How do you tell if a tumour is cancerous? How does your phone recognize your face? How does Gmail identify spam in your inbox? These are all classification problems that are suited for machine learning. In the past month, I got started with quantum machine learning (QML) by applying it to typical classification problems. 

I'll describe my projects in further detail below but the bite-size version is that I built two models using qiskit (quantum computing framework for programming). One predicts breast cancer malignancy and the other determines wine quality 😄.  Classification was interesting, but I wanted to move on to a more exciting area of QML—combinatorial optimization!

So nearing the end of the month, I started learning a famous QML algorithm used for combinatorial optimization—QAOA. 

For fellow QC nerds that want to see some of what I've indulged in:

• Purpose of adiabatic quantum computation
• Ising type models 
• Proof of the variational principle
• Using QAOA to solve the weighted max-cut problem

Here's the link for the most helpful review I found when first entering QML (it's on variational quantum algorithms which is the foundation of quantum machine learning):