A Python Framework For the Simulation and Analysis of Random Quantum Clifford Circuits and Stabilizer Codes.
Used to generate initial data about the NoRA tensor network ansatz as presented in arXiv:2303.16946.
Disclaimer: Work on this implementation of QStab has stopped due to the inefficiency of the Python language for large-scale data generation. Please refer to QStab.jl for the currently maintained implementation using Julia.
Newer versions might also work, but have not been tested.
The code relevant for the data generation in arXiv:2303.16946 is primarily found in nora.py, whereas new random Cliffords/Weyls and stabilizers can be generally sampled using the code in clifford.py and stabilizer.py respectively. All important functions are commented indicating how to use them.
Most functions also require a Galois field object (GF) as input parameter, which indicates the qudit dimension and can be generated using galois.GF(p**m) for some prime number p > 2 and positive integer m > 0. For the paper p = 3 and m = 1 were used.