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Approximate Autonomous Quantum Error Correction with Reinforcement Learning.

Yexiong Zeng1,2, Zheng-Yang Zhou1, Enrico Rinaldi1,2,3,4,5

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This study introduces a novel bosonic code for approximate autonomous quantum error correction (AQEC) using reinforcement learning. The new RL code effectively suppresses photon loss, simplifying experimental implementation for robust quantum computing.

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Area of Science:

  • Quantum Information Science
  • Quantum Computing
  • Quantum Error Correction

Background:

  • Autonomous quantum error correction (AQEC) offers a path to protect logical qubits without measurement-feedback loops.
  • Bosonic codes are promising for AQEC due to their controllability, but existing methods require complex implementations (Hamiltonian distance d≥2).

Purpose of the Study:

  • To propose and validate a bosonic code for approximate AQEC that relaxes stringent Knill-Laflamme conditions.
  • To reduce the experimental complexity of implementing AQEC in bosonic systems.

Main Methods:

  • Utilized reinforcement learning (RL) to identify an optimal set of bosonic code words.
  • Developed an error-correcting Hamiltonian with ancilla systems based on a simplified Hamiltonian distance (d=1).

Main Results:

  • Identified an RL code composed of Fock states |2⟩ and |4⟩.
  • Demonstrated effective suppression of single-photon loss, reducing it to a manageable dephasing process.
  • Achieved a significant reduction in model complexity compared to previous methods.

Conclusions:

  • The proposed approximate AQEC bosonic code offers a practical approach to quantum error suppression.
  • This simplified approach, based on d=1 Hamiltonian distance, is a promising building block for scalable quantum computers.