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Synthesis of quantum circuits based on supervised learning and correlations

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Abstract

This work introduces the use of a variational quantum circuit in order to perform unitary decomposition of a quantum operator. By use of classical optimization techniques and exploiting correlations and entanglement, the variational quantum circuit is able to translate a wide range of quantum algorithms, including the Toffoli gate and random unitary. A case study is also presented, where this decomposition is used to decompose a unitary matrix arise from a classification task.

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Data availability

The datasets generated during and/or analyzed during the current study are available in the UCI machine learning repository, http://archive.ics.uci.edu/ml from University of California, Irvine, School of Information and Computer Sciences.

Notes

  1. The decomposition does not directly address the classification problem; instead, it decompose a matrix derived from a classical pre-analysis.

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Acknowledgements

We acknowledge the use of IBM quantum services for this work. The views expressed are those of the authors and do not reflect the official policy or position of IBM or the IBM quantum team.

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Author notes

  1. André Fonseca de Olivera and Efrain Buksman have contributed equally to this work.

Authors and Affiliations

  1. Universidad ORT Uruguay, Cuareim 1451, 11100, Montevideo, Uruguay

    Carolina Allende, André Fonseca de Olivera & Efrain Buksman

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  1. Carolina Allende
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  2. André Fonseca de Olivera
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  3. Efrain Buksman
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Correspondence to Efrain Buksman.

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Allende, C., de Olivera, A.F. & Buksman, E. Synthesis of quantum circuits based on supervised learning and correlations. Quantum Inf Process 23, 204 (2024). https://doi.org/10.1007/s11128-024-04426-6

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