/CPEN400Q-FinalProject

Primary LanguageJupyter Notebook

CPEN400Q-FinalProject

Project Dependencies

  1. Pennylane >= v0.29.1
  2. Matplotlib >= v3.7.1

How To Run

  • To reproduce any results, just run the notebook in order until "Reproducing Results"
  • Reproducing results contains the simulations for the following and you can run whichever result you would like to reproduce
    • Bitflip Noise Model
    • Phaseflip Noise Model
    • Training Wasserstein on Alpha, Beta from Fidelity
    • Training Fidelity on Alpha, Beta from Wasserstein
  • Note that running the entire notebook could take a long time (~30 hours potentially) due to the number of iterations and simulations needed for gradient descent

Contributions

Claire Song

  • Understanding the calculation of Hamiltonians and Cost Functions for Fidelity and Wasserstein
  • Implementation of Hamiltonian calculations

Justin Hua

  • Code for encoding and noise correction unitaries
  • Kraus matrices for bit-flip noise

Matthew Chow

  • Code for running the simulations and results minus creating the unitaries

Matthew Yen

  • Kraus matrices for phase-flip noise
  • Implementation of VQA with cost calculations