学习量子处理器的高精度错误解码
Johannes Bausch1, Andrew W Senior2, Francisco J H Heras3
1Google DeepMind, London, UK. jbausch@google.com.
Nature
|November 20, 2024
概括
一个新的神经网络解码器通过准确地解释量子计算机的噪音数据, 显著改善了量子错误的纠正. 这种机器学习方法提高了量子计算的可靠性,并有助于构建大型量子系统.
科学领域:
- 量子计算
- 量子错误的纠正
- 机器学习
背景情况:
- 量子错误纠正对于构建大型量子计算机至关重要.
- 量子错误纠正代码在多个量子位上冗余地编码信息.
- 精确解码噪音综合征信息是一个关键挑战.
研究的目的:
- 开发基于机器学习的表面代码解码器,
- 为了提高解码噪声综合征信息的准确性,
主要方法:
- 开发了一个循环,基于变压器的神经网络.
- 在模拟和真实世界数据上训练网络,
- 使用软读取和泄露信息进行增强解码.
主要成果:
- 神经网络解码器在距离-3和距离-5表面代码上的真实数据中超越了最先进的解码器.
- 在模拟数据上保持性能优势,直至距离11以现实的噪声.
- 使用实验样本证明适应未知的错误分布.
结论:
- 机器学习可以在量子错误解码方面超越人类设计的算法.
- 开发的解码器在量子计算机中具有很强的应用潜力.
- 这项工作突显了数据驱动的方法在量子技术进步中的力量.
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