Concise Guide to Quantum Machine Learning

This book offers a brief but effective introduction to quantum machine learning (QML). QML is not merely a translation of classical machine learning techniques into the language of quantum computing, but rather a new approach to data representation and processing. Accordingly, the content is not divided into a "classical part" that describes standard machine learning schemes and a "quantum part" that addresses their quantum counterparts. Instead, to immerse the reader in the quantum realm from the outset, the book starts from fundamental notions of quantum mechanics and quantum computing. Avoiding unnecessary details, it presents the concepts and mathematical tools that are essential for the required quantum formalism. In turn, it reviews those quantum algorithms most relevant to machine learning. Later chapters highlight the latest advances in this field and discuss the most promising directions for future research.

To gain the most from this book, a basic grasp of statistics and linear algebra is sufficient; no previous experience with quantum computing or machine learning is needed. The book is aimed at researchers and students with no background in quantum physics and is also suitable for physicists looking to enter the field of QML.

本書提供了一個簡明但有效的量子機器學習（QML）介紹。QML不僅僅是將傳統機器學習技術轉化為量子計算語言，而是一種新的數據表示和處理方法。因此，內容並沒有將其分為描述標準機器學習方案的“經典部分”和涉及其量子對應的“量子部分”。相反，為了讓讀者從一開始就深入了解量子領域，本書從量子力學和量子計算的基本概念開始。避免冗長的細節，它介紹了必要的量子形式主義所需的概念和數學工具。接著，它回顧了與機器學習最相關的那些量子算法。後面的章節突出了這個領域的最新進展，並討論了未來研究的最有前景的方向。

要從本書中獲得最大的收益，只需要基本的統計學和線性代數知識；不需要有量子計算或機器學習的先前經驗。本書針對沒有量子物理背景的研究人員和學生，同時也適合物理學家進入QML領域。

Davide Pastorello is an assistant professor in the Department of Information Engineering and Computer Science at the University of Trento.

Davide Pastorello是特倫托大學資訊工程與電腦科學系的助理教授。