期限(时间)
电流(流体)
计算机科学
量子
量子机器学习
国家(计算机科学)
无监督学习
人工智能
机器学习
量子计算机
电气工程
物理
量子力学
算法
工程类
作者
Yaswitha Gujju,Atsushi Matsuo,Rudy Raymond
出处
期刊:Physical review applied
[American Physical Society]
日期:2024-06-04
卷期号:21 (6)
被引量:1
标识
DOI:10.1103/physrevapplied.21.067001
摘要
This Review focuses on the practical implications of quantum machine learning (QML) algorithms and their applicability in real-world domains such as high-energy physics, healthcare, and finance. Despite rising interest in QML, the field contends with numerous challenges, particularly in execution on real quantum devices. This comprehensive exploration of the field delves into those challenges and the proposed solutions to overcome them. The authors provide an extensive survey of different techniques in QML, from data-encoding methods to model types, and offer insight into open questions in the field from a practical standpoint.
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