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MITS: A Quantum Sorcerer's Stone for Designing Surface Codes.

Avimita Chatterjee1, Debarshi Kundu1, Swaroop Ghosh2

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Entropy (Basel, Switzerland)
|August 28, 2025
PubMed
Summary

Optimizing quantum error correction (QEC) requires efficient parameter tuning. A new reverse engineering tool, MITS, automatically finds optimal QEC settings, minimizing resource usage for quantum computing.

Keywords:
distancelogical error ratemachine learningphysical error ratequantum error correction codesroundssurface codesthreshold

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Area of Science:

  • Quantum Computing
  • Quantum Information Science

Background:

  • Quantum Error Correction (QEC) parameter optimization is vital due to diverse physical noise in quantum computers.
  • Traditional forward simulation methods for deriving logical error rates can be resource-intensive.
  • Manual adjustment of QEC parameters is inefficient due to daily quantum error rate fluctuations.

Purpose of the Study:

  • To introduce MITS, a novel reverse engineering tool for STIM.
  • To automate the determination of optimal QEC parameters based on specific quantum hardware noise models and target logical error rates.
  • To minimize qubit and gate utilization by aligning QEC settings with hardware constraints.

Main Methods:

  • Developed MITS, a reverse engineering tool that interfaces with STIM.
  • Investigated various heuristic and machine learning models for parameter optimization.
  • Utilized XGBoost and Random Forest regression models.

Main Results:

  • MITS automatically determines optimal QEC settings for given noise models and target error rates.
  • XGBoost and Random Forest models demonstrated high effectiveness, achieving Pearson correlation coefficients of 0.98 and 0.96, respectively.
  • The approach minimizes qubit and gate usage by precisely matching logical error rates with hardware constraints.

Conclusions:

  • MITS offers an efficient solution for optimizing QEC parameters in quantum computing.
  • Automated parameter tuning using machine learning significantly improves resource efficiency.
  • This method is crucial for advancing practical quantum error correction strategies.