MFIM-OM_HVA-v2.py

######################################################
# OM for HVA Ansatz - MFIM - Overlaps Set to Relative
######################################################

############################################
# Author - R Tali [rtali@iastate.edu]
# Version - v1.1
############################################


import torch
import sqlalchemy as sa
from sqlalchemy.sql.sqltypes import TIMESTAMP, Integer, Numeric
from sqlalchemy.orm import sessionmaker
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy import Table, Column, String, MetaData
from sqlalchemy import create_engine
import datetime as dt
import numpy as np
import random
from numpy import pi
from numpy import linalg as LA
import matplotlib.pyplot as plt
import time
from datetime import datetime
plt.style.use('fivethirtyeight')


if torch.cuda.is_available():

    print('Using MySQL on AWS to dump all data from the runs \n')

    continue_FLAG = True

    print('============================\n')
    print('If CUDA available? Yes')
    print('\n============================\n')

    # Define Device
    cuda0 = torch.device('cuda:0')

    ##################################
    # Logging in Database
    ##################################

    try:
        db = create_engine(
            'mysql+pymysql://vqe_user:Login1root@vqe.cuzj4xzkiyrf.us-east-2.rds.amazonaws.com:3306/Logs')
        base = declarative_base()

        Session = sessionmaker(db)
        session = Session()

    except Exception as e:

        print(e)

    ###################################################
    # Define ORM Adaptor
    ###################################################

    class OM_MODEL(base):
        __tablename__ = 'OM_MODEL'
        __table_args__ = {'schema': 'Logs', 'extend_existing': True}
        model_id = Column(Integer, primary_key=True)
        n_qubits = Column(Integer)
        g = Column(sa.Float)
        l = Column(Integer)
        ref = Column(String(10))

        # Overlap Type - 1

        o1t_0 = Column(sa.Float)
        o1t_1 = Column(sa.Float)
        o1t_2 = Column(sa.Float)
        o1t_3 = Column(sa.Float)
        o1t_4 = Column(sa.Float)
        o1t_5 = Column(sa.Float)
        o1t_6 = Column(sa.Float)
        o1t_7 = Column(sa.Float)
        o1t_8 = Column(sa.Float)
        o1t_9 = Column(sa.Float)

        o1t_10 = Column(sa.Float)
        o1t_11 = Column(sa.Float)
        o1t_12 = Column(sa.Float)
        o1t_13 = Column(sa.Float)
        o1t_14 = Column(sa.Float)
        o1t_15 = Column(sa.Float)
        o1t_16 = Column(sa.Float)
        o1t_17 = Column(sa.Float)
        o1t_18 = Column(sa.Float)
        o1t_19 = Column(sa.Float)

        o1t_20 = Column(sa.Float)
        o1t_21 = Column(sa.Float)
        o1t_22 = Column(sa.Float)
        o1t_23 = Column(sa.Float)
        o1t_24 = Column(sa.Float)
        o1t_25 = Column(sa.Float)
        o1t_26 = Column(sa.Float)
        o1t_27 = Column(sa.Float)
        o1t_28 = Column(sa.Float)
        o1t_29 = Column(sa.Float)

        o1t_30 = Column(sa.Float)
        o1t_31 = Column(sa.Float)
        o1t_32 = Column(sa.Float)
        o1t_33 = Column(sa.Float)
        o1t_34 = Column(sa.Float)
        o1t_35 = Column(sa.Float)
        o1t_36 = Column(sa.Float)
        o1t_37 = Column(sa.Float)
        o1t_38 = Column(sa.Float)
        o1t_39 = Column(sa.Float)

        o1t_40 = Column(sa.Float)
        o1t_41 = Column(sa.Float)
        o1t_42 = Column(sa.Float)
        o1t_43 = Column(sa.Float)
        o1t_44 = Column(sa.Float)
        o1t_45 = Column(sa.Float)
        o1t_46 = Column(sa.Float)
        o1t_47 = Column(sa.Float)
        o1t_48 = Column(sa.Float)
        o1t_49 = Column(sa.Float)

        o1t_50 = Column(sa.Float)
        o1t_51 = Column(sa.Float)
        o1t_52 = Column(sa.Float)
        o1t_53 = Column(sa.Float)
        o1t_54 = Column(sa.Float)
        o1t_55 = Column(sa.Float)
        o1t_56 = Column(sa.Float)
        o1t_57 = Column(sa.Float)
        o1t_58 = Column(sa.Float)
        o1t_59 = Column(sa.Float)

        o1t_60 = Column(sa.Float)
        o1t_61 = Column(sa.Float)
        o1t_62 = Column(sa.Float)
        o1t_63 = Column(sa.Float)
        o1t_64 = Column(sa.Float)
        o1t_65 = Column(sa.Float)
        o1t_66 = Column(sa.Float)
        o1t_67 = Column(sa.Float)
        o1t_68 = Column(sa.Float)
        o1t_69 = Column(sa.Float)

        o1t_70 = Column(sa.Float)
        o1t_71 = Column(sa.Float)
        o1t_72 = Column(sa.Float)
        o1t_73 = Column(sa.Float)
        o1t_74 = Column(sa.Float)
        o1t_75 = Column(sa.Float)
        o1t_76 = Column(sa.Float)
        o1t_77 = Column(sa.Float)
        o1t_78 = Column(sa.Float)
        o1t_79 = Column(sa.Float)

        o1t_80 = Column(sa.Float)
        o1t_81 = Column(sa.Float)
        o1t_82 = Column(sa.Float)
        o1t_83 = Column(sa.Float)
        o1t_84 = Column(sa.Float)
        o1t_85 = Column(sa.Float)
        o1t_86 = Column(sa.Float)
        o1t_87 = Column(sa.Float)
        o1t_88 = Column(sa.Float)
        o1t_89 = Column(sa.Float)

        o1t_90 = Column(sa.Float)
        o1t_91 = Column(sa.Float)
        o1t_92 = Column(sa.Float)
        o1t_93 = Column(sa.Float)
        o1t_94 = Column(sa.Float)
        o1t_95 = Column(sa.Float)
        o1t_96 = Column(sa.Float)
        o1t_97 = Column(sa.Float)
        o1t_98 = Column(sa.Float)
        o1t_99 = Column(sa.Float)

        o1t_100 = Column(sa.Float)
        o1t_101 = Column(sa.Float)
        o1t_102 = Column(sa.Float)
        o1t_103 = Column(sa.Float)
        o1t_104 = Column(sa.Float)
        o1t_105 = Column(sa.Float)
        o1t_106 = Column(sa.Float)
        o1t_107 = Column(sa.Float)
        o1t_108 = Column(sa.Float)
        o1t_109 = Column(sa.Float)

