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Matteo Puviani1, Sangkha Borah1,2, Remmy Zen1

  • 1Max Planck Institute for the Science of Light, 91058 Erlangen, Germany.

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This summary is machine-generated.

This study introduces a novel quantum error correction (QEC) scheme using recurrent neural networks and memory. This non-Markovian approach significantly improves the performance of bosonic codes, outperforming current strategies.

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Area of Science:

  • Quantum Information Science
  • Quantum Computing
  • Quantum Error Correction

Background:

  • Bosonic codes encode logical qubits in harmonic oscillators, leveraging large Hilbert spaces.
  • The Gottesman-Kitaev-Preskill code shows promising error correction capabilities beyond passive encoding.
  • Existing quantum error correction (QEC) protocols for bosonic codes rely on feedback from single, latest measurement outcomes.

Purpose of the Study:

  • To develop an advanced QEC scheme for bosonic codes that utilizes the full history of measurement outcomes.
  • To implement a memory-based, non-Markovian QEC strategy for improved performance.

Main Methods:

  • Utilized the feedback-GRAPE (gradient-ascent pulse engineering with feedback) method.
  • Trained a recurrent neural network to process historical measurement data.
  • Developed a QEC scheme responding non-Markovianly to the full measurement history.

Main Results:

  • The trained recurrent neural network provides a QEC scheme that significantly outperforms current strategies.
  • The new QEC approach optimizes subsequent unitary operations based on historical data.
  • Demonstrated a powerful measurement-based QEC protocol for bosonic systems.

Conclusions:

  • The developed memory-based QEC scheme offers a substantial improvement over existing methods for bosonic codes.
  • This work paves the way for more advanced and powerful measurement-based QEC protocols.
  • Highlights the potential of recurrent neural networks in advancing quantum error correction.