.png){kind=icon}
.png){kind=icon}
Return
Quantum Machine Learning: A tutorial
.png){kind=icon}
OA
AI
DOI：10.1016/j.neucom.2021.02.102.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This tutorial provides an overview of Quantum Machine Learning (QML), a relatively novel discipline that brings together concepts from Machine Learning (ML), Quantum Computing (QC) and Quantum Information (QI). The great development experienced by QC, partly due to the involvement of giant technological companies as well as the popularity and success of ML have been responsible of making QML one of the main streams for researchers working on fuzzy borders between Physics, Mathematics and Computer Science. A possible, although arguably coarse, classification of QML methods may be based on those approaches that make use of ML in a quantum experimentation environment and those others that take advantage of QC and QI to find out alternative and enhanced solutions to problems driven by data, oftentimes offering a considerable speedup and improved performances as a result of tackling problems from a complete different standpoint. Several examples will be provided to illustrate both classes of methods. (C) 2021 The Authors. Published by Elsevier B.V.
Keywords:
Quantum computing
Computational speed -up
Quantum-inspired learning algorithms
Quantum clustering
Quantum reinforcement learning
Quantum autoencoders
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
6.5
Papers:
2.5W
Citations:
6.5W