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Efficient Near-Optimal Decoding of the Surface Code through Ensembling.

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Harmonization, an ensembling method, combines noisy decoders to improve decoding accuracy. This technique reduces logical error rates and offers a path toward highly accurate real-time decoding for quantum error correction.

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Area of Science:

  • Quantum Information Science
  • Computer Science

Background:

  • Accurate decoding is crucial for fault-tolerant quantum computation.
  • Existing decoders often struggle with high logical error rates on complex quantum error-correcting codes.
  • Ensembling methods offer potential improvements but can be computationally expensive.

Purpose of the Study:

  • To introduce and evaluate a novel ensembling method called harmonization for improving decoding accuracy.
  • To assess the effectiveness of harmonization on repetition and surface codes.
  • To develop a confidence measure for decoding based on ensemble consensus.

Main Methods:

  • Harmonization combines multiple "noisy" decoders to produce more accurate predictions.
  • Ensembles of minimum weight perfect matching-based decoders were benchmarked.
  • A layered decoding scheme was proposed, utilizing ensemble consensus as a confidence measure.

Main Results:

  • Harmonized ensembles achieved lower logical error rates compared to individual decoders on repetition and surface codes.
  • Accuracy approached maximum-likelihood levels with increasing ensemble sizes.
  • The layered scheme demonstrated significant accuracy gains with manageable computational overhead.

Conclusions:

  • Harmonization is a viable ensembling technique for enhancing decoder accuracy in quantum error correction.
  • The method provides a practical approach to achieving high-fidelity, real-time decoding.
  • Ensemble consensus serves as an effective confidence metric for adaptive decoding strategies.