        o1t_110 = Column(sa.Float)
        o1t_111 = Column(sa.Float)
        o1t_112 = Column(sa.Float)
        o1t_113 = Column(sa.Float)
        o1t_114 = Column(sa.Float)
        o1t_115 = Column(sa.Float)
        o1t_116 = Column(sa.Float)
        o1t_117 = Column(sa.Float)
        o1t_118 = Column(sa.Float)
        o1t_119 = Column(sa.Float)

        o1t_120 = Column(sa.Float)
        o1t_121 = Column(sa.Float)
        o1t_122 = Column(sa.Float)
        o1t_123 = Column(sa.Float)
        o1t_124 = Column(sa.Float)
        o1t_125 = Column(sa.Float)
        o1t_126 = Column(sa.Float)
        o1t_127 = Column(sa.Float)
        o1t_128 = Column(sa.Float)
        o1t_129 = Column(sa.Float)

        o1t_130 = Column(sa.Float)
        o1t_131 = Column(sa.Float)
        o1t_132 = Column(sa.Float)
        o1t_133 = Column(sa.Float)
        o1t_134 = Column(sa.Float)
        o1t_135 = Column(sa.Float)
        o1t_136 = Column(sa.Float)
        o1t_137 = Column(sa.Float)
        o1t_138 = Column(sa.Float)
        o1t_139 = Column(sa.Float)

        o1t_140 = Column(sa.Float)
        o1t_141 = Column(sa.Float)
        o1t_142 = Column(sa.Float)
        o1t_143 = Column(sa.Float)
        o1t_144 = Column(sa.Float)
        o1t_145 = Column(sa.Float)
        o1t_146 = Column(sa.Float)
        o1t_147 = Column(sa.Float)
        o1t_148 = Column(sa.Float)
        o1t_149 = Column(sa.Float)

        o1t_150 = Column(sa.Float)
        o1t_151 = Column(sa.Float)
        o1t_152 = Column(sa.Float)
        o1t_153 = Column(sa.Float)
        o1t_154 = Column(sa.Float)
        o1t_155 = Column(sa.Float)
        o1t_156 = Column(sa.Float)
        o1t_157 = Column(sa.Float)
        o1t_158 = Column(sa.Float)
        o1t_159 = Column(sa.Float)

        o1t_160 = Column(sa.Float)
        o1t_161 = Column(sa.Float)
        o1t_162 = Column(sa.Float)
        o1t_163 = Column(sa.Float)
        o1t_164 = Column(sa.Float)
        o1t_165 = Column(sa.Float)
        o1t_166 = Column(sa.Float)
        o1t_167 = Column(sa.Float)
        o1t_168 = Column(sa.Float)
        o1t_169 = Column(sa.Float)

        o1t_170 = Column(sa.Float)
        o1t_171 = Column(sa.Float)
        o1t_172 = Column(sa.Float)
        o1t_173 = Column(sa.Float)
        o1t_174 = Column(sa.Float)
        o1t_175 = Column(sa.Float)
        o1t_176 = Column(sa.Float)
        o1t_177 = Column(sa.Float)
        o1t_178 = Column(sa.Float)
        o1t_179 = Column(sa.Float)

        o1t_180 = Column(sa.Float)
        o1t_181 = Column(sa.Float)
        o1t_182 = Column(sa.Float)
        o1t_183 = Column(sa.Float)
        o1t_184 = Column(sa.Float)
        o1t_185 = Column(sa.Float)
        o1t_186 = Column(sa.Float)
        o1t_187 = Column(sa.Float)
        o1t_188 = Column(sa.Float)
        o1t_189 = Column(sa.Float)

        o1t_190 = Column(sa.Float)
        o1t_191 = Column(sa.Float)
        o1t_192 = Column(sa.Float)
        o1t_193 = Column(sa.Float)
        o1t_194 = Column(sa.Float)
        o1t_195 = Column(sa.Float)
        o1t_196 = Column(sa.Float)
        o1t_197 = Column(sa.Float)
        o1t_198 = Column(sa.Float)
        o1t_199 = Column(sa.Float)

        o1t_200 = Column(sa.Float)
        o1t_201 = Column(sa.Float)
        o1t_202 = Column(sa.Float)
        o1t_203 = Column(sa.Float)
        o1t_204 = Column(sa.Float)
        o1t_205 = Column(sa.Float)
        o1t_206 = Column(sa.Float)
        o1t_207 = Column(sa.Float)
        o1t_208 = Column(sa.Float)
        o1t_209 = Column(sa.Float)

        # Overlap Type - 2

        o2t_0 = Column(sa.Float)
        o2t_1 = Column(sa.Float)
        o2t_2 = Column(sa.Float)
        o2t_3 = Column(sa.Float)
        o2t_4 = Column(sa.Float)
        o2t_5 = Column(sa.Float)
        o2t_6 = Column(sa.Float)
        o2t_7 = Column(sa.Float)
        o2t_8 = Column(sa.Float)
        o2t_9 = Column(sa.Float)

        o2t_10 = Column(sa.Float)
        o2t_11 = Column(sa.Float)
        o2t_12 = Column(sa.Float)
        o2t_13 = Column(sa.Float)
        o2t_14 = Column(sa.Float)
        o2t_15 = Column(sa.Float)
        o2t_16 = Column(sa.Float)
        o2t_17 = Column(sa.Float)
        o2t_18 = Column(sa.Float)
        o2t_19 = Column(sa.Float)

        o2t_20 = Column(sa.Float)
        o2t_21 = Column(sa.Float)
        o2t_22 = Column(sa.Float)
        o2t_23 = Column(sa.Float)
        o2t_24 = Column(sa.Float)
        o2t_25 = Column(sa.Float)
        o2t_26 = Column(sa.Float)
        o2t_27 = Column(sa.Float)
        o2t_28 = Column(sa.Float)
        o2t_29 = Column(sa.Float)

        o2t_30 = Column(sa.Float)
        o2t_31 = Column(sa.Float)
        o2t_32 = Column(sa.Float)
        o2t_33 = Column(sa.Float)
        o2t_34 = Column(sa.Float)
        o2t_35 = Column(sa.Float)
        o2t_36 = Column(sa.Float)
        o2t_37 = Column(sa.Float)
        o2t_38 = Column(sa.Float)
        o2t_39 = Column(sa.Float)

        o2t_40 = Column(sa.Float)
        o2t_41 = Column(sa.Float)
        o2t_42 = Column(sa.Float)
        o2t_43 = Column(sa.Float)
        o2t_44 = Column(sa.Float)
        o2t_45 = Column(sa.Float)
        o2t_46 = Column(sa.Float)
        o2t_47 = Column(sa.Float)
        o2t_48 = Column(sa.Float)
        o2t_49 = Column(sa.Float)

        o2t_50 = Column(sa.Float)
        o2t_51 = Column(sa.Float)
        o2t_52 = Column(sa.Float)
        o2t_53 = Column(sa.Float)
        o2t_54 = Column(sa.Float)
        o2t_55 = Column(sa.Float)
        o2t_56 = Column(sa.Float)
        o2t_57 = Column(sa.Float)
        o2t_58 = Column(sa.Float)
        o2t_59 = Column(sa.Float)

