变量量子和量子启发的聚类
Pablo Bermejo1,2, Román Orús3,4,5
1Multiverse Computing, Paseo de Miramón 170, 20014, San Sebastián, Spain.
Scientific reports
|August 16, 2023
概括
我们使用变量量子电路开发了一种量子聚类算法. 这种方法有效地将数据分类为许多集群,即使在小型量子设备上,也显示出仅用一个量子比特的强大性能.
科学领域:
- 量子计算是一种量子计算.
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 聚类是数据分析的基本机器学习任务.
- 变量量子电路为近期量子计算提供了一个有希望的途径.
- 现有的量子聚类算法往往需要大量的量子位资源.
研究的目的:
- 介绍一个新的量子算法用于数据聚类.
- 为了证明该算法的适用性在杂的中间尺度量子 (NISQ) 设备上.
- 探索非对角量子位状态的使用,以增强集群能力.
主要方法:
- 该算法将数据聚类简化为一个优化问题.
- 它采用一个变量量子Eigensolver (VQE) 与非直角量子比特状态.
- 使用最大直角状态,而不是使用标准计算基础.
主要成果:
- 该算法成功将数据分类为多个集群.
- 通过对真实数据集的数值模拟来证明出色的性能,即使使用单个量子比特.
- 一个张量网络模拟产生了一个量子启发的集群算法可在经典硬件上执行.
结论:
- 这种基于变量量子电路的算法为数据集群提供了一种高效的方法.
- 该方法适合在当前的NISQ设备上实现.
- 使用非直角状态可以处理大量集群,但量子比特有限.
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