We talk to author Frank Zickert on his book on Quantum Machine Learning, how he got started and how the field is developing

We Talk To Author Frank Zickert On His Book On Quantum Machine Learning, How He Got Started And How The Field Is Developing

QML or Quantum Machine Learning is a hot topic amongst those at the leading edge of technology. Many companies such as Xanadu, IBM, Honeywell and Cambridge Quantum are busy exploring and exploiting the field. Frank Zickert PhD is an IT professional who has taught himself Quantum Machine Learning over the last few years. In his book: Hands-On Quantum Machine Learning With Python, Frank distils his learning into the field, both Quantum and QML with the aim of taking away the difficulties of learning about these two nascent fields. We wanted to know more about the motivation behind the book and about Frank.

We Talk To Author Frank Zickert On His Book On Quantum Machine Learning, How He Got Started And How The Field Is Developing
A new book on QML from Frank Zickert

QZ: Frank, please tell us about your background

I studied Information Systems Development and earned my Ph.D. at the Goethe University of Frankfurt.

I have been working as an IT professional for 17 years. While I spent most of the time in a management role, I always had a hands-on mentality. I believe in you can’t manage what you don’t understand.

I started as a business analyst and project manager in a large financial institution. I took over the responsibility of the bank’s ATMs in Germany and learned how to program C and Cobol in a CICS mainframe environment.

Then, I switched to a software development agency to become a multi-project and team lead. I got into web technologies, such as React and Node.Js.

Today, I am the CTO of a small company and building up the organizational IT infrastructure and software. Our company provides medical physic services to ensure compliance with the Euratom directive.

QZ: How did you get into QC (Quantum Computing) and QML (Quantum Machine Learning)?

I did not have the fortune to take a quantum computing class in college, not to speak of a course in quantum machine learning.

When I heard of quantum computing for the first time, I think it was around 2008, researchers had successfully entangled qubits and were able to control them. Then, of course, Star Trek-like transportation came to mind when I heard two physically apart particles could share a state so that it was possible to change the state of one particle by observing the other.

Yet, until around 2014, I did not pay much attention. I was too busy writing my doctoral dissertation about assessing the effort caused by the requirements in a software development project. When I returned to everyday life, I was just right in time to experience the end of the second AI winter and the advent of practical machine learning. What had been theory thus far became a reality now.

But, soon, I recognized that the models we’re developing today have become increasingly hard to train. For instance, Open AI’s GPT-3 model that uses deep learning to produce human-like text would require 355 years on a single GPU. Thus, it is hard to believe that we can reach the upcoming milestones classically.

This insight brought quantum computing back into my focus. Quantum computing promises to reduce the computational complexity of specific algorithms by magnitudes. It promises to solve tasks in a few seconds classical computers would need thousands of years for. It may even prevent us from the next AI winter caused by the inability to reach the following milestones of machine learning.

In 2018, I started to deep dive into quantum machine learning. Scientific papers and a few academic books were all I could find. And these did not cover quantum machine learning but quantum computing in general. So I was happy about every little piece.

These quantum computing publications left me scratching my head. Most of the papers are pretty heavy on math and assume you’re familiar with much physical jargon. I could not even find an appropriate starting point or guidance on how to structure my learning efforts.

Frustrated with my failed attempts, I spent hours searching on Google. Finally, I hunted for quantum tutorials, only to come up empty-handed. I could see the potential value of quantum computing for machine learning. Yet, I couldn’t see how all these parts of quantum computing fit together. Entry-level material was hard to find. And practical guides were simply not existent. I wanted to get started, but I had nothing to show for my effort, except for a stack of quantum computing papers on my desk that I hardly understood.

Finally, I resorted to learning the theory first. Then, I heard about Qiskit, the IBM quantum SDK for Python. Its documentation was relatively poor at the time, especially if you were not familiar with all the physical jargon and its underlying theory. But it let me experience what some of these things like superposition, entanglement, and interference meant practically.

This practical knowledge enabled me to connect quantum computing with the algorithms I knew from machine learning. I found my way to quantum machine learning success through myriads of trial-and-error experiments, countless late nights, and much endurance.

QZ: What were your motivations for creating a book?

My motivation for creating this book arose as a result of the way I learned QML. I believe that nobody should need to painstakingly work everything out in small pieces as I did.

Since physicists and mathematicians discovered most of the stuff in quantum computing, they build upon the knowledge of their peers when they share insights and teach their students. So it is reasonable that they use the terms they are familiar with.

It is reasonable to assume a certain kind of knowledge when we talk or write about something. But should we restrain students of other, nearby disciplines from learning the stuff? For example, why shouldn’t we support a computer scientist or a software engineer in learning quantum computing?

I’ve got a clear opinion. I believe anyone sincerely interested in quantum computing should be able to learn it. There should be resources out there catering to the student’s needs, not to the teacher’s convenience.

But since I couldn’t find such resources, I started to create them on my own. I started to write about quantum machine learning in a way non-mathematicians can relate to. Rather than working through tons of theory, I want to build up practical intuition about the core concepts. I think it is best to acquire the exact theoretical knowledge we need to solve practical examples.

I wanted to create the book I wished to have when I learned QML.

QZ: Tell us about your journey through the many Quantum tools out there

Thus far, I mainly concentrate on Qiskit and the basic algorithms—from Deutsch to Grover, Simon, and of course Shor. But, I also started working with Google Cirq and Pennylane.

QZ: Any amazing insights or surprises so far

Quantum entanglement is astonishing. Two particles share a state of superposition even though they might be lightyears apart. Yet, if you measure one, the other instantly jumps into an according state.

And, the ability to use this phenomenon for computation is fascinating. I couldn’t believe that this was possible when I heard about quantum entanglement for the first time.

QZ: What is next for you?

In the near future, I plan to dive deep into Variational Quantum Eigensolvers (VQE) and quantum approximate optimization algorithms (QAOA).

QZ: When do you think QML will hit prime-time?

I’d say we’re about to enter a new era, soon.

Google, IBM, and the Chinese already declared quantum supremacy. While the problems they solve are still too superficial, they show that current quantum computers can do things classical computers can’t. So, we don’t have to wait for machines with millions of error-corrected qubits that are capable to run Shor’s algorithm.

We’re all waiting for the one algorithm that shows some practical quantum advantage today. Once we see this algorithm, I believe, interest in QML will flourish pretty fast. It will be comparable to 2014 when we saw what deep neural networks were able to do.

QZ: What is your favourite Quantum algorithm.

I like Grover because it showed me how to amplify the probabilities of measuring the qubits in a certain state. Until I learned this algorithm, qubits were just probabilistic bits to me. But, Grover changed this.

QZ: How do we get the book?

There are some free chapters available on my website, where you can purchase the full edition, but also the book is for sale on Amazon.