        o2t_60 = Column(sa.Float)
        o2t_61 = Column(sa.Float)
        o2t_62 = Column(sa.Float)
        o2t_63 = Column(sa.Float)
        o2t_64 = Column(sa.Float)
        o2t_65 = Column(sa.Float)
        o2t_66 = Column(sa.Float)
        o2t_67 = Column(sa.Float)
        o2t_68 = Column(sa.Float)
        o2t_69 = Column(sa.Float)

        o2t_70 = Column(sa.Float)
        o2t_71 = Column(sa.Float)
        o2t_72 = Column(sa.Float)
        o2t_73 = Column(sa.Float)
        o2t_74 = Column(sa.Float)
        o2t_75 = Column(sa.Float)
        o2t_76 = Column(sa.Float)
        o2t_77 = Column(sa.Float)
        o2t_78 = Column(sa.Float)
        o2t_79 = Column(sa.Float)

        o2t_80 = Column(sa.Float)
        o2t_81 = Column(sa.Float)
        o2t_82 = Column(sa.Float)
        o2t_83 = Column(sa.Float)
        o2t_84 = Column(sa.Float)
        o2t_85 = Column(sa.Float)
        o2t_86 = Column(sa.Float)
        o2t_87 = Column(sa.Float)
        o2t_88 = Column(sa.Float)
        o2t_89 = Column(sa.Float)

        o2t_90 = Column(sa.Float)
        o2t_91 = Column(sa.Float)
        o2t_92 = Column(sa.Float)
        o2t_93 = Column(sa.Float)
        o2t_94 = Column(sa.Float)
        o2t_95 = Column(sa.Float)
        o2t_96 = Column(sa.Float)
        o2t_97 = Column(sa.Float)
        o2t_98 = Column(sa.Float)
        o2t_99 = Column(sa.Float)

        o2t_100 = Column(sa.Float)
        o2t_101 = Column(sa.Float)
        o2t_102 = Column(sa.Float)
        o2t_103 = Column(sa.Float)
        o2t_104 = Column(sa.Float)
        o2t_105 = Column(sa.Float)
        o2t_106 = Column(sa.Float)
        o2t_107 = Column(sa.Float)
        o2t_108 = Column(sa.Float)
        o2t_109 = Column(sa.Float)

        o2t_110 = Column(sa.Float)
        o2t_111 = Column(sa.Float)
        o2t_112 = Column(sa.Float)
        o2t_113 = Column(sa.Float)
        o2t_114 = Column(sa.Float)
        o2t_115 = Column(sa.Float)
        o2t_116 = Column(sa.Float)
        o2t_117 = Column(sa.Float)
        o2t_118 = Column(sa.Float)
        o2t_119 = Column(sa.Float)

        o2t_120 = Column(sa.Float)
        o2t_121 = Column(sa.Float)
        o2t_122 = Column(sa.Float)
        o2t_123 = Column(sa.Float)
        o2t_124 = Column(sa.Float)
        o2t_125 = Column(sa.Float)
        o2t_126 = Column(sa.Float)
        o2t_127 = Column(sa.Float)
        o2t_128 = Column(sa.Float)
        o2t_129 = Column(sa.Float)

        o2t_130 = Column(sa.Float)
        o2t_131 = Column(sa.Float)
        o2t_132 = Column(sa.Float)
        o2t_133 = Column(sa.Float)
        o2t_134 = Column(sa.Float)
        o2t_135 = Column(sa.Float)
        o2t_136 = Column(sa.Float)
        o2t_137 = Column(sa.Float)
        o2t_138 = Column(sa.Float)
        o2t_139 = Column(sa.Float)

        o2t_140 = Column(sa.Float)
        o2t_141 = Column(sa.Float)
        o2t_142 = Column(sa.Float)
        o2t_143 = Column(sa.Float)
        o2t_144 = Column(sa.Float)
        o2t_145 = Column(sa.Float)
        o2t_146 = Column(sa.Float)
        o2t_147 = Column(sa.Float)
        o2t_148 = Column(sa.Float)
        o2t_149 = Column(sa.Float)

        o2t_150 = Column(sa.Float)
        o2t_151 = Column(sa.Float)
        o2t_152 = Column(sa.Float)
        o2t_153 = Column(sa.Float)
        o2t_154 = Column(sa.Float)
        o2t_155 = Column(sa.Float)
        o2t_156 = Column(sa.Float)
        o2t_157 = Column(sa.Float)
        o2t_158 = Column(sa.Float)
        o2t_159 = Column(sa.Float)

        o2t_160 = Column(sa.Float)
        o2t_161 = Column(sa.Float)
        o2t_162 = Column(sa.Float)
        o2t_163 = Column(sa.Float)
        o2t_164 = Column(sa.Float)
        o2t_165 = Column(sa.Float)
        o2t_166 = Column(sa.Float)
        o2t_167 = Column(sa.Float)
        o2t_168 = Column(sa.Float)
        o2t_169 = Column(sa.Float)

        o2t_170 = Column(sa.Float)
        o2t_171 = Column(sa.Float)
        o2t_172 = Column(sa.Float)
        o2t_173 = Column(sa.Float)
        o2t_174 = Column(sa.Float)
        o2t_175 = Column(sa.Float)
        o2t_176 = Column(sa.Float)
        o2t_177 = Column(sa.Float)
        o2t_178 = Column(sa.Float)
        o2t_179 = Column(sa.Float)

        o2t_180 = Column(sa.Float)
        o2t_181 = Column(sa.Float)
        o2t_182 = Column(sa.Float)
        o2t_183 = Column(sa.Float)
        o2t_184 = Column(sa.Float)
        o2t_185 = Column(sa.Float)
        o2t_186 = Column(sa.Float)
        o2t_187 = Column(sa.Float)
        o2t_188 = Column(sa.Float)
        o2t_189 = Column(sa.Float)

        o2t_190 = Column(sa.Float)
        o2t_191 = Column(sa.Float)
        o2t_192 = Column(sa.Float)
        o2t_193 = Column(sa.Float)
        o2t_194 = Column(sa.Float)
        o2t_195 = Column(sa.Float)
        o2t_196 = Column(sa.Float)
        o2t_197 = Column(sa.Float)
        o2t_198 = Column(sa.Float)
        o2t_199 = Column(sa.Float)

        o2t_200 = Column(sa.Float)
        o2t_201 = Column(sa.Float)
        o2t_202 = Column(sa.Float)
        o2t_203 = Column(sa.Float)
        o2t_204 = Column(sa.Float)
        o2t_205 = Column(sa.Float)
        o2t_206 = Column(sa.Float)
        o2t_207 = Column(sa.Float)
        o2t_208 = Column(sa.Float)
        o2t_209 = Column(sa.Float)

