从不完整的测量中对量子纠进行深度学习
Dominik Koutný1, Laia Ginés2, Magdalena Moczała-Dusanowska3
1Department of Optics, Faculty of Science, Palacký University, 17. listopadu 12, 77146 Olomouc, Czechia.
Science advances
|July 19, 2023
概括
神经网络现在可以使用不完整的测量量量量子纠,与传统方法相比,大大减少错误. 这一突破简化了用于研究和应用的纠量化.
科学领域:
- 量子信息科学 量子信息科学
- 机器学习应用 机器学习应用
背景情况:
- 量化量子纠对于基础研究和先进技术至关重要.
- 目前的方法,如量子状态断层扫描,需要大量的数据或先前的系统知识.
研究的目的:
- 开发一种使用神经网络量化量子纠的新方法.
- 为了使不完整的测量数据能够量化纠.
主要方法:
- 利用在模拟数据上训练的神经网络来分析量子系统.
- 采用低样本局部测量来量化纠.
- 开发一个卷积神经网络输入,以实现设备独立.
主要成果:
- 在没有完整的量子状态描述的情况下,实现了量子相关性的直接量化.
- 与最先进的量子断层扫描相比,量化误差减少了多达一个数量级.
- 在不同的测量场景和设备中证明了稳定性.
结论:
- 神经网络为纠量化提供了一个强大的,高效的替代方案.
- 开发的方法显著降低了实验要求,提高了准确性.
- 这种方法对实际的,独立于设备的量子信息处理具有前景.
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