Quantum Machine Learning
Description
This book primarily introduces the background knowledge and fundamental concepts of quantum machine learning, as well as the basic principles and implementation of several important quantum machine learning algorithms.
It is structured into nine chapters, covering the following main topics: background knowledge of quantum machine learning, fundamentals of quantum computing, the quantum machine learning framework VQNet, support vector machines, clustering, convolutional neural networks, recurrent neural networks, generative adversarial networks, and natural language processing.
This book can serve as a reference for graduate students, teachers, and researchers in relevant fields at universities or research institutes. It is also suitable as a self-study guide for quantum machine learning enthusiasts.
More details
Persons
Guo Guoping received his Ph.D. from the University of Science and Technology of China (USTC) in 2005. He is currently a professor and also the deputy laboratory director at CAS Key Laboratory of Quantum Information, USTC, and the chief scientist at Origin Quantum Computing Technology (Hefei) Co., Ltd. He also serves as the secretary-general of the Technical Committee on Quantum Computing of the China Computer Federation (CCF) and the director of the Technical Committee on Quantum Computing of the China Institute of Communications (CIC).
Professor Guo has long been engaged in research related to quantum computing. He has published over 270 SCI papers in international academic journals and filed for over 90 national patents in fields such as quantum chips, quantum computing operating systems, and quantum computing software. He has co-authored five professional books on quantum computing, including Introduction to Quantum Computing and Programming , Qpanda Quantum Computing Programming , and Quantum Machine Learning Theory and Practice .
He was a principal leader in the R&D team for China's first superconducting quantum computer delivered to users and for China's first quantum chip production line. He has made a series of innovative research achievements in areas including qubit encoding, manipulation, and scaling, as well as quantum software and quantum algorithms. His work includes the implementation of single-, two-, and three-qubit gate operations; achieving the world's fastest manipulation of silicon-based spin qubits; realizing long-range coupling of two qubits; and further integrating five qubits with a resonator. These achievements have incubated China's first company dedicated to the R&D and application of quantum computing-Origin Quantum.
Content
Chapter 1. Background Knowledge.- Chapter 2. Fundamentals of Quantum Computing.- Chapter 3. Quantum Machine Learning Framework VQNet.- Chapter 4. Support Vector Machines.- Chapter 5. Clustering.- Chapter 6. Convolutional Neural Networks.- Chapter 7. Recurrent Neural Networks.- Chapter 8. Generative Adversarial Networks.- Chapter 9. Natural Language Processing.