        # Energy

        e_0 = Column(sa.Float)
        e_1 = Column(sa.Float)
        e_2 = Column(sa.Float)
        e_3 = Column(sa.Float)
        e_4 = Column(sa.Float)
        e_5 = Column(sa.Float)
        e_6 = Column(sa.Float)
        e_7 = Column(sa.Float)
        e_8 = Column(sa.Float)
        e_9 = Column(sa.Float)

        e_10 = Column(sa.Float)
        e_11 = Column(sa.Float)
        e_12 = Column(sa.Float)
        e_13 = Column(sa.Float)
        e_14 = Column(sa.Float)
        e_15 = Column(sa.Float)
        e_16 = Column(sa.Float)
        e_17 = Column(sa.Float)
        e_18 = Column(sa.Float)
        e_19 = Column(sa.Float)

        e_20 = Column(sa.Float)
        e_21 = Column(sa.Float)
        e_22 = Column(sa.Float)
        e_23 = Column(sa.Float)
        e_24 = Column(sa.Float)
        e_25 = Column(sa.Float)
        e_26 = Column(sa.Float)
        e_27 = Column(sa.Float)
        e_28 = Column(sa.Float)
        e_29 = Column(sa.Float)

        e_30 = Column(sa.Float)
        e_31 = Column(sa.Float)
        e_32 = Column(sa.Float)
        e_33 = Column(sa.Float)
        e_34 = Column(sa.Float)
        e_35 = Column(sa.Float)
        e_36 = Column(sa.Float)
        e_37 = Column(sa.Float)
        e_38 = Column(sa.Float)
        e_39 = Column(sa.Float)

        e_40 = Column(sa.Float)
        e_41 = Column(sa.Float)
        e_42 = Column(sa.Float)
        e_43 = Column(sa.Float)
        e_44 = Column(sa.Float)
        e_45 = Column(sa.Float)
        e_46 = Column(sa.Float)
        e_47 = Column(sa.Float)
        e_48 = Column(sa.Float)
        e_49 = Column(sa.Float)

        e_50 = Column(sa.Float)
        e_51 = Column(sa.Float)
        e_52 = Column(sa.Float)
        e_53 = Column(sa.Float)
        e_54 = Column(sa.Float)
        e_55 = Column(sa.Float)
        e_56 = Column(sa.Float)
        e_57 = Column(sa.Float)
        e_58 = Column(sa.Float)
        e_59 = Column(sa.Float)

        e_60 = Column(sa.Float)
        e_61 = Column(sa.Float)
        e_62 = Column(sa.Float)
        e_63 = Column(sa.Float)
        e_64 = Column(sa.Float)
        e_65 = Column(sa.Float)
        e_66 = Column(sa.Float)
        e_67 = Column(sa.Float)
        e_68 = Column(sa.Float)
        e_69 = Column(sa.Float)

        e_70 = Column(sa.Float)
        e_71 = Column(sa.Float)
        e_72 = Column(sa.Float)
        e_73 = Column(sa.Float)
        e_74 = Column(sa.Float)
        e_75 = Column(sa.Float)
        e_76 = Column(sa.Float)
        e_77 = Column(sa.Float)
        e_78 = Column(sa.Float)
        e_79 = Column(sa.Float)

        e_80 = Column(sa.Float)
        e_81 = Column(sa.Float)
        e_82 = Column(sa.Float)
        e_83 = Column(sa.Float)
        e_84 = Column(sa.Float)
        e_85 = Column(sa.Float)
        e_86 = Column(sa.Float)
        e_87 = Column(sa.Float)
        e_88 = Column(sa.Float)
        e_89 = Column(sa.Float)

        e_90 = Column(sa.Float)
        e_91 = Column(sa.Float)
        e_92 = Column(sa.Float)
        e_93 = Column(sa.Float)
        e_94 = Column(sa.Float)
        e_95 = Column(sa.Float)
        e_96 = Column(sa.Float)
        e_97 = Column(sa.Float)
        e_98 = Column(sa.Float)
        e_99 = Column(sa.Float)

        e_100 = Column(sa.Float)
        e_101 = Column(sa.Float)
        e_102 = Column(sa.Float)
        e_103 = Column(sa.Float)
        e_104 = Column(sa.Float)
        e_105 = Column(sa.Float)
        e_106 = Column(sa.Float)
        e_107 = Column(sa.Float)
        e_108 = Column(sa.Float)
        e_109 = Column(sa.Float)

        e_110 = Column(sa.Float)
        e_111 = Column(sa.Float)
        e_112 = Column(sa.Float)
        e_113 = Column(sa.Float)
        e_114 = Column(sa.Float)
        e_115 = Column(sa.Float)
        e_116 = Column(sa.Float)
        e_117 = Column(sa.Float)
        e_118 = Column(sa.Float)
        e_119 = Column(sa.Float)

        e_120 = Column(sa.Float)
        e_121 = Column(sa.Float)
        e_122 = Column(sa.Float)
        e_123 = Column(sa.Float)
        e_124 = Column(sa.Float)
        e_125 = Column(sa.Float)
        e_126 = Column(sa.Float)
        e_127 = Column(sa.Float)
        e_128 = Column(sa.Float)
        e_129 = Column(sa.Float)

        e_130 = Column(sa.Float)
        e_131 = Column(sa.Float)
        e_132 = Column(sa.Float)
        e_133 = Column(sa.Float)
        e_134 = Column(sa.Float)
        e_135 = Column(sa.Float)
        e_136 = Column(sa.Float)
        e_137 = Column(sa.Float)
        e_138 = Column(sa.Float)
        e_139 = Column(sa.Float)

        e_140 = Column(sa.Float)
        e_141 = Column(sa.Float)
        e_142 = Column(sa.Float)
        e_143 = Column(sa.Float)
        e_144 = Column(sa.Float)
        e_145 = Column(sa.Float)
        e_146 = Column(sa.Float)
        e_147 = Column(sa.Float)
        e_148 = Column(sa.Float)
        e_149 = Column(sa.Float)

        e_150 = Column(sa.Float)
        e_151 = Column(sa.Float)
        e_152 = Column(sa.Float)
        e_153 = Column(sa.Float)
        e_154 = Column(sa.Float)
        e_155 = Column(sa.Float)
        e_156 = Column(sa.Float)
        e_157 = Column(sa.Float)
        e_158 = Column(sa.Float)
        e_159 = Column(sa.Float)

        e_160 = Column(sa.Float)
        e_161 = Column(sa.Float)
        e_162 = Column(sa.Float)
        e_163 = Column(sa.Float)
        e_164 = Column(sa.Float)
        e_165 = Column(sa.Float)
        e_166 = Column(sa.Float)
        e_167 = Column(sa.Float)
        e_168 = Column(sa.Float)
        e_169 = Column(sa.Float)

