonstrate self-guided quantum tomography performed on polarization photonic qubits. The quantum state is iteratively learned by optimizing a projection measurement without any data storage or postprocessing. We experimentally demonstrate robustness against statistical noise and measurement errors on single-qubit and entangled two-qubit states.

DOI
10.1103/physrevlett.117.040402

Related papers

Self-Guided Quantum Tomography

Christopher Ferrie

We introduce a self-learning tomographic technique in which the experiment guides itself to an estimate of its own state. Self-guided quantum tomography uses measurements to directly test hypotheses in an iterative algorithm which converges to the true state. We demonstrate through simulation on many qubits that Self-guided quantum tomography is a more efficient and robust alternative to the usual paradigm of taking a large amount of informationally complete data and solving the inverse problem of postprocessed state estimation.

arXiv:1406.4101•Physical Review Letters•Nov 7, 2014•124 citations

Robust and Efficient High-Dimensional Quantum State Tomography

Markus Rambach, Mahdi Qaryan, Michael J. Kewming +3 more

The exponential growth in Hilbert space with increasing size of a quantum system means that accurately characterizing the system becomes significantly harder with system dimension d. We show that self-guided tomography is a practical, efficient, and robust technique of measuring higher-dimensional quantum states. The achieved fidelities are over 99.9% for qutrits (d=3) and ququints (d=5), and 99.1% for quvigints (d=20)-the highest values ever realized for qudit pure states. We also show excellent performance for mixed states, achieving average fidelities of 96.5% for qutrits. We demonstrate robustness against experimental sources of noise, both statistical and environmental. The technique is applicable to any higher-dimensional system, from a collection of qubits through to individual qudits, and any physical realization, be it photonic, superconducting, ionic, or spin.

arXiv:2010.00632•Physical Review Letters•Mar 10, 2021•77 citations

High-Dimensional Self-Guided Quantum Tomography of Laguerre-Gaussian Modes

Ruomin Bi, Dawei Lyu, Qianlei Liu +3 more

We experimentally demonstrate self-guided quantum tomography (SGQT) for states encoded by Laguerre-Gaussian (LG) modes. By iteratively learning the unknown quantum states from adaptively optimized projection measurements, we experimentally achieve a reconstruction fidelity of more than 97.3% in eight-dimensional systems.

•Jan 1, 2026•0 citations

Single-acquisition tomography of photonic qubits with structured media

Francesco Di Colandrea, Yingwen Zhang, John Grace +3 more

Quantum state tomography is an essential tool for characterizing quantum systems and underpins nearly every experimental realization of quantum technologies. Conventional tomography relies on performing a sequence of projective measurements on many identical copies of a quantum state, requiring the measurement apparatus to be reconfigured between successive acquisitions. As the Hilbert-space dimension increases, the number of required measurements grows rapidly; in practice, additional overcomplete measurements are often performed to improve robustness to experimental imperfections. Here, we introduce a tomography platform based on structured anisotropic media that performs informationally complete measurements of photonic polarization qubits within a single acquisition. The approach employs three liquid-crystal metasurfaces with spatially varying optic-axis orientations that transform the input polarization into a far-field distribution of discrete transverse-momentum modes. Each diffraction pattern uniquely determines the polarization state, enabling its reconstruction without sequential changes to the measurement apparatus. Unlike previous implementations, our scheme is intrinsically photon-number independent: the same optical device operates identically for arbitrary photon numbers, while the desired photon-number sector can be selected afterwards through post-selection of the corresponding $n$-fold coincidence events. We experimentally demonstrate single-frame quantum state tomography of both single- and two-photon polarization states, providing a simple and scalable route toward efficient quantum-state characterization.

quant-pharXiv:2607.03052•Jul 6, 2026•0 citations

Experimental realization of self-guided quantum process tomography

Zhibo Hou, Jun-Feng Tang, Christopher Ferrie +3 more

Characterization of quantum processes is a preliminary step necessary in the development of quantum technology. The conventional method uses standard quantum process tomography, which requires ${d}^{2}$ input states and ${d}^{4}$ quantum measurements for a $d$-dimensional Hilbert space. These experimental requirements are compounded by the complexity of processing the collected data, which can take several orders of magnitude longer than the experiment itself. In this paper we propose an alternative self-guided algorithm for quantum process tomography, tuned for the task of finding an unknown unitary process. Our algorithm is a fully automated and adaptive process characterization technique. The advantages of our algorithm are the following: it has an inherent robustness to both statistical and technical noise; it requires less space and time since there is no postprocessing of the data; it requires only a single input state and measurement; and it provides on-the-fly diagnostic information while the experiment is running. Numerical results show our algorithm achieves the same $1/n$ scaling as standard quantum process tomography when $n$ uses of the unknown process are used. We also present experimental results wherein the algorithm and its advantages are realized for the task of finding an element of SU(2).

arXiv:1908.01082•Physical Review A•Feb 18, 2020•25 citations

Self-Guided Quantum Tomography in High-Dimensional Systems

Laura Serino, Markus Rambach, Benjamin Brecht +3 more

Quantum tomography-characterising a quantum system by assigning it a quantum state-is an essential building block for future technologies like quantum metrology or quantum computation. Ideal quantum tomography is accurate and robust to noise while being quick and efficient. The standard technique for decades, full quantum tomography (FQT), only fulfils parts of these criteria and quickly becomes unfeasible, even for medium-sized systems. As the number of possible states increases, the amount of measurements to characterise the system grows exponentially. The very feature that gives quantum physics immense possibilities hinders our ability to describe it. Additionally, FQT is neither robust to noise nor experimental errors and generally fails in the presence of these environmental perturbations unless additional resources are used.

Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC)•Jun 23, 2025•0 citations
Traditional methods of quantum state characterization are impractical for systems of more than a few qubits due to exponentially expensive postprocessing and data storage and lack robustness against errors and noise. Here, we experimentally demonstrate self-guided quantum tomography performed on polarization photonic qubits. The quantum state is iteratively learned by optimizing a projection measurement without any data storage or postprocessing. We experimentally demonstrate robustness against statistical noise and measurement errors on single-qubit and entangled two-qubit states.
We introduce a self-learning tomographic technique in which the experiment guides itself to an estimate of its own state. Self-guided quantum tomography uses measurements to directly test hypotheses in an iterative algorithm which converges to the true state. We demonstrate through simulation on many qubits that Self-guided quantum tomography is a more efficient and robust alternative to the usual paradigm of taking a large amount of informationally complete data and solving the inverse problem of postprocessed state estimation.
The exponential growth in Hilbert space with increasing size of a quantum system means that accurately characterizing the system becomes significantly harder with system dimension d. We show that self-guided tomography is a practical, efficient, and robust technique of measuring higher-dimensional quantum states. The achieved fidelities are over 99.9% for qutrits (d=3) and ququints (d=5), and 99.1% for quvigints (d=20)-the highest values ever realized for qudit pure states. We also show excellent performance for mixed states, achieving average fidelities of 96.5% for qutrits. We demonstrate robustness against experimental sources of noise, both statistical and environmental. The technique is applicable to any higher-dimensional system, from a collection of qubits through to individual qudits, and any physical realization, be it photonic, superconducting, ionic, or spin.
Quantum state tomography is an essential tool for characterizing quantum systems and underpins nearly every experimental realization of quantum technologies. Conventional tomography relies on performing a sequence of projective measurements on many identical copies of a quantum state, requiring the measurement apparatus to be reconfigured between successive acquisitions. As the Hilbert-space dimension increases, the number of required measurements grows rapidly; in practice, additional overcomplete measurements are often performed to improve robustness to experimental imperfections. Here, we introduce a tomography platform based on structured anisotropic media that performs informationally complete measurements of photonic polarization qubits within a single acquisition. The approach employs three liquid-crystal metasurfaces with spatially varying optic-axis orientations that transform the input polarization into a far-field distribution of discrete transverse-momentum modes. Each diffraction pattern uniquely determines the polarization state, enabling its reconstruction without sequential changes to the measurement apparatus. Unlike previous implementations, our scheme is intrinsically photon-number independent: the same optical device operates identically for arbitrary photon numbers, while the desired photon-number sector can be selected afterwards through post-selection of the corresponding nnn-fold coincidence events. We experimentally demonstrate single-frame quantum state tomography of both single- and two-photon polarization states, providing a simple and scalable route toward efficient quantum-state characterization.
Characterization of quantum processes is a preliminary step necessary in the development of quantum technology. The conventional method uses standard quantum process tomography, which requires d2{d}^{2}d2 input states and d4{d}^{4}d4 quantum measurements for a ddd-dimensional Hilbert space. These experimental requirements are compounded by the complexity of processing the collected data, which can take several orders of magnitude longer than the experiment itself. In this paper we propose an alternative self-guided algorithm for quantum process tomography, tuned for the task of finding an unknown unitary process. Our algorithm is a fully automated and adaptive process characterization technique. The advantages of our algorithm are the following: it has an inherent robustness to both statistical and technical noise; it requires less space and time since there is no postprocessing of the data; it requires only a single input state and measurement; and it provides on-the-fly diagnostic information while the experiment is running. Numerical results show our algorithm achieves the same 1/n1/n1/n scaling as standard quantum process tomography when nnn uses of the unknown process are used. We also present experimental results wherein the algorithm and its advantages are realized for the task of finding an element of SU(2).
Quantum tomography-characterising a quantum system by assigning it a quantum state-is an essential building block for future technologies like quantum metrology or quantum computation. Ideal quantum tomography is accurate and robust to noise while being quick and efficient. The standard technique for decades, full quantum tomography (FQT), only fulfils parts of these criteria and quickly becomes unfeasible, even for medium-sized systems. As the number of possible states increases, the amount of measurements to characterise the system grows exponentially. The very feature that gives quantum physics immense possibilities hinders our ability to describe it. Additionally, FQT is neither robust to noise nor experimental errors and generally fails in the presence of these environmental perturbations unless additional resources are used.