浅的阴影:使用低深随机克利福德电路进行预期估计
Christian Bertoni1, Jonas Haferkamp1, Marcel Hinsche1
1Dahlem Center for Complex Quantum Systems, <a href="https://ror.org/046ak2485">Freie Universität Berlin</a>, Germany.
Physical review letters
|July 29, 2024
概括
我们开发了一个新的量子状态学习方案,使用随机测量和随机量子电路. 这种方法可以在较少的测量中高效准确地估计量子属性.
科学领域:
- 量子信息科学 量子信息科学
- 量子计算是一种量子计算.
- 量子多体物理学 量子多体物理学
背景情况:
- 学习量子状态的特性对于量子信息处理至关重要.
- 像古典影子这样的现有方法在样本复杂性和实验可行性方面存在局限.
- 通过最小的测量有效地描述量子状态仍然是一个关键的挑战.
研究的目的:
- 提出一种新的,实用的和强大的学习量子状态属性的方案.
- 开发一个随机测量方案,由随机量子电路的深度调制.
- 为了提高性能和可行性,在现有的经典影子方案之间进行插入.
主要方法:
- 在一个空间维度中使用深度调制的随机测量方案.
- 在电路深度与系统大小对数推移的模式下分析该方案.
- 使用来自影子估计,随机电路和张量网络的工具.
- 开发估计预期值和界定影子规范的方法.
主要成果:
- 拟议方案保留了极端经典影子方案的可取样本复杂性属性.
- 该方法被证明是实验上可行的.
- 通过计算深度调制阴影标准的上限,对输出估计的准确性提供了严格的保证.
结论:
- 深度调制的随机测量方案为量子状态学习提供了一种高效和准确的方法.
- 这项工作弥合了量子状态表征中的理论效率和实验实用性之间的差距.
- 这些发现有助于量子状态断层扫描和特征技术的进步.
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