Hands-On Quantum Machine Learning with Python

Frank Zickert

You're interested in quantum computing and machine learning. But you don't know how to get started? Let me help.

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.