I would like to share here some of the code I wrote for various research projects I have been working on (see also my GitHub page).
QUANTUM MACHINE LEARNING (Python)
This is an extension of an idea presented in arXiv:1706.01561 extended for a slightly more practical super-conducting qubits implementation that can be tested on IBM quantum experience machine. Here is a code written in python based ProjectQ, and here is a short note with the explanation of the protocol.
MACHINE LEARNING WITH CNNs (Python)
This is my ongoing project about adapting machine learning for quantum parameter estimation. Here is the python code using Tensorflow library, where I show how to implement the convolutional neural network.
This project has been done in collaboration with Klaus Mølmer and Christian Kraglund Andersen.
STOCHASTIC SCHRÖDINGER AND MASTER EQUATIONS (Matlab)
These are the basic elements used for simulation of quantum continuously monitored systems. Here are some examples of how to implement these in Matlab.
The system simulated in this code is described in arXiv:1708.06680. I also wrote a short popular post about this in the blog post A Case for Past Quantum State.
This project has been done in collaboration with Edward Laird, Andrew Briggs and Klaus Mølmer.
MATRIX PRODUCT STATES (C++)
As an original project in one of our graduate courses, in collaboration with Philip Daniel Blocher and Jinglei Zhang, we wrote an iTensor simulation of the driven and measured Heisenberg chain. Using matrix product states we were able to explore measurement back-action, spin transfer through the chain and even hints of some topological phenomena. See the examples of our code and our presentation here.