Quantum State Tomography through Maximum Likelihood Estimation

This work is a culmanation of my graduate school masters project at Stony Brook University in the Quantum Information Science & Technology Group under Dr. Eden Figueroa to perform quantum state tomography through maximum likelihood estimation. You can read more about the work in the document Quantum_State_Tomography.pdf. The main files to perform the simulation of quantum states, through Simulate_QST.py, as well as to perform the maximum likelihood estimation of quantum states, through QST_MLE.py, are described below.

Simulate_QST.py (Simulations of Quantum Statistics)

Serves to simulate real world examples of quantum states that can be measured through techniques such as Optical Homodyne Detection.

It provides:

• An efficient way of simulating quadrature data for a given quantum state
• Can work with any arbitrary quantum state, including coherent states and (WORK IN PROGRESS) squeezed states

Usage:

The Jupyter Notebook Generate_Quadratures_Simulation.ipynb contains several working examples of how to easily generate different sets of quadrature data. Datasets from this notebook can be easily saved to a Data folder to be used for Maximum Likelihood Reconstruction.

In order to use the functions in a different Python file, make sure to download the Simulate_QST.py file to the same folder as the new code and use the command:

from Simulate_QST.py import *

The full list of functions, their usage, as well as some examples can be found within the above Python file.

QST_MLE.py (Maximum Likelihood Estimation)

Serves to reconstruct the density matrix of a given quantum state, and can be used to extract amplitude and phase information. Our group has shown it to work with real data gathered in the lab through Homodyne Detection of several different coherent states.

It provides:

• A vectorized method of finding the most likely density matrix corresponding to a given set of quadrature data
• Visualizations of the Wigner distribution of the quantum state through QuTiP

Usage:

The Jupyter Notebook MLE.ipynb contains an illustritive working example of how to reconstruct a density matrix given a set of quadrature data. It can easily be used in conjuction with the Generate_Quadratures_Simulation.ipynb.

In order to use the functions in a different Python file, make sure to download the MLE_Functions.py file to the same folder as the new code and use the command:

from QST_MLE.py import *

The full list of functions, their usage, as well as some examples can be found within the above Python file.