Optimization of Decoder Priors for Accurate Quantum Error Correction.

Area of Science:

• Quantum Information Science
• Quantum Computing
• Error Correction

Background:

• Accurate decoding of quantum error-correcting codes is essential for protecting quantum information from decoherence.
• Characterizing error channels and using this information as a prior for decoders is a key challenge.
• Current methods face limitations in optimizing decoder performance.

Purpose of the Study:

• To introduce a novel reinforcement learning-inspired method for calibrating priors in quantum error correction.
• To minimize the logical error rate in quantum computations.
• To enhance the accuracy of quantum decoders.

Main Methods:

• Developed a reinforcement learning-based approach for adaptive calibration of decoder priors.
• Applied the method to repetition and surface code memory experiments.
• Utilized Google's Sycamore quantum processor for experimental validation.

Main Results:

• Achieved significant improvements in decoding accuracy for both repetition codes (16% improvement) and surface codes (3.3% improvement).
• Outperformed the leading decoder-agnostic method in experimental tests.
• Demonstrated the effectiveness of the reinforcement learning approach in a real quantum device.

Conclusions:

• The proposed reinforcement learning-based calibration method substantially enhances quantum error correction decoding accuracy.
• This approach offers a powerful tool for optimizing the performance of current and future quantum computing hardware.
• The findings pave the way for more robust and reliable quantum information processing.