由-巴克斯特方程和人工神经网络介导的量子时间动力学
Sahil Gulania1, Yuri Alexeev2, Stephen K Gray3
1Mathematics and Computer Science, Argonne National Laboratory, Lemont, Illinois 60439, United States.
Journal of chemical theory and computation
|June 13, 2025
概括
本研究介绍了量子计算的人工误差缓解,使用人工神经网络 (ANN) 和-巴克斯特方程 (YBE). 这种新的方法提高了量子模拟的准确性,特别是对于杂的中级量子系统 (NISQ).
科学领域:
- 量子信息科学 量子信息科学
- 计算物理 计算物理
背景情况:
- 量子计算有望取得重大进展,但受到固有的错误的阻碍.
- 传统的量子误差缓解技术往往在计算上要求很高.
研究的目的:
- 开发一种新的,计算效率高的量子误差缓解方法.
- 提高量子模拟的准确性和真实性,特别是在NISQ设备上.
主要方法:
- 结合人工神经网络 (ANN) 用于降低噪音和-巴克斯特方程 (YBE) 用于控制噪音数据生成.
- 使用YBE来保持电路压缩和可扩展性的量子相关性和对称性.
- 训练ANN模型对部分量子模拟数据进行错误纠正.
主要成果:
- 在量子模拟中成功降低了噪声,从而提高了准确性.
- 在使用ANN的时间演变量子状态中,证明了有效的误差缓解.
- 在真实量子设备上通过对海森堡XY哈密尔顿的模拟验证了这种方法.
结论:
- 拟议的人工误差缓解框架提供了一个可扩展的解决方案,以提高量子计算的可靠性.
- 这种方法对于杂的中等尺度量子系统 (NISQ) 尤其有利.
- 对ANN和YBE的整合为推进实际量子计算提供了一个强大的工具.
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