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A new neural network decoder significantly improves quantum error correction by accurately interpreting noisy data from quantum computers. This machine learning approach enhances the reliability of quantum computations and aids in building large-scale quantum systems.

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Area of Science:

  • Quantum computing
  • Quantum error correction
  • Machine learning

Background:

  • Quantum error correction is crucial for building large-scale quantum computers.
  • Quantum error-correction codes encode information redundantly across multiple qubits.
  • Accurate decoding of noisy syndrome information is a key challenge.

Purpose of the Study:

  • To develop a machine learning-based decoder for the surface code, a leading quantum error-correction code.
  • To improve the accuracy of decoding noisy syndrome information for quantum computation.

Main Methods:

  • Developed a recurrent, transformer-based neural network.
  • Trained the network on simulated and real-world data from Google's Sycamore quantum processor.
  • Utilized soft readouts and leakage information for enhanced decoding.

Main Results:

  • The neural network decoder outperformed state-of-the-art decoders on real-world data for distance-3 and distance-5 surface codes.
  • Maintained performance advantage on simulated data up to distance 11 with realistic noise.
  • Demonstrated adaptation to unknown error distributions using experimental samples.

Conclusions:

  • Machine learning can surpass human-designed algorithms in quantum error decoding.
  • The developed decoder shows strong potential for practical application in quantum computers.
  • This work highlights the power of data-driven approaches in advancing quantum technologies.