Book review — errata

Remarks on “Hands on Quantum ML with Python vol1”

to get the most out of the book.

Dany Majard
8 min readJun 22, 2022


Photo by Dan-Cristian Pădureț on Unsplash

First and foremost, I’d like to acknowledge how critical books such as “Hands on Quantum Machine Learning with Python” by Frank Zickert | Quantum Machine Learning are and laud him for the efforts he put in learning the subject. I backed his kickstarter myself, read the book with anticipation and enjoyed how it focused my reading on the subject. As he mentions in his book, it is very easy to get lost in publications and articles. It is fantastic to have resources that are curated to learning the subject, not advancing it. As a theoretical physicist and mathematician by training, I can attest how difficult it is to read research papers and how impermeable a lot of the jargon is. So I am very happy that this book exists.

There is a great need for accessible books on the subject of quantum physics, and what a great way to use Machine Learning/AI/Data Science to start!

I realize this claim differs from the book’s goal, which is to help ML practitioners or software engineers dip their toes into QML. But as the current Quantum Computing field is very transient, with many competing approaches and paradigms such as quantum circuit model, quantum Turing machine, adiabatic quantum computer, one-way quantum computer, and various quantum cellular automata, I think a little more time on the conceptual side of things wouldn’t hurt. While the book chose the first of these to work with and mentioned the fact that we are in the noisy intermediate-scale quantum (NISQ), it does not mention these competing frameworks, and therefore that it is currently unknown as to which will prevail, or to what extent they are equivalent. This is why I consider it as a didactic and fun introduction to quantum information theory.

The drawback of this approach is that it requires to get up to par with a reasonable amount of statistics, which is awkwardly sprinkled throughout the chapters. Probability theory and statistics are notoriously treacherous…