From 3d45e1ef5c5dae6af7b3af3e09a4729bc5c4d0ff Mon Sep 17 00:00:00 2001 From: knzwnao Date: Mon, 30 Mar 2020 15:40:47 +0900 Subject: [PATCH] update ignis RB overview for api changes --- ignis/RB_overview.ipynb | 150 ++++++++++++++++++++++++---------------- 1 file changed, 90 insertions(+), 60 deletions(-) diff --git a/ignis/RB_overview.ipynb b/ignis/RB_overview.ipynb index a1e5e7a..043d8bb 100644 --- a/ignis/RB_overview.ipynb +++ b/ignis/RB_overview.ipynb @@ -15,10 +15,11 @@ "\n", "### Contributors\n", "\n", - "Shelly Garion$^{1}$, Yael Ben-Haim$^{1}$ and David McKay$^{2}$\n", + "Shelly Garion$^{1}$, Yael Ben-Haim$^{1}$, Naoki Kanazawa$^{2}$ and David McKay$^{3}$\n", "\n", "1. IBM Research Haifa, Haifa University Campus, Mount Carmel Haifa, Israel\n", - "2. IBM T.J. Watson Research Center, Yorktown Heights, NY, USA\n" + "2. IBM Research Tokyo, Hakozaki-cho, Chuo-ku, Tokyo, Japan\n", + "3. IBM T.J. Watson Research Center, Yorktown Heights, NY, USA\n" ] }, { @@ -138,24 +139,24 @@ "name": "stdout", "output_type": "stream", "text": [ - " ┌───┐┌─────┐┌───┐┌───┐ ░ ┌───┐ ┌───┐ ┌───┐┌───┐»\n", - "qr_0: |0>───────────────────■──┤ X ├┤ Sdg ├┤ H ├┤ Z ├─░─┤ Z ├─┤ H ├─┤ S ├┤ X ├»\n", - " ┌───┐┌─────┐┌───┐┌─┴─┐└─┬─┘└┬───┬┘├───┤├───┤ ░ ├───┤┌┴───┴┐├───┤└─┬─┘»\n", - "qr_1: |0>┤ H ├┤ Sdg ├┤ H ├┤ X ├──■───┤ H ├─┤ S ├┤ X ├─░─┤ X ├┤ Sdg ├┤ H ├──■──»\n", - " └───┘└─────┘└───┘└───┘ └───┘ └───┘└───┘ ░ └───┘└─────┘└───┘ »\n", + " ┌───┐┌───┐┌───┐ ┌───┐┌───┐ ░ ┌─────┐ ┌───┐ ┌─────┐»\n", + "qr_0: |0>┤ H ├┤ H ├┤ S ├──■──┤ H ├┤ S ├──────░─┤ Sdg ├─┤ H ├────────■──┤ Sdg ├»\n", + " ├───┤├───┤├───┤┌─┴─┐├───┤├───┤┌───┐ ░ └┬───┬┘┌┴───┴┐┌───┐┌─┴─┐├─────┤»\n", + "qr_1: |0>┤ H ├┤ H ├┤ S ├┤ X ├┤ H ├┤ S ├┤ X ├─░──┤ X ├─┤ Sdg ├┤ H ├┤ X ├┤ Sdg ├»\n", + " └───┘└───┘└───┘└───┘└───┘└───┘└───┘ ░ └───┘ └─────┘└───┘└───┘└─────┘»\n", " cr_0: 0 ═════════════════════════════════════════════════════════════════════»\n", " »\n", " cr_1: 0 ═════════════════════════════════════════════════════════════════════»\n", " »\n", - "« ┌─┐ \n", - "«qr_0: ──■──┤M├──────────────────\n", - "« ┌─┴─┐└╥┘┌───┐┌───┐┌───┐┌─┐\n", - "«qr_1: ┤ X ├─╫─┤ H ├┤ S ├┤ H ├┤M├\n", - "« └───┘ ║ └───┘└───┘└───┘└╥┘\n", - "«cr_0: ══════╩═════════════════╬═\n", - "« ║ \n", - "«cr_1: ════════════════════════╩═\n", - "« \n" + "« ┌───┐┌───┐┌─┐ \n", + "«qr_0: ┤ H ├┤ H ├┤M├───\n", + "« ├───┤├───┤└╥┘┌─┐\n", + "«qr_1: ┤ H ├┤ H ├─╫─┤M├\n", + "« └───┘└───┘ ║ └╥┘\n", + "«cr_0: ═══════════╩══╬═\n", + "« ║ \n", + "«cr_1: ══════════════╩═\n", + "« \n" ] } ], @@ -194,10 +195,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[0.+1.j 0.+0.j 0.+0.j 0.+0.j]\n", - " [0.+0.j 0.+1.j 0.+0.j 0.+0.j]\n", - " [0.+0.j 0.+0.j 0.+1.j 0.+0.j]\n", - " [0.+0.j 0.+0.j 0.+0.j 0.+1.j]]\n" + "[[-0.-1.j 0.+0.j -0.-0.j 0.+0.j]\n", + " [ 0.+0.j -0.-1.j 0.+0.j -0.+0.j]\n", + " [ 0.+0.j 0.-0.j -0.-1.j -0.-0.j]\n", + " [ 0.-0.j 0.-0.j -0.+0.j -0.-1.j]]\n" ] } ], @@ -297,19 +298,19 @@ "text": [ "Compiling seed 0\n", "Simulating seed 0\n", - "After seed 0, alpha: 0.976775, EPC: 0.017419\n", + "After seed 0, alpha: 0.979619, EPC: 0.015285\n", "Compiling seed 1\n", "Simulating seed 1\n", - "After seed 1, alpha: 0.980139, EPC: 0.014896\n", + "After seed 1, alpha: 0.978386, EPC: 0.016211\n", "Compiling seed 2\n", "Simulating seed 2\n", - "After seed 2, alpha: 0.979076, EPC: 0.015693\n", + "After seed 2, alpha: 0.980224, EPC: 0.014832\n", "Compiling seed 3\n", "Simulating seed 3\n", - "After seed 3, alpha: 0.980159, EPC: 0.014881\n", + "After seed 3, alpha: 0.978546, EPC: 0.016090\n", "Compiling seed 4\n", "Simulating seed 4\n", - "After seed 4, alpha: 0.980134, EPC: 0.014899\n" + "After seed 4, alpha: 0.977678, EPC: 0.016742\n" ] } ], @@ -318,15 +319,16 @@ "backend = qiskit.Aer.get_backend('qasm_simulator')\n", "basis_gates = ['u1','u2','u3','cx'] \n", "shots = 200\n", - "qobj_list = []\n", + "transpiled_circs_list = []\n", "rb_fit = rb.RBFitter(None, xdata, rb_opts['rb_pattern'])\n", - "for rb_seed,rb_circ_seed in enumerate(rb_circs):\n", + "for rb_seed, rb_circ_seed in enumerate(rb_circs):\n", " print('Compiling seed %d'%rb_seed)\n", " new_rb_circ_seed = qiskit.compiler.transpile(rb_circ_seed, basis_gates=basis_gates)\n", - " qobj = qiskit.compiler.assemble(new_rb_circ_seed, shots=shots)\n", + " transpiled_circs_list.append(new_rb_circ_seed)\n", + " job = qiskit.execute(new_rb_circ_seed, backend, shots=shots,\n", + " noise_model=noise_model,\n", + " backend_options={'max_parallel_experiments': 0})\n", " print('Simulating seed %d'%rb_seed)\n", - " job = backend.run(qobj, noise_model=noise_model, backend_options={'max_parallel_experiments': 0})\n", - " qobj_list.append(qobj)\n", " # Add data to the fitter\n", " rb_fit.add_data(job.result())\n", " print('After seed %d, alpha: %f, EPC: %f'%(rb_seed,rb_fit.fit[0]['params'][1], rb_fit.fit[0]['epc']))" @@ -346,7 +348,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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HD6dnz54kJSWxcuVK+vfvz6RJk1i+fLnP9T958iTVqlXjmWee4ZprrvF5vf/7v//jrrvuonv37qSkpLB8+XJ69+7tmt+lS5dcdbz77rupX78+0dHRubbXs2dPmjVr5vP+8/Paa68xatQo3nnnHVasWEH16tXp2LEjR44cAWDjxo1kZGTw/vvvs27dOt555x2mTJmS67a08ePHc/XVV1O/fn1XWXp6Ok2bNmX06NGEhob6FM+ff/5Jx44diYiIYMWKFYwePZrXX3+dN95445zr2rVrV1atWsXs2bOZPXs2q1atonv37q75ZcuWpUePHsydO5dNmzbx1ltvMWHCBLcDmeDgYLp27crbb799zvHkSVVL7NSyZUstDEuXLtUpU6boa6+9pj/88IN+0aOHKqiCHg+vqnr8eKHsp7gsWLCgWPe3bNkyBbRVq1a55n333Xfapk0brVSpklauXFk7deqk69evd1sG0JkzZ6qqampqqgI6ffp0jY2N1eDgYL3ssst0zpw5ruUXLFiggH7//fd69dVXa2hoqLZs2VJXrlzpWmb//v161113aa1atTQkJEQbN26sEydOLHDdnnrqKb300kvdynr37u2xrlkGDhyozZo1cyv7+uuvNSQkRP/880+P6+zcuVODgoJ0+vTprrKZM2dqUFCQnjlzxlX2ww8/KKC///67qqru27dPRURXr17tcbsrVqxQQFNTU/OspzfNmzfXZ555xvU+MzNTR4wYofXr19eQkBBt2rSpTp06Nc9tfPvttyoiunPnTlfZ1KlTNTg4WA8fPux1vdtvv13vuusut7LJkydrrVq18txfjx499MYbb8xzmYsvvlhff/11t7IGDRq49jdixAgVEV2xYkWudTMyMvKMOy833nij9ujRI9/lzpw5o3Xq1NFx48b5vO2jR49qxYoVdfjw4bnmvfXWW3rttdfq/Pnz3b4/WZKSkjQuLk5DQ0M1MjJSH3zwwTzrmJmZqTVq1NCXXnrJVXbs2DENDw/XsWPHel1vzJgxWqVKFbeyJk2a6FtvveV1nbCwMJ00aZLX+Vnee+89LV++vB47dsxV9uKLL2pkZKRmZma6yiZOnKiNGjXS4OBgbdCggb7xxhuakZHhdbvr169XQJcuXeoqW7JkiQK6ceNGr+s98cQTuf5PLFq0SMuWLatHjx51K09OTlZAW