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有限门量子电路的线性性质的高效学习
Yuxuan Du1, Min-Hsiu Hsieh2, Dacheng Tao3
1College of Computing and Data Science, Nanyang Technological University, Singapore, Singapore. duyuxuan123@gmail.com.
Nature communications
|April 22, 2025
概括
学习量子电路的线性特性需要样本的复杂度是线性的,但计算复杂度可以是指数的. 一种新的核心方法平衡了量子学习和认证的准确性和开销.
科学领域:
- 量子计算是一种量子计算.
- 量子信息理论 量子信息理论
- 机器学习 机器学习
背景情况:
- 许多量子比特系统的复杂性阻碍了经典模拟和量子断层学.
- 量子学习理论研究了量子性质的高效学习.
研究的目的:
- 为了确定量子电路的线性性质是否可以从测量数据中有效地学习.
- 开发一种方法来学习这些属性,尽管存在计算方面的挑战.
主要方法:
- 证明学习线性属性的样本复杂性要求.
- 开发一种基于内核的方法,使用经典的影子和截断的三角形扩展.
- 进行数值模拟以进行验证.
主要成果:
- 样本复杂度与d (门数) 对小预测误差进行线性扩展.
- 计算复杂性可以随着d的指数级扩展.
- 拟议的核心方法提供了可控制的预测准确性和计算成本之间的权衡.
结论:
- 通过适当的方法,可以有效地学习线性量子电路属性.
- 拟议的方法推进了实用的量子算法和量子系统认证.
- 在多种量子计算场景中得到验证,最高可达60个量子比特.
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