Bonjour, Density Matrices. In this project, a python script was created to study the observations of previous U.S. House of Representative election parties and make predictions on (1) the probability of that a party that was in the majority before an election would become a minority party afterward (and vice versa) and (2) the probability that a party that was not represented in the U.S. House of Representatives before an election would have representation there afterward.
A density matrix is a matrix that describes the statistical state of a system. Traditionally in physics, the density matrix is applied when dealing with mixed states, which consist of a statistical ensemble of several different quantum sys tems. Mixed states arise in situations where the experimenter does not know which particular states are being manipulated. Examples include a system with an uncertain preparation history. In this honor project, we look at the pro portional representation of all parties in the U.S. House of Representatives before and after the election observations. We do so for all elections to the U.S. House of Representatives from the founding of the republic until present. We then determine the empirical expectation value of density matrix for the U.S. House of Representatives. The result would yield information such as (1) the probability of that a party that was in the majority before an elec tion would become a minority party afterward (and vice versa); the probability that a party that was not represented in the U.S. House of Representatives before an election would have representation there afterward.
Through command prompt (Windows) or terminal (Mac), navigate to the folder you want to hold the project folder.
Once there git clone using either the ssh or http method
SSH method
git clone git@github.com:theFlawlessHack/ushor-density-matrices-python.git
HTTP method
git clone https://github.com/theFlawlessHack/ushor-density-matrices-python.git
Through command prompt (Windows) or terminal (Mac), navigate to the project folder which you just cloned.
Once there, run the run.py file using python
type
python run.py
in your terminal or command line in the project folder.
If the run was successful then you should now see a workfile in the current project directory.
In this workfile you can analyze the results.
Sample workfile