7RokW89VVWBZPWSF+1M3wexsbHUqVOH6OhoFi9eTKW77+ZIlboABB1LBw+Xyoxvjh49yuOPP85PP/3EwoULqVixIjfffLPbZS9PnnrqKfr27cvq1avp2LEjt956K7/99pvbMgMHDuTVV19l1apVVK1alW7durku5Z04cYIWLVrwn//8h3Xr1tGvXz/69OnD/PnzXetnXR7N69L38uXL6dSpk1tZ586dSU5O5vTp0x7XOXnyJCEhIW5loaGhnDhxgpUrV3pcZ8KECVSuXJk777zTVXbVVVdRpkwZxo8fT0ZGBkeOHGHy5MlcddVVro4/S5cuJTg4mKZNm3qtw1+hqsyfP59NmzYRFxfnKh80aBATJkxgzJgxrF+/noEDB9KnTx9mzZrldVvLly+nUaNG1KlTx1XWuXNnTp486fXzSElJYdmyZbRr186t/Oqrr+a3335j69at51jD3EJCQly/0+nTp9OhQwePZ8xBQUFUqFAB8O079FesXLmSXbt2UbZsWVq0aEGNGjXo1KkTKSkpXtf57LPPOHr0aK628ZSUFEaMGMGUKVM8Pjl07dq1dOrUiVtuuYWff/6ZL774gtWrV+fZxp6amsqePXvc/jZCQ0OJi4tj2bJlXtf7888/qVy5suv9gQMHWL9+vcfPuaCWL19O27Zt3a4MdO7c2TnM+nYAPvzwQ5599lmGDRvGhg0bGDVqFCNGjHC7ouRpu+Hh4bRu3dpVFhsbS1hYmNe6/u9//2P27Nm5vr/R0dGcOXOmQFeKCszb0UBJmArrTF9V9YsvvnCd6b/22mu64/lX9dlSr+hF7NNduwptN8UikM70c0pPT9egoCBdsmSJqwwPZ/rZzyAyMjK0QYMG+txzz6nq2TP92bNnu5ZZunSpArorj19Wly5dtHfv3q73X3zxhV522WX666+/el2nQYMGOnToULeyRYsWKaBpaWke15kzZ46KiE6dOlVPnz6tv/76q7Zt21YB/fjjj3Mtn3VW9/jjj+eat3jxYo2IiNBSpUqpiGiLFi107969rvlvvvmm1q1b12v8BT3TP3TokIaFhWnp0qU1ODhYx48f75qXnp6uISEhunjxYrd1+vXrp9dff73Xbd5///0aHx/vVpaZmamlSpXK9XnUqlVLy5Ytq0FBQbk+d1XVw4cPu67yeNOjRw8tVaqUhoWFuU1PPfWUa5nsZ/qnT5/WSZMmKaDvvfeeqqqGhoZq3759ve4jiy/foex8PdP/5JNPFNC6devqzJkzNTk5WXv27KkVKlTw+r2LiYnR2267za0sPT1dGzZsqImJiap69m8n+5l+9+7dtVevXm7rpaSkKOD2XcsuKSlJAd2xY4dbec+ePbVTp04e19m+fbtWrVpVR40alWs/27Zt8/JJ+H6m37FjR+3Zs6db2Y4dOxTQZcuWqapqnTp1dMqUKW7LvPnmm9qoUSOv2x0+fLhGRUXlKo+KitKXX37ZrSwmJkaDg4MV0Pvvv9/jFYTKlSu7/V2p2pl+sUtNTWXDhg0kJCQQHx9PQkICM8oHkdLpMX7nIj7+2N8Rnr+2bt1K165dueSSS6hQoQIRERFkZmayM58OkjExMa7XQUFBXHPNNaxfv95tmSuvvNL1OuuJiFmd4DIyMhg+fDhXXnklVatWJTw8nC+++MJtv7fffjsbN26kVq1a51zP7Dp16sTIkSN55JFHCAkJoWHDhq52eE9nWrNnz2bXrl3cf//9buV79uyhd+/e3HPPPaxYsYKFCxdSvnx5/vnPf7oe+3z8+PFcVxXORfny5Vm9ejUrVqxg+PDh9O/f33V1ZP369Zw4cYLrrruO8PBw1/T++++7zryvv/56V3mTJk0KvP8lS5aQnJzM2LFjeeutt5g6darb/KyzuJydwXKKi4tj9erVbtOAAQPclnnuuecIDw8nNDSURx55hAEDBtCnTx8Anzt/FdV3KOv3+9xzz5GQkEDLli0ZN24cFStW9HiXxrp161i+fHmu71Dfvn1p06aN2xWknFauXMm0adPcfqdZg5Jt3bqV6dOnu837