xxz.py

from create_model import generate_symmetries
import numpy as np
import yaml
import time
import matplotlib.pyplot as plt

from create_model import spin_model
from entropy_new import _hamming_weight, reduced_dm, compute_entropy

import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--set_number_spins", "-d", dest='number_spins',type=int, required=False)
parser.add_argument("--set_param_range_and_steps", '-pr', dest='param_range', nargs='+', type=float, required=False)
parser.add_argument("--boundary_condition", "-b", dest='periodic' ,type=bool, required=False)
args = parser.parse_args()


def test_xxz(number_spins, param_range, periodic=False):
    model_name = 'xxz'
    params = np.linspace(param_range[0], param_range[1], int(param_range[2]))
    entropies = []
    hamming_weight = None #number_spins // 2
    for param in params:
        if param > -1.0:
            hamming_weight = number_spins // 2
        model = spin_model(model_name=model_name, number_spins=number_spins, periodic=periodic, 
                                param=param, hamming_weight=hamming_weight)
        model.compute_ew_and_ev()
        print('EIGENSTATE at Delta/J = ', param, 'is: ', model.eigenstates[:,0])
        basis_states = model.basis.states
        gs = model.eigenstates[:,0]
        # just for testing
        if number_spins == 8 and param >= -1.1 and param > -1.4:
            print(gs[0])
            printi = []
            for i, x in enumerate(gs):
                if abs(x) > 1e-1:
                    printi.append([x, basis_states[i]])
            print(printi)
        #print('Number Spins: ', model.basis.number_spins)
        #print('States', model.basis.states)
        sub_dim = number_spins // 2
        first_trace_spin = None #number_spins // 4
        rhos = reduced_dm(sub_dim, number_spins, hamming_weight, gs, basis_states, first_trace_spin=first_trace_spin)
        entropy = compute_entropy(rhos)
        entropies.append(entropy)

    plt.figure(figsize=(12, 12))
    plt.plot(params, entropies, label="half chain entropy")
    plt.legend()
    plt.xlabel('Delta / J')
    plt.ylabel('Bipartite Entanglement Entropy')
    plt.title('Model:' + model_name + str(number_spins) + str(periodic))
    plt.grid(True)
    timestr = time.strftime("%Y%m%d-%H%M%S")
    plt.savefig('output/' + timestr + 'entropytest.jpg')

    
def test_area_law(number_spins, periodic, param_range):
    model_name = 'xxz'
    hamming_weight = number_spins // 2
    params = np.linspace(param_range[0], param_range[1], int(param_range[2]))
    spin_inversion = None
    entropies = {}
    sub_dims = [i for i in range(1,number_spins)]
    for param in params:
        if param < -1.0:
            hamming = hamming_weight
        else: 
            hamming = hamming_weight
        bipartite_entropies = []
        model = spin_model(model_name=model_name, number_spins=number_spins, periodic=periodic, 
                                param=param, hamming_weight=hamming, spin_inversion=spin_inversion)
        model.compute_ew_and_ev()
        gs = model.eigenstates[:,0]
        basis_states = model.basis.states
        for sub_dim in sub_dims:
            print('----------------------------------')
            print('Sub Dimension is ', sub_dim)
            rhos = reduced_dm(sub_dim, number_spins, hamming_weight, gs, basis_states, spin_inversion)
            bipartite_entropies.append(compute_entropy(rhos))
        entropies[str(param)] = bipartite_entropies
        del model
    
    data = {
        'Model': model_name,
        'Number_spins': number_spins,
        'Periodic': periodic,
        'Hamming_weight': hamming_weight,
        'Sub_dims': sub_dims,
        'Delta_over_J': params,
        'Entropies': entropies
    }

    timestr = time.strftime("%Y%m%d-%H%M%S")
    filename = 'test_area_law_' + timestr
    with open('output/' + filename + '.yaml', 'w') as outfile:
        yaml.dump(data, outfile, default_flow_style=False)
    print(filename)
    plt.figure(figsize=(12,12))
    i = 0
    for key in entropies:
        if i % 5 == 0:
            plt.plot(np.array(sub_dims)/number_spins, entropies[key], label='Delta_over_J:' + key)
        i += 1
    plt.legend()
    plt.xlabel('x/L')
    plt.ylabel('Bipartite Entanglement Entropy')
    plt.title('Model:' + model_name + str(number_spins)+ str(periodic))
    plt.grid(True)

    plt.savefig('output/' + filename + '.jpg')


    plt.figure(figsize=(12, 12))
    half_chain_ee = []
    for key in entropies:
        half_chain_ee.append(entropies[key][number_spins // 2 - 1])
    plt.plot(params, half_chain_ee, label='Delta_over_J:' + key)
    plt.legend()
    plt.xlabel('Delta / J')
    plt.ylabel('Bipartite Entanglement Entropy')
    plt.title('Model:' + model_name + ' Number spins:' + str(number_spins))#, ' Periodic:' + str(periodic))
    plt.grid(True)

    plt.savefig('output/' + 'entropy' + timestr + '.jpg')

if __name__ == "__main__":
    param_range = args.param_range
    number_spins = args.number_spins
    periodic = args.periodic
    #test_area_law(number_spins, periodic, param_range)
    test_xxz(number_spins, param_range)