An Introduction to Quantum Computing through comics: Quantum Machine Learning

Please join us for this weekly series on Introduction to Quantum Computing through comics.

You might have done a bit of self-study. You might have participated in a quantum hackathon. You might have got an awesome certificate from Kitty — and had it printed on a mug! You might have even implemented a quantum algorithm on a cloud-based quantum computer. By all accounts, you might have become a quantum coder. That’s great, because I have a problem. Someone gave me this quantum device, but I have no way of proving it really is a quantum device! Can you help me?

Quantum tomography defines the problem of characterizing an unknown quantum device. Unlike deductively stepping through a quantum algorithm to find out what the output is, quantum tomography gives you the output and asks how it came to be. Since quantum physics is probabilistic, this is an inductive problem — there is no unique solution. That is maybe a bit frustrating because finding out what a device does is important. Let me tell you about it.

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Prof. Chris Ferrie, University of Technology Sydney, Quantum Tomography

Chris Ferrie is an Associate Professor at the University of Technology Sydney and the Centre for Quantum Software and Information. His research interests include quantum estimation and control, and, in particular, the use of machine learning to solve statistical problems in quantum information science. He obtained his PhD in Applied Mathematics from the Institute for Quantum Computing and University of Waterloo (Canada) in 2012. Chris’s passion for communicating science has led from the most esoteric topics of mathematical physics to more recently writing children’s books, such as Quantum Physics for Babies, and a whole collection of other titles that make science accessible even for the youngest children.