        e_170 = Column(sa.Float)
        e_171 = Column(sa.Float)
        e_172 = Column(sa.Float)
        e_173 = Column(sa.Float)
        e_174 = Column(sa.Float)
        e_175 = Column(sa.Float)
        e_176 = Column(sa.Float)
        e_177 = Column(sa.Float)
        e_178 = Column(sa.Float)
        e_179 = Column(sa.Float)

        e_180 = Column(sa.Float)
        e_181 = Column(sa.Float)
        e_182 = Column(sa.Float)
        e_183 = Column(sa.Float)
        e_184 = Column(sa.Float)
        e_185 = Column(sa.Float)
        e_186 = Column(sa.Float)
        e_187 = Column(sa.Float)
        e_188 = Column(sa.Float)
        e_189 = Column(sa.Float)

        e_190 = Column(sa.Float)
        e_191 = Column(sa.Float)
        e_192 = Column(sa.Float)
        e_193 = Column(sa.Float)
        e_194 = Column(sa.Float)
        e_195 = Column(sa.Float)
        e_196 = Column(sa.Float)
        e_197 = Column(sa.Float)
        e_198 = Column(sa.Float)
        e_199 = Column(sa.Float)

        e_200 = Column(sa.Float)
        e_201 = Column(sa.Float)
        e_202 = Column(sa.Float)
        e_203 = Column(sa.Float)
        e_204 = Column(sa.Float)
        e_205 = Column(sa.Float)
        e_206 = Column(sa.Float)
        e_207 = Column(sa.Float)
        e_208 = Column(sa.Float)
        e_209 = Column(sa.Float)

    class Entry(base):
        __tablename__ = 'iterlog'
        __table_args__ = {'schema': 'Logs', 'extend_existing': True}
        logid = Column(Integer, primary_key=True)
        owner = Column(String(1))
        n_qubits = Column(Integer)
        g = Column(sa.Float)
        layers = Column(Integer)
        ETA = Column(sa.Float)
        MAX_ITER = Column(Integer)
        init_type = Column(String(5))
        NUM_ROUNDS = Column(Integer)
        iter = Column(Integer)
        overlap = Column(sa.Float)
        energy = Column(sa.Float)
        norm_grad = Column(Numeric)
        vector = Column(String(4))
        angles = Column(String(200))
        log_start_time = Column(TIMESTAMP)
        atype = Column(String(10))

    class Round(base):
        __tablename__ = 'roundlog'
        __table_args__ = {'schema': 'Logs', 'extend_existing': True}
        logid = Column(Integer, primary_key=True)
        owner = Column(String(1))
        n_qubits = Column(Integer)
        g = Column(sa.Float)
        layers = Column(Integer)
        ETA = Column(sa.Float)
        MAX_ITER = Column(Integer)
        init_type = Column(String(5))
        NUM_ROUNDS = Column(Integer)
        round_id = Column(Integer)
        overlap = Column(sa.Float)
        energy = Column(sa.Float)
        ansatz = Column(String(4))
        params = Column(String(200))
        log_start_time = Column(TIMESTAMP)
        round_time = Column(sa.Float)
        atype = Column(String(10))

    class EMEntry(base):
        __tablename__ = 'emlog'
        __table_args__ = {'schema': 'Logs', 'extend_existing': True}
        logid = Column(Integer, primary_key=True)
        owner = Column(String(1))
        n_qubits = Column(Integer)
        g = Column(sa.Float)
        layers = Column(Integer)
        ETA = Column(sa.Float)
        MAX_ITER = Column(Integer)
        init_type = Column(String(5))
        iter = Column(Integer)
        overlap = Column(sa.Float)
        energy = Column(sa.Float)
        norm_grad = Column(sa.Float)
        vector = Column(String(4))
        angles = Column(String(200))
        log_start_time = Column(TIMESTAMP)
        atype = Column(String(10))

    class EMSummary(base):
        __tablename__ = 'emsummary'
        __table_args__ = {'schema': 'Logs', 'extend_existing': True}
        logid = Column(Integer, primary_key=True)
        owner = Column(String(1))
        n_qubits = Column(Integer)
        g = Column(sa.Float)
        layers = Column(Integer)
        ETA = Column(sa.Float)
        MAX_ITER = Column(Integer)
        init_type = Column(String(5))
        NUMITERS = Column(Integer)
        overlap = Column(sa.Float)
        energy = Column(sa.Float)
        vector = Column(String(4))
        angles = Column(String(200))
        log_start_time = Column(TIMESTAMP)
        total_time = Column(sa.Float)
        atype = Column(String(10))


else:
    continue_FLAG = False

    print('============================\n')
    print('If CUDA available? No')
    print('\n Program will fail')
    print('\n============================\n')


##################################
# Define Basics
##################################

# single qubit basis states |0> and |1>
q0 = np.array([[1], [0]])
q1 = np.array([[0], [1]])

# Pauli Matrices
I = np.array([[1, 0], [0, 1]])
X = np.array([[0, 1], [1, 0]])
Y = np.array([[0, -1j], [1j, 0]])
Z = np.array([[1, 0], [0, -1]])
HG = (1 / np.sqrt(2)) * np.array([[1, 1], [1, -1]])
CZ = np.array([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, -1]])

######################
#  HELPER FUNCTIONS
######################


# Creates the all zero input state.
def all_Zero_State(n_qubits):
    if n_qubits < 2:
        return 'Invalid Input : Specify at least 2 qubits'

    else:
        # Init State
        init_all_zero = np.kron(q0, q0)

        for t in range(n_qubits - 2):
            init_all_zero = np.kron(init_all_zero, q0)

        return init_all_zero


# Creates Random Initial State Ansatz
def psi0(n_qubits):
    if n_qubits < 2:
        return 'Invalid Input : Specify at least 2 qubits'

    else:
        pick = random.uniform(0, 2 * pi)
        i1 = np.cos(pick) * q0 + np.sin(pick) * q1
        pick = random.uniform(0, 2 * pi)
        i2 = np.cos(pick) * q0 + np.sin(pick) * q1
        init_random = np.kron(i1, i2)

        for t in range(n_qubits - 2):
            pick = random.uniform(0, 2 * pi)
            inow = np.cos(pick) * q0 + np.sin(pick) * q1
            init_random = np.kron(init_random, inow)

        return init_random


# Creates the equally superimposed product state from all zero state.
def equal_Superposition(n_qubits, init_all_zero):
    if n_qubits < 2:
        return 'Invalid Input : Specify at least 2 qubits'

    else:

        all_H = np.kron(HG, HG)

        for t in range(n_qubits - 2):
            all_H = np.kron(all_H, HG)

        equal_Superpos = all_H @ init_all_zero

        return equal_Superpos


# Analytical Ground State using Numpy's inbuilt eigh function.
def get_analytical_ground_state(H):
    e, v = LA.eigh(H)
    return np.min(e), v[:, np.argmin(e)]


# Create Unitary
def CU(Q, theta, n_qubits):
    Id = np.eye(2 ** n_qubits)
    return np.cos(theta) * Id - 1j * np.sin(theta) * Q


# Create Unitary within CUDA
def CUTensor(Q, theta, n_qubits):
    Id = torch.eye(2 ** n_qubits, dtype=torch.cdouble, device=cuda0)
    U = np.cos(theta) * Id - 1j * np.sin(theta) * \
        torch.tensor(Q, dtype=torch.cdouble, device=cuda0)
    return U


# Ansatz - MFIM
def ansatz_vha(
        X_param_set,
        ZZ_param_set,
        Z_param_set,
        components,
        n_qubits,
        layers):
    # Initialize Ansatz to I
    ansatz = np.eye(2 ** n_qubits)

    ZZ_components = components[0]
    X_components = components[1]
    Z_components = components[2]

    for layer in range(layers):

        for ct1, comp1 in enumerate(ZZ_components):
            ansatz = CU(
                comp1,
                theta=ZZ_param_set[layer],
                n_qubits=n_qubits) @ ansatz

        for ct2, comp2 in enumerate(X_components):
            ansatz = CU(
                comp2,
                theta=X_param_set[layer],
                n_qubits=n_qubits) @ ansatz

        for ct3, comp3 in enumerate(Z_components):
            ansatz = CU(
                comp3,
                theta=Z_param_set[layer],
                n_qubits=n_qubits) @ ansatz

    return ansatz


##################################
# Define MFIM model
##################################

# helper funnction for TFIM model creation.
def component_sums(components, n_qubits):
    ZZ_sum = np.zeros((2 ** n_qubits, 2 ** n_qubits))
    X_sum = np.zeros((2 ** n_qubits, 2 ** n_qubits))
    Z_sum = np.zeros((2 ** n_qubits, 2 ** n_qubits))

    for zz_arr in components[0]:
        ZZ_sum += zz_arr

    for x_arr in components[1]:
        X_sum += x_arr

    for z_arr in components[2]:
        Z_sum += z_arr

    return ZZ_sum, X_sum, Z_sum


# Coding
def array_coding_to_kron(arr, type):
    n_qubits = len(arr)

    if type == 'ZZ':
        convert = {0: I, 1: Z}  # Dictionary that maps code to Pauli Matrix
        expr = np.kron(convert[arr[0]], convert[arr[1]])
        for t in range(2, n_qubits):
            expr = np.kron(expr, convert[arr[t]])

        return expr

    else:
        if type == 'X':
            convert = {0: I, 1: X}
            expr2 = np.kron(convert[arr[0]], convert[arr[1]])
            for k in range(2, n_qubits):
                expr2 = np.kron(expr2, convert[arr[k]])

            return expr2
        else:
            convert = {0: I, 1: Z}
            expr3 = np.kron(convert[arr[0]], convert[arr[1]])
            for m in range(2, n_qubits):
                expr3 = np.kron(expr3, convert[arr[m]])

            return expr3


# MFIM
def create_MFIM(n_qubits, g):
    if n_qubits == 2:
        return -1 * np.kron(Z, Z) - g * (np.kron(X, I) + np.kron(I, X)) - g * (np.kron(Z, I) + np.kron(I, Z)), {
            0: [np.kron(Z, Z)],
            1: [np.kron(X, I), np.kron(I, X)],
            2: [np.kron(Z, I), np.kron(I, Z)]
        }

    else:
        # This will store all the kronecker products used in Ansatz Layers
        comps = {0: [], 1: [], 2: []}

        # Initializing an empty
        mfim = np.zeros((2 ** n_qubits, 2 ** n_qubits))

        # Encode ZZ Terms
        for i in range(n_qubits):
            zz_arr = np.zeros(n_qubits)
            if i < n_qubits - 1:
                zz_arr[i] = 1
                zz_arr[i + 1] = 1
            else:
                zz_arr[0] = 1
                zz_arr[i] = 1

            # Call the coding function
            mfim = mfim - array_coding_to_kron(zz_arr, type='ZZ')
            # Append component
            comps[0].append(array_coding_to_kron(zz_arr, type='ZZ'))

        # X Terms
        for i in range(n_qubits):
            x_arr = np.zeros(n_qubits)
            x_arr[i] = 1

            # Call the coding function
            mfim = mfim - g * array_coding_to_kron(x_arr, type='X')
            # Append component
            comps[1].append(array_coding_to_kron(x_arr, type='X'))

        # Z Terms
        for i in range(n_qubits):
            z_arr = np.zeros(n_qubits)
            z_arr[i] = 1

            # Call the coding function
            mfim = mfim - g * array_coding_to_kron(z_arr, type='Z')
            # Append component
            comps[2].append(array_coding_to_kron(z_arr, type='Z'))

        return mfim, comps


# This funaction calculates the overlap of the solution with analytical
# ground state.
def overlap_calculator(min_pm, ground_st):
    return np.abs(np.vdot(min_pm, ground_st)) ** 2


def power_computation(H, circuit_input):
    return (1 / (LA.norm(H @ circuit_input))) * (H @ circuit_input)


def energy_raw(H, psi):
    return np.real((psi.conj().T) @ H @ psi)[0][0]


# Helper functions to compute derivative

def all_X(X_components, param, n_qubits):
    X = torch.eye(2 ** n_qubits, dtype=torch.cdouble, device=cuda0)
    for component in X_components:
        X = CUTensor(component, param, n_qubits=n_qubits) @ X
    return X


def all_ZZ(ZZ_components, param, n_qubits):
    ZZ = torch.eye(2 ** n_qubits, dtype=torch.cdouble, device=cuda0)
    for component in ZZ_components:
        ZZ = CUTensor(component, param, n_qubits=n_qubits) @ ZZ
    return ZZ


def all_Z(Z_components, param, n_qubits):
    Z = torch.eye(2 ** n_qubits, dtype=torch.cdouble, device=cuda0)
    for component in Z_components:
        Z = CUTensor(component, param, n_qubits=n_qubits) @ Z
    return Z

# Send Component sums one time to GPU


def send_bulk_gpu(components, n_qubits, kind):
    try:

        # This is implemented via a function call.
        sum_ZZ, sum_X, sum_Z = component_sums(components, n_qubits=n_qubits)

        if kind == 'OM':
            # Send to CUDA
            cuda_sum_iZZ = torch.as_tensor(
                1j * sum_ZZ, dtype=torch.cdouble, device=cuda0)
            cuda_sum_iX = torch.as_tensor(
                1j * sum_X, dtype=torch.cdouble, device=cuda0)
            cuda_sum_iZ = torch.as_tensor(
                1j * sum_Z, dtype=torch.cdouble, device=cuda0)

            return cuda_sum_iZZ, cuda_sum_iX, cuda_sum_iZ
        else:
            cuda_sum_ZZ = torch.as_tensor(
                sum_ZZ, dtype=torch.cdouble, device=cuda0)
            cuda_sum_X = torch.as_tensor(
                sum_X, dtype=torch.cdouble, device=cuda0)
            cuda_sum_Z = torch.as_tensor(
                sum_Z, dtype=torch.cdouble, device=cuda0)

            return cuda_sum_ZZ, cuda_sum_X, cuda_sum_Z

    except Exception as e:
        print('\n ==== Error Sending to GPU===== \n')
        print(e)


def grad_positioning(grad):
    ZZ = []
    X = []
    Z = []
    for i in range(len(grad)):
        if i % 3 == 0:
            ZZ.append(grad[i])
        else:
            if i % 3 == 1:
                X.append(grad[i])
            else:
                Z.append(grad[i])

    return np.array(ZZ), np.array(X), np.array(Z)


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

# OM

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


# Gradient - Harrow Napp
def grad_power(
    b0,
    b,
    X_param_set,
    ZZ_param_set,
    Z_param_set,
    components,
    n_qubits,
    layers,
    cuda_szz,
    cuda_sx,
        cuda_sz):

    # Prepare the common right hand side for the derivative in GPU
    psi_right = torch.as_tensor(
        b0 @ (b.conj().T), dtype=torch.cdouble, device=cuda0)

    # Prepare fixed parts of the overall derivative in CPU - Used just once.
    anz = ansatz_vha(
        X_param_set,
        ZZ_param_set,
        Z_param_set,
        components,
        n_qubits,
        layers)
    rpart = np.real((b.conj().T) @ anz @ b0)
    ipart = np.imag((b.conj().T) @ anz @ b0)

    # Sum the ZZ and X components in GPU
    # sum_ZZ, sum_X, sum_Z = component_sums(components, n_qubits=n_qubits)  #
    # This is implemented via a function call.

    # Total parameters
    param_per_layer = 3  # We always have 2 params per layer for VHA Ansatz.
    # This is just initialization for the gradient vector
    full_derivative = np.zeros(param_per_layer * layers)

    # Derivative Expression for each param
    for j in range(layers):

        # initialize computation for the jth ZZ derivative
        deriv = torch.eye(2 ** n_qubits, dtype=torch.cdouble, device=cuda0)
        deriv2 = torch.eye(2 ** n_qubits, dtype=torch.cdouble, device=cuda0)
        deriv3 = torch.eye(2 ** n_qubits, dtype=torch.cdouble, device=cuda0)

        # This inner loop is to loop through the circuit elements, only one of
        # the ZZ elements will have a derivative
        for i in range(layers):
            all_Xs = all_X(components[1], X_param_set[i], n_qubits=n_qubits)
            all_ZZs = all_ZZ(components[0], ZZ_param_set[i], n_qubits=n_qubits)
            all_Zs = all_Z(components[2], Z_param_set[i], n_qubits=n_qubits)

            if i == j:
                deriv = all_Zs @ all_Xs @ all_ZZs @ cuda_szz @ deriv
                deriv2 = all_Zs @ all_Xs @ cuda_sx @ all_ZZs @ deriv2
                deriv3 = all_Zs @ cuda_sz @ all_Xs  @ all_ZZs @ deriv3
            else:
                deriv = all_Zs @ all_Xs @ all_ZZs @ deriv
                deriv2 = all_Zs @ all_Xs @ all_ZZs @ deriv2
                deriv3 = all_Zs @ all_Xs @ all_ZZs @ deriv3

        trace_deriv = torch.trace(
            deriv @ psi_right).cpu().detach().numpy()  # Send to CPU
        trace_deriv2 = torch.trace(
            deriv2 @ psi_right).cpu().detach().numpy()  # Send to CPU
        trace_deriv3 = torch.trace(
            deriv3 @ psi_right).cpu().detach().numpy()  # Send to CPU

        # Store
        full_derivative[j *
                        param_per_layer] = (2 *
                                            rpart *
                                            np.real(trace_deriv) +
                                            2 *
                                            ipart *
                                            np.imag(trace_deriv))[0][0]  # CPU
        full_derivative[j *
                        param_per_layer +
                        1] = (2 *
                              rpart *
                              np.real(trace_deriv2) +
                              2 *
                              ipart *
                              np.imag(trace_deriv2))[0][0]  # CPU
        full_derivative[j *
                        param_per_layer +
                        2] = (2 *
                              rpart *
                              np.real(trace_deriv3) +
                              2 *
                              ipart *
                              np.imag(trace_deriv3))[0][0]  # CPU

    # Return all partial derivatives
    return full_derivative


def grad_positioning(grad):
    ZZ = []
    X = []
    Z = []
    for i in range(len(grad)):
        if i % 3 == 0:
            ZZ.append(grad[i])
        else:
            if i % 3 == 1:
                X.append(grad[i])
            else:
                Z.append(grad[i])

    return np.array(ZZ), np.array(X), np.array(Z)


def grad_descent(
    b0,
    b,
    components,
    X_param_set,
    ZZ_param_set,
    Z_param_set,
    MAXITERS,
    eta,
    GRADTOL,
    n_qubits,
    layers,
    time_start,
    cuda_iZZ,
    cuda_iX,
    cuda_iZ,
    log_freq=1,
    plotting='off',
        logging='off'):

    store_grad_norm = []
    store_vecs = []
    store_energy = []

    # Theta is a vector ---> np.array
    theta_X = X_param_set.copy()
    theta_ZZ = ZZ_param_set.copy()
    theta_Z = Z_param_set.copy()

    # Keep track of number of iterations
    counter = 0

    # Iterate
    for iter in range(MAXITERS):

        grad = grad_power(
            b0,
            b,
            X_param_set=theta_X,
            ZZ_param_set=theta_ZZ,
            Z_param_set=Z_param_set,
            components=components,
            n_qubits=n_qubits,
            layers=layers,
            cuda_szz=cuda_iZZ,
            cuda_sx=cuda_iX,
            cuda_sz=cuda_iZ)

        if LA.norm(grad) < GRADTOL:
            break

        # Extract components - This is to correctly order gradient components
        ZZ, X, Z = grad_positioning(grad)

        # Update thetas - Grad Ascent
        theta_ZZ = theta_ZZ - eta * ZZ
        theta_X = theta_X - eta * X
        theta_Z = theta_Z - eta * Z

        # Eigenvector
        v = ansatz_vha(
            X_param_set=theta_X,
            ZZ_param_set=theta_ZZ,
            Z_param_set=Z_param_set,
            components=components,
            n_qubits=n_qubits,
            layers=layers) @ b0

        # Store The Vector
        store_vecs.append(v)

        # Overlap
        ov = overlap_calculator(v, v_an)

        # Energy
        ev = energy_raw(H, v)
        store_energy.append(ev)

        # Some Periodic Logging on Terminal for large N --> if requested.
        if logging == 'on':

            # Log every log_freq steps.
            if counter % log_freq == 0:
                vals_now = Entry(
                    owner='R',
                    n_qubits=n_qubits,
                    g=g,
                    layers=layers,
                    ETA=eta,
                    MAX_ITER=MAXITERS,
                    NUM_ROUNDS=ROUNDS,
                    init_type='eq',
                    iter=counter,
                    overlap=ov,
                    energy=ev,
                    norm_grad=LA.norm(grad),
                    vector='none',
                    angles=str([theta_ZZ, theta_X, theta_Z]),
                    log_start_time=time_start,
                    atype='m-hva-mfim')

                session.add(vals_now)
                session.commit()

        # Keep track of number of iterations
        counter += 1

        # Store Gradient Norm and Energy
        store_grad_norm.append(LA.norm(grad))

    # Some Plotting --> if requested.
    if plotting == 'on':
        plt.plot(range(counter), store_grad_norm)
        plt.title('Track Gradient Norm')
        plt.xlabel('Iteration Number')
        plt.ylabel('L2 Norm of the Gradient')
        plt.show()

    return [theta_ZZ, theta_X, theta_Z], counter, v, LA.norm(
        grad), store_grad_norm, store_vecs, store_energy


def serial_statistics(session, n, g, l, v_hist, e_hist):

    v_final = v_hist[20][9]

    v_olap_t1 = []
    v_olap_t2 = []
    e = []

    for i in range(21):
        for j in range(10):
            v_olap_t1.append(overlap_calculator(v_hist[i][j], v_final))
            e.append(e_hist[i][j])
            if j < 9 and i <= 20:
                v_olap_t2.append(overlap_calculator(
                    v_hist[i][j], v_hist[i][j + 1]))
            else:
                if j == 9 and i < 20:
                    v_olap_t2.append(overlap_calculator(
                        v_hist[i][j], v_hist[i + 1][0]))
                else:
                    v_olap_t2.append(1)

    # Write everything to Database
    insert_data = {'n_qubits': n, 'g': g, 'l': l, 'ref': 'OM-MODEL'}
    for h in range(len(v_olap_t1)):
        insert_data['o1t_' + str(h)] = v_olap_t1[h]
        insert_data['o2t_' + str(h)] = v_olap_t2[h]
        insert_data['e_' + str(h)] = e[h]

    om_model_data = OM_MODEL(**insert_data)
    session.add(om_model_data)
    session.commit()


if continue_FLAG:

    ETA = 0.005

    # Update as necessary - Only used for Overlap Maximization
    OFFSET_DICT = {4: 15, 6: 30, 8: 50, 10: 40, 12: 100}
    LAYERS_DICT = {
        4: [
            2, 4, 6], 6: [
            2, 4, 6, 8], 8: [
                2, 4, 6, 8, 10], 10: [
                    2, 4, 6, 8, 10], 12: [
                        2, 4, 6, 8, 10, 12]}  # Update as necessary - Specifying the layers for MFIM Model
    OWNER = 'R'  # For Justin = 'J', For Ronak = 'R'
    # .strftime('%Y-%m-%d %H:%M:%S') #If using Postgres instead of SQLite then uncomment the strftime part.
    tstart = dt.datetime.now()
    # Important - We identify a run by this value. Save this somewhere.
    print(tstart)

    btype = 'eq'

    for n_qubit in [8]:
        print('MFIM - Now At n_qubit = ', n_qubit)

        for g in [0.5, 1.0, 2.0]:
        #for g in np.linspace(0.1, 2.0, 150):

            print('Now At g = ', g)

            # Create the MFIM Model
            H, components = create_MFIM(n_qubits=n_qubit, g=g)

            # Diagonalize
            e_an, v_an = get_analytical_ground_state(H)

            # Send components to GPU
            SZZ, SX, SZ = send_bulk_gpu(components, n_qubit, kind='OM')

            for MAX_ITER in [10]:
                for ROUNDS in [20]:
                    for layers in LAYERS_DICT[n_qubit]:
                        print('At layer = ', layers)

                        total_v_history = []
                        total_e_history = []

                        ov = 0  		# Just an initialization. Don't change
                        MAXIMUM = 0  		# Just an initialization. Don't change
                        TOL = 0.0001  		# Just an initialization. Don't change

                        ZZ_param_set = (pi / 3) * np.ones(layers)
                        X_param_set = (pi / 3) * np.ones(layers)
                        Z_param_set = (pi / 3) * np.ones(layers)

                        if btype == 'eq':
                            b0_e = equal_Superposition(
                                n_qubit, all_Zero_State(n_qubit))
                        else:
                            b0_e = psi0(n_qubit)

                        # or b0 or b0_r                    #Select psi0 for our
                        # ansatz. One of the three choices.
                        b0_now = b0_e

                        Hprime = H - \
                            OFFSET_DICT[n_qubit] * np.eye(2 ** n_qubit)
                        in_vec = Hprime @ b0_now / LA.norm(Hprime @ b0_now)

                        while (ov <= 0.99999 and MAXIMUM <= ROUNDS):
                            round_start_time = time.time()
                            p, _, vec, grad, g_hist, v_hist, e_hist = grad_descent(b0_e, in_vec, components, X_param_set,
                                                                                   ZZ_param_set, Z_param_set, MAX_ITER, ETA, TOL, n_qubit,
                                                                                   layers, tstart, SZZ, SX, SZ, 1,
                                                                                   'off', 'on')
                            round_end_time = time.time()
                            round_time = (
                                round_end_time - round_start_time) / 60

                            #print('OM - 1 round took = ', round_time, ' mins')

                            ov = overlap_calculator(vec, v_an)
                            ev = energy_raw(H, vec)
                            in_vec = Hprime @ vec / LA.norm(Hprime @ vec)

                            # Log at round level

                            vals_round = Round(
                                owner=OWNER,
                                n_qubits=n_qubit,
                                g=g,
                                layers=layers,
                                ETA=ETA,
                                MAX_ITER=MAX_ITER,
                                NUM_ROUNDS=ROUNDS,
                                init_type=btype,
                                round_id=MAXIMUM,
                                overlap=ov,
                                energy=ev,
                                ansatz='none',
                                params=str(p),
                                log_start_time=tstart,
                                round_time=round_time,
                                atype='m-hva-mfim')

                            session.add(vals_round)
                            session.commit()

                            # Push the iteration level vector_history to
                            # storage
                            total_v_history.append(v_hist)
                            total_e_history.append(e_hist)

                            MAXIMUM += 1

                        #v_final = total_v_history[20][9]
                        # This is call for calculation and storage
                        serial_statistics(
                            session, n_qubit, g, layers, total_v_history, total_e_history)

    # Close Database Session
    session.close()


else:
    print('==================================================\n')
    print('Sorry no GPU available for use. PROGRAM TERMINATED\n')
    print('==================================================\n')