Authors: Yue Zhao

Published on: November 18, 2023

Impact Score: 7.0

Arxiv code: Arxiv:2311.11167

Summary

• What is new: This paper introduces a machine learning-based approach to quantum error correction that specifically focuses on exploiting long-range dependencies between data qubits and ancilla qubits.
• Why this is important: Quantum computers are hindered by unreliable data qubits which require effective quantum error correction methods for stable operation.
• What the research proposes: A new machine learning benchmark is created to assess the ability of algorithms to capture long-range dependencies essential for quantum error correction, highlighting the use of ancilla qubits for improved error detection.
• Results: Experiments showed that increasing the receptive field to include distant ancilla qubits significantly improves QEC accuracy, with U-Net improving CNN’s performance by about 50%.

Technical Details

Technological frameworks used: Deep learning algorithms

Models used: Convolutional Neural Networks, Graph Neural Networks, Graph Transformers

Data used: Ancilla and data qubits information

Potential Impact

Quantum computing companies and markets offering quantum computing as a service could benefit from these insights for developing more reliable quantum computer systems.

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