针对特定噪声设计高效纠错码，提升量子计算容错能力。
Learning Encodings by Maximizing State Distinguishability: Variational Quantum Error Correction
- 通过最大化噪声后量子态的可区分性来优化编码电路
- 在多种噪声场景下性能优于传统表面码等标准编码
- 可在实际量子硬件上实现，适合近中期量子设备
量子误差纠正对保护量子信息免受退相干至关重要。传统编码如表面码需大量资源开销，不适用于近期早期容错设备。本文提出一种新目标函数，通过最大化噪声通道后量子态的可区分性，定制化纠错码以适配特定噪声结构，确保高效恢复操作。我们引入可区分性损失函数作为机器学习目标，用于发现针对给定噪声特性的资源高效编码电路。采用变分技术实现该方法，称为变分量子误差纠正（VarQEC）。所获编码具备良好的理论与实用性质，在多种场景下表现优于标准编码。我们在IBM和IQM硬件设备上完成了概念验证，凸显该方法的实际应用价值。
原文摘要 · Abstract (English)
Quantum error correction is crucial for protecting quantum information against decoherence. Traditional codes like the surface code require substantial overhead, making them impractical for near-term, early fault-tolerant devices. We propose a novel objective function for tailoring error correction codes to specific noise structures by maximizing the distinguishability between quantum states after a noise channel, ensuring efficient recovery operations. We formalize this concept with the distinguishability loss function, serving as a machine learning objective to discover resource-efficient encoding circuits optimized for given noise characteristics. We implement this methodology using variational techniques, termed variational quantum error correction (VarQEC). Our approach yields codes with desirable theoretical and practical properties and outperforms standard codes in various scenarios. We also provide proof-of-concept demonstrations on IBM and IQM hardware devices, highlighting the practical relevance of our procedure.
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