Machine Learning with Quantum Computers

by Maria Schuld, Francesco Petruccione

This book offers an introduction into quantum machine learning research, covering approaches that range from "near-term" to fault-tolerant quantum machine learning algorithms, and from theoretical to practical techniques that help us understand how quantum computers can learn from data. Among the topics discussed are parameterized quantum circuits, hybrid optimization, data encoding, quantum feature maps and kernel methods, quantum learning theory, as well as quantum neural networks. The book aims at an audience of computer scientists and physicists at the graduate level onwards. The second edition extends the material beyond supervised learning and puts a special focus on the developments in near-term quantum machine learning seen over the past few years.

Genres: Textbooks

328 pages, Paperback

Published October 19, 2022

Reviews

[Negar Astani · Rating 5 out of 5]

I am a teacher, and I used this book to teach quantum machine learning this semester. Since the field is very new, there aren’t many comprehensive textbooks available—most resources are scattered across research papers. Maria Schuld, one of the pioneers of quantum machine learning, brings her broad vision to this book, and her honest perspective on the field shines through, especially if you’ve followed her talks.

That said, this book assumes a foundational understanding of both quantum computing and classical machine learning. Without prior knowledge in these areas, it can be challenging to follow. In our course, we prepared by studying quantum computing from Thomas Wong's book and Quantum Computation and Quantum Information by Nielsen and Chuang in previous semesters. We also provided a brief introduction to classical machine learning to familiarize students with the terminology. Only after this preparation could we fully appreciate the essence of this book. Maria Schuld and Francesco Petruccione’s collaborative effort is evident in how well the material is presented and structured.

Given the nascent nature of quantum machine learning, the authors had to distill the key concepts of classical machine learning and combine them with the core mechanisms of quantum computation to identify areas where the two fields could merge effectively. I believe the authors succeeded in presenting this innovative perspective, making the book a valuable resource for those ready to explore the frontier of this exciting field