This year’s August was dedicated to IBM’s Global Quantum Summer School where I not only learned the basics in a compressed timeline and a tight schedule but also a few applications of quantum computing. The badge one gets after 4 gruelling weeks is a “quantum experience” in itself as you think you understand what you are doing but at the same time, you have no idea what is going on. The month transitioned from quantum circuit basics to variational algorithms at a fast pace which left only a little and limited time to ‘do your own research’ and get your hands dirty on the application part.
As far as the applications are concerned, quantum chemistry, quantum simulations, and a few really complicated modelling tasks would fit the bill of the problems that can be solved with quantum computers. Having said so, there is another branch that’s burgeoning and seeking a lot of interest from the users and researchers and that’s Quantum Machine Learning — QML in short.
I thought QML should be a logical successor to the conventional ML and I set out to do the same. Now, I wanted to have a problem that wouldn’t be straightforward for ML algorithms to solve because of the sheer size of the data, hard to identify the complex patterns, but something that I could code from the comfort of my humble machine. I looked no further than our old friend, Physics, which hides a gamut of complex but interesting problems in its lap and it sounds intellectually cool to work on such problems.
So it goes.
I decided to deal with the dark matter classification problem examined under the OPERA experiment (Oscillation Project with Emulsion-tRacking Apparatus) associated with the Large Hadron Collider, CERN.
In short, we will train a classifier to differentiate between the signal and the noise. The signal is the presence of the dark matter and the noise means an absence or something else altogether but not the signal.