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Quantum state tomography using quantum machine learning

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Abstract

Quantum state tomography (QST) is a fundamental technique in quantum information processing (QIP) for reconstructing unknown quantum states. However, the conventional QST methods are limited by the number of measurements required, which makes them impractical for large-scale quantum systems. To overcome this challenge, we propose the integration of quantum machine learning (QML) techniques to enhance the efficiency of QST. In this paper, we conduct a comprehensive investigation into various approaches for QST, encompassing both classical and quantum methodologies. We also implement different QML approaches for QST and demonstrate their effectiveness on various simulated and experimental quantum systems, including multi-qubit networks. Our results show that our QML-based QST approach can achieve high fidelity (\(98 \%\)) with significantly fewer measurements than conventional methods, making it a promising tool for practical QIP applications.

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Availability of data and materials

The datasets and numerical details necessary to replicate this work are available from the corresponding author upon reasonable request.

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Authors and Affiliations

  1. Quantum Physics and Magnetism Team, LPMC, Faculty of Sciences Ben M’sick, Hassan II University of Casablanca, Casablanca, Morocco

    Nouhaila Innan & Mohamed Bennai

  2. Quantum Formalism Fellow, Zaiku Group Ltd, Liverpool, UK

    Nouhaila Innan & Muhammad Al-Zafar Khan

  3. Department of Physics, COMSATS University Islamabad, 45550, Islamabad, Pakistan

    Owais Ishtiaq Siddiqui

  4. Technical University of Munich, Munich, Germany

    Shivang Arora

  5. Department of Physics, Indian Institute of Technology Madras, 600036, Chennai, Tamil Nadu, India

    Tamojit Ghosh

  6. Computer Technology Department, Istanbul University-Cerrahpaşa, Istanbul, Turkey

    Yasemin Poyraz Koçak

  7. University of California Berkeley, Berkeley, CA, USA

    Dominic Paragas

  8. Independent Researcher, Dhaka, Bangladesh

    Abdullah Al Omar Galib

  9. Robotics, Autonomous Intelligence, and Learning Laboratory (RAIL), School of Computer Science and Applied Mathematics, University of the Witwatersrand, 1 Jan Smuts Ave, Braamfontein, Johannesburg, 2000, Gauteng, South Africa

    Muhammad Al-Zafar Khan

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  1. Nouhaila Innan
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  2. Owais Ishtiaq Siddiqui
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  3. Shivang Arora
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Innan, N., Siddiqui, O.I., Arora, S. et al. Quantum state tomography using quantum machine learning. Quantum Mach. Intell. 6, 28 (2024). https://doi.org/10.1007/s42484-024-00162-3

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