Santanu Ganguly - Quantum Machine Learning: An Applied Approach. The Theory and Application of Quantum Machine Learning in Science and Industry

I shall be telling this with a sigh

Somewhere ages and ages hence:

Two roads diverged in a wood, and I

I took the one less traveled by,

And that has made all the difference.

Robert Frost, 1916

Quantum machine learning (QML) is a cross-disciplinary subject made up of two of the most exciting research areas: quantum computing and classical machine learning. Quantum computing and machine learning are arguably two of the hottest topics in science and industry. Quantum computation and quantum information have appeared in various areas of computer science, such as information theory, cryptography, emotion representation, and image processing because tasks that appear inefficient on classical computers can be achieved by exploiting the power of quantum computation. Novel methods for speeding up certain tasks and interdisciplinary research between computer science and several other scientific fields have attracted deep scientific interest to this emerging field.

The growing confidence in the promises harbored by quantum computing and machine learning fields has given rise to many start-ups boosted by the quantum promise. Besides the well-known outfits, such as Google, IBM, Rigetti, Xanadu, and D-Wave, to name a few, there are quite a few young, exciting, and enthusiastic endeavors taking place around the world.

This book is the result of my deep interest and involvement in quantum computing and machine learning. There are two notable, ground-breaking books on the subject: Dr. Peter Witteks Quantum Machine Learning: What Quantum Computing Means to Data Mining (Academic Press, 2014) and Supervised Learning with Quantum Computers, a monumental volume, by Dr. Maria Schuld and Prof. Francesco Petruccione (Springer, 2018). In parallel to these two great works, there has been an explosion of research publications in various areas of quantum science. Besides diverse research groups in academia, great industrial research outfits from Google, IBM, Rigetti, Xanadu, and D-Wave, along with national laboratories, such as Brookhaven and Los Alamos, have made profound impacts on the field of quantum computing and machine learning.

These three realizations prompted me to put together this book. I attempt to address the most important aspects of quantum computing and machine learning algorithms for newcomers to this subject. This involves an effort to bridge the gap between the theoretical and coding side of things, utilizing libraries such as Googles Cirq and TensorFlow Quantum, D-Waves dimod and qbsolve, and Xanadus PennyLane, Qiskit, pyQuil, and QVM. I hope that this approach of complementing the theory with code helps readers with a more applied mindset find a quicker and less laborious path to advance further in the field.

The first chapter of this book addresses the basics of quantum mechanics. The next two chapters cover aspects and techniques of classical machine learning. The subsequent chapters follow a systematic, structured deep dive into various quantum machine learning algorithms, quantum optimization, applications of advanced QML algorithms, inference, Hamiltonian simulation, and so forth, finishing with advanced and up-to-date research areas, such as quantum walks, QBoost, and more. These subjects complement hands-on exercises from open source libraries regularly used today in industry and research, such as Qiskit, Rigettis Forest, D-Waves dOcean, Googles Cirq and TensorFlow Quantum, and Xanadus PennyLane, accompanied by guided implementation instructions. Wherever applicable, the book also states various options of accessing quantum computing and machine learning ecosystems as may be relevant to specific algorithms.

The most important mathematical fundamentals have been addressed in Appendix A. The book offers a hands-on approach to QML using updated libraries and algorithms in this emerging field accompanied by a constantly updated website for the book, which provides all the code examples used. My deepest wish is to make a difference to the readers who are either quantum physicists without much insight into machine learning or are machine learning professionals who aspire to get into the quantum side of things.

I have studied, lived, and worked in four different countries on three different continents. I have been fortunate enough to see much of the wonders of the world that I had aspired to see growing up wild in the Himalayas as a boy in a middle-class Indian family: from sperm whales along the US Pacific Coast to the Kinkaku-ji Temple in Kyoto, Japan; from the aurora borealis in Iceland to the magnificent Nile, pyramids, and temples of Egypt; from rhinos and tigers in Kaziranga, India, to following lions and the wildebeest migration line in Serengeti, Tanzania. In a life that has experienced the joy of diversity as much as mine, it is not unusual to have come across many amazing people whose profound influence was instrumental in getting me to plunge into the madness of writing a book.