Quantum machine learning (QML) is revolutionizing artificial intelligence by leveraging the power of quantum computing to access previously unimaginable computational possibilities. However, the field remains fragmented-balancing rigorous quantum theory with practical AI applications remains a challenge. This book bridges this gap, offering a systematic, hands-on guide for AI researchers, ML practitioners, and computer scientists eager to explore this emerging frontier.

It provides a cohesive roadmap, covering everything from fundamental quantum computing principles to state-of-the-art QML techniques. Readers will explore quantum kernel methods, quantum neural networks, and quantum Transformers, gaining insight into their theoretical foundations, performance advantages, and practical implementations. The book's code demonstrations offer hands-on experience, ensuring that readers can move beyond theory to real-world applications.

Designed for those with an AI or ML background, this tutorial does not assume prior expertise in quantum computing. Instead, it presents complex concepts with clarity, making it an essential resource for researchers, graduate students, and industry professionals eager to stay ahead in the quantum AI revolution. Whether you seek to understand quantum speedups, develop quantum-based models, or explore future research directions, this book provides the foundation you need to engage with QML and shape the future of intelligent computing.

Autorentext

Yuxuan Du is an assistant professor at Nanyang Technological University, specializing in quantum machine learning, quantum learning theory, and AI for quantum science. He was previously a senior researcher at JD Explore Academy and earned his Ph.D. in computer science from The University of Sydney in 2021.

Xinbiao Wang is a research fellow at Nanyang Technological University. He earned his Master's (2021) and Ph.D. (2024) from Wuhan University, researching quantum machine learning under Professors Dacheng Tao and Yong Luo. He interned at JD.com and held visiting positions at NTU and NUS.

Naixu Guo is a Ph.D. candidate in Quantum Information at NUS. He holds an M.E. in Electrical Engineering from Osaka University (2022) and a B.E. in Applied Physics from Kyoto University (2020) and has conducted research visits at RWTH Aachen and the Free University of Berlin.

Zhan Yu is a Ph.D. student in Quantum Computing at NUS (since 2023). He holds an M.Sc. (2021) and B.Sc. (2019) in Computer Science from the University of Calgary, where he researched quantum walks under Peter Høyer. He also holds a B.Eng. in Software Engineering from Wuhan University of Technology (2016) and interned at Baidu Research (2021-2023).

Yang Qian received his B.S. from Huazhong University of Science and Technology (2016), M.S. from CASIA (2019), and Ph.D. from the University of Sydney (2024) under Prof. Dacheng Tao.

Kaining Zhang is a Research Fellow at NTU's College of Computing and Data Science. He earned his Ph.D. (2024) and MPhil (2020) in Computer Science from the University of Sydney and a B.Sc. in Physics from USTC (2018).

Min-Hsiu Hsieh is Director of the Hon Hai Quantum Computing Research Center, Taiwan. He was previously an Associate Professor at UTS and held research roles at Cambridge, the University of Tokyo, and ERATO-SORST in Japan. He also held an Australian Research Council Future Fellowship (2014-2018).

Patrick Rebentrost is an assistant professor at NUS, specializing in quantum computing and quantum machine learning. He previously held research positions at MIT, Xanadu, and the Centre for Quantum Technologies. He earned his Ph.D. from Harvard University in 2012.

Dacheng Tao is a distinguished university professor at NTU and a leading AI, machine learning, and quantum computing researcher. He was previously a professor at the University of Sydney (2016-2023) and Senior VP at JD.com. Holding a Ph.D. from the University of London, he has held faculty roles at UTS, NTU, and HK PolyU.