Quantum Machine Learning: An Applied Approach: The Theory and Application of Quantum Machine Learning in Science and Industry

Santanu Ganguly

  • 出版商: Apress
  • 出版日期: 2021-07-30
  • 售價: $1,870
  • 貴賓價: 9.5$1,777
  • 語言: 英文
  • 頁數: 551
  • 裝訂: Paperback
  • ISBN: 1484270975
  • ISBN-13: 9781484270974
  • 相關分類: Machine Learning量子 Quantum
  • 立即出貨 (庫存=1)



Know how to adapt quantum computing and machine learning algorithms. This book takes you on a journey into hands-on quantum machine learning (QML) through various options available in industry and research.

The first three chapters offer insights into the combination of the science of quantum mechanics and the techniques of machine learning, where concepts of classical information technology meet the power of physics. Subsequent chapters follow a systematic deep dive into various quantum machine learning algorithms, quantum optimization, applications of advanced QML algorithms (quantum k-means, quantum k-medians, quantum neural networks, etc.), qubit state preparation for specific QML algorithms, inference, polynomial Hamiltonian simulation, and more, finishing with advanced and up-to-date research areas such as quantum walks, QML via Tensor Networks, and QBoost.

Hands-on exercises from open source libraries regularly used today in industry and research are included, such as Qiskit, Rigetti's Forest, D-Wave's dOcean, Google's Cirq and brand new TensorFlow Quantum, and Xanadu's PennyLane, accompanied by guided implementation instructions. Wherever applicable, the book also shares various options of accessing quantum computing and machine learning ecosystems as may be relevant to specific algorithms.

The book offers a hands-on approach to the field of QML using updated libraries and algorithms in this emerging field. You will benefit from the concrete examples and understanding of tools and concepts for building intelligent systems boosted by the quantum computing ecosystem. This work leverages the author's active research in the field and is accompanied by a constantly updated website for the book which provides all of the code examples.


What You will Learn

  • Understand and explore quantum computing and quantum machine learning, and their application in science and industry
  • Explore various data training models utilizing quantum machine learning algorithms and Python libraries
  • Get hands-on and familiar with applied quantum computing, including freely available cloud-based access
  • Be familiar with techniques for training and scaling quantum neural networks
  • Gain insight into the application of practical code examples without needing to acquire excessive machine learning theory or take a quantum mechanics deep dive


Who This Book Is For
Data scientists, machine learning professionals, and researchers






書中包含了來自當今行業和研究中常用的開源庫的實踐性練習,如Qiskit、Rigetti的Forest、D-Wave的dOcean、Google的Cirq和全新的TensorFlow Quantum,以及Xanadu的PennyLane,並附有指導性的實施說明。在適用的情況下,本書還分享了訪問量子計算和機器學習生態系統的各種選項,以滿足特定演算法的需求。



- 了解和探索量子計算和量子機器學習,以及它們在科學和行業中的應用
- 探索使用量子機器學習演算法和Python庫的各種數據訓練模型
- 進行實際的量子計算實踐,包括免費的基於雲端的訪問
- 熟悉訓練和擴展量子神經網絡的技術
- 瞭解實際程式碼示例的應用,無需過多機器學習理論或深入量子力學




Santanu Ganguly has been working in the fields of quantum technologies, cloud computing, data networking, and security (on research, design, and delivery) for over 21 years. He works in Switzerland and the United Kingdom (UK) for various Silicon Valley vendors and ISPs. He has two postgraduate degrees (one in mathematics and another in observational astrophysics), and research experience and publications in nanoscale photonics and laser spectroscopy. He is currently leading global projects out of the UK related to quantum communication and machine learning, among other technologies.


Santanu Ganguly在量子技術、雲端運算、資料網路和安全領域已經從事研究、設計和交付工作超過21年。他在瑞士和英國為多家矽谷供應商和互聯網服務提供商工作。他擁有兩個研究生學位(一個是數學,另一個是天文觀測學),並在納米級光子學和激光光譜學方面有研究經驗和發表論文。他目前在英國領導與量子通信和機器學習等技術相關的全球項目。