Quantum Machine Learning: What Quantum Computing Means to Data Mining(paperback)

Peter Wittek

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商品描述

Quantum Machine Learning bridges the gap between abstract developments in quantum computing and the applied research on machine learning. Paring down the complexity of the disciplines involved, it focuses on providing a synthesis that explains the most important machine learning algorithms in a quantum framework. Theoretical advances in quantum computing are hard to follow for computer scientists, and sometimes even for researchers involved in the field. The lack of a step-by-step guide hampers the broader understanding of this emergent interdisciplinary body of research.

Quantum Machine Learning sets the scene for a deeper understanding of the subject for readers of different backgrounds. The author has carefully constructed a clear comparison of classical learning algorithms and their quantum counterparts, thus making differences in computational complexity and learning performance apparent. This book synthesizes of a broad array of research into a manageable and concise presentation, with practical examples and applications.

  • Bridges the gap between abstract developments in quantum computing with the applied research on machine learning
  • Provides the theoretical minimum of machine learning, quantum mechanics, and quantum computing
  • Gives step-by-step guidance to a broader understanding of this emergent interdisciplinary body of research

商品描述(中文翻譯)

《量子機器學習》將量子計算的抽象發展與機器學習的應用研究之間的鴻溝拉近。簡化所涉學科的複雜性,它專注於提供一種在量子框架下解釋最重要的機器學習算法的綜合方法。量子計算的理論進展對於計算機科學家來說很難理解,甚至對於從事該領域研究的人來說也是如此。缺乏一個逐步指南阻礙了對這一新興跨學科研究領域的更廣泛理解。

《量子機器學習》為不同背景的讀者打下了更深入理解這一主題的基礎。作者精心構建了對比傳統學習算法和它們的量子對應物的清晰比較,從而顯示了計算複雜性和學習性能上的差異。本書將廣泛的研究綜合成一個可管理且簡潔的呈現,並提供實際的例子和應用。

本書的特點包括:
- 將量子計算的抽象發展與機器學習的應用研究相結合
- 提供機器學習、量子力學和量子計算的理論最低要求
- 提供逐步指南,以更廣泛地理解這一新興跨學科研究領域