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<p>This work is devoted to solving the problem of processing numerical sequences using variational quantum algorithms implemented on a noisy intermediate-scale quantum computer. The quantum computing platform is an integrated circuit based on superconducting artificial atoms. The architecture of the quantum recurrent neural network is constructed using single- and two-qubit operations. At the simulation stage, we investigated the trainability of the model as a function of the number of qubits, as well as the encoded into the qubit states data amount and the encoding method. A comparison with classical architectures showed that on current quantum processors it is possible to achieve a prediction quality on the chosen machine-learning task that is comparable to that of models implemented on classical processors.</p>

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Training Quantum Recurrent Neural Networks on a Josephson Integrated Circuit

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月1日

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  • S. S. Samarin,
  • A. E. Tolstobrov,
  • S. V. Sanduleanu,
  • G. P. Fedorov,
  • A. A. Ryabov,
  • V. V. Vanovskiy,
  • D. A. Kalacheva,
  • A. N. Bolgar,
  • V. B. Lubsanov,
  • Sh. V. Kadyrmetov,
  • A. M. Muraviev,
  • E. S. Alekseeva,
  • P. Yu. Shlykov,
  • A. M. Yeremeyev,
  • A. V. Vasenin,
  • A. Yu. Dmitriev,
  • O. V. Astafiev

摘要

This work is devoted to solving the problem of processing numerical sequences using variational quantum algorithms implemented on a noisy intermediate-scale quantum computer. The quantum computing platform is an integrated circuit based on superconducting artificial atoms. The architecture of the quantum recurrent neural network is constructed using single- and two-qubit operations. At the simulation stage, we investigated the trainability of the model as a function of the number of qubits, as well as the encoded into the qubit states data amount and the encoding method. A comparison with classical architectures showed that on current quantum processors it is possible to achieve a prediction quality on the chosen machine-learning task that is comparable to that of models implemented on classical processors.