SAMPLINGEXPERIMENTS_executable.py

## import essential modules
import qumcmc
from qumcmc.basic_utils import *

# from qumcmc.energy_models import IsingEnergyFunction
from qumcmc.energy_models import IsingEnergyFunction, Exact_Sampling

from qumcmc.classical_mcmc_routines import classical_mcmc

# from qumcmc.quantum_mcmc_routines_qulacs import quantum_enhanced_mcmc     #for Qulacs Simulator backend (** Faster )
# from qumcmc.quantum_mcmc_routines_qulacs_exact import quantum_mcmc_exact
#  from qumcmc.quantum_mcmc_routines_qiskit import quantum_enhanced_mcmc   #for qiskit Aer's Simulator backend

from qumcmc.trajectory_processing import (
    calculate_running_js_divergence,
    calculate_running_kl_divergence,
    calculate_runnning_magnetisation,
    get_trajectory_statistics,
    PLOT_KL_DIV,
    PLOT_MAGNETISATION,
    PLOT_MCMC_STATISTICS,
    ProcessMCMCData,
)

# from scipy.linalg import expm
# from qulacs.gate import DenseMatrix, SparseMatrix
# from qulacs import QuantumState
# from qumcmc.quantum_mcmc_routines_qulacs import quantum_enhanced_mcmc
from qumcmc.quantum_mcmc_routines import quantum_enhanced_mcmc

# from qumcmc.quantum_mcmc_routines_qulacs_exact import quantum_mcmc_exact
from qumcmc.prob_dist import DiscreteProbabilityDistribution

import pickle
import os

#####################################################################
#####################################################################


gridsize = 3
bas = bas_dataset(grid_size=gridsize)
bas.dataset.sort()

DATA_b3 = DiscreteProbabilityDistribution(Counter(bas.horizontal_bars()))

# considering only bars dataset
wt = hebbing_learning(bas.bas_dict["bars"])

# creating ising model for the bas only dataset
n_spins = gridsize * gridsize
shape_of_J = (n_spins, n_spins)
J = -1 * wt
h = np.zeros(n_spins)
model = IsingEnergyFunction(J, h, name=f"ising model BAS {n_spins}X{n_spins} bars only")

beta = 1.5
## run exact sampling over all possible configurations
exact_sampled_model = Exact_Sampling(model, beta)

## get distribution from the model
bpd = exact_sampled_model.boltzmann_pd
# exact_sampled_model.sampling_summary()


#####################################################################
#####################################################################

MCMC_SETTINGS = {
    "qu-wt1": {"mcmc_type": "quantum-enhanced", "mixer": [[["random", 1]], []]},
    "qu-wt2": {"mcmc_type": "quantum-enhanced", "mixer": [[["random", 2]], []]},
    "qu-wt3": {"mcmc_type": "quantum-enhanced", "mixer": [[["random", 3]], []]},
    "qu-alt-wt1-wt3": {
        "mcmc_type": "quantum-enhanced",
        "mixer": [[["random", 3], ["random", 1]], [0.5, 0.5]],
    },
    "qu-stabilizers-wt3": {
        "mcmc_type": "quantum-enhanced",
        "mixer": [[["custom", [[0, 1, 2], [3, 4, 5], [6, 7, 8]]]], []],
    },
    "cl-uniform": {"mcmc_type": "classical", "mixer": [[["uniform"]], []]},
    "cl-local-wt1": {"mcmc_type": "classical", "mixer": [[["local", 1]], []]},
    "cl-local-wt3": {"mcmc_type": "classical", "mixer": [[["local", 3]], []]},
    "cl-alt-wt1-wt3": {
        "mcmc_type": "classical",
        "mixer": [[["local", 3], ["local", 1]], [0.5, 0.5]],
    },
}

#####################################################################
## EXPERIMENT ##
#####################################################################

## mcmc types
# mcmc_types = ['qu-wt1', 'qu-wt2', 'qu-wt3', 'qu-stabilizers-wt3', 'cl-uniform', 'cl-local-wt3' ]
mcmc_types = ["cl-uniform", "cl-local-wt3", "cl-local-wt1"]

## seeds
num_different_chains = 10
mcmc_steps = 15000

## data saving location
name_data = "SamplingData/BAS3/SAMPLINGDATA_BAS3.pkl"
name_result = "SamplingData/BAS3/SAMPLINGRESULT_BAS3.pkl"

## to reinitiate experiment TODO
# SAMPLINGDATA_BAS3 = {}
# SAMPLINGRESULT_BAS3 = {}

## to continue previous experiment
with open(name_data, "rb") as f:
    SAMPLINGDATA_BAS3 = pickle.load(f)
with open(name_result, "rb") as f:
    SAMPLINGRESULT_BAS3 = pickle.load(f)
model = SAMPLINGRESULT_BAS3.model

for mcmc_setting in mcmc_types:

    for seed_val in tqdm(range(1, num_different_chains + 1), desc=str(mcmc_setting)):

        if MCMC_SETTINGS[mcmc_setting]["mcmc_type"] == "classical":
            steps = mcmc_steps
            # list_labels.append('classical uniform strategy')
            mcmc_chain = classical_mcmc(
                n_hops=steps,
                model=model,
                temperature=1 / beta,
                initial_state=DATA_b3.get_sample(1)[0],
                proposition_method=MCMC_SETTINGS[mcmc_setting]["mixer"],
            )

        if MCMC_SETTINGS[mcmc_setting]["mcmc_type"] == "quantum-enhanced":
            steps = mcmc_steps
            # list_labels.append('classical uniform strategy')
            mcmc_chain = quantum_enhanced_mcmc_2(
                n_hops=steps,
                model=model,
                temperature=1 / beta,
                gamma_range=(0.1, 0.4),
                initial_state=DATA_b3.get_sample(1)[0],
                mixer=MCMC_SETTINGS[mcmc_setting]["mixer"],
            )

    SAMPLINGDATA_BAS3[seed_val][mcmc_setting] = mcmc_chain
    SAMPLINGRESULT_BAS3.UPDATE_DATA(
        SAMPLINGDATA_BAS3, None, [mcmc_setting], save_data=False
    )

    with open(name_data, "wb") as f:
        pickle.dump(SAMPLINGDATA_BAS3, f)
    with open(name_result, "wb") as f:
        pickle.dump(SAMPLINGRESULT_BAS3, f)