Bayesian vs Frequentist Statistical Model for Randomized Benchmarking

00H_demo.py

import matplotlib.pyplot as plt

with h_model:    
    az.plot_posterior(trace_h_model, var_names = ["EPC"],
                      round_to = 4, figsize = [10,6], textsize = 12)                          
    Bayes_legend =  "EPC  Bayesian:    {0:1.3e} ± {1:1.3e}"\
                      .format(az_summary['mean']['EPC'], az_summary['sd']['EPC'])
    LSF_legend =    "EPC  Frequentist: {0:1.3e} ± {1:1.3e}"\
                      .format(epc_est, epc_est_err)      
    plt.axvline(x=az_summary['mean']['EPC'], color='blue', ls="--")
    plt.axvline(x=epc_est, color='red', ls=":")        
    plt.legend(("Posterior density", "$Highest\; density\; interval$ HDI",
                   Bayes_legend, LSF_legend), fontsize=12)
    plt.title(experiment_type +', '+ gate + " " + str(qubits)\
              +', '+ system, fontsize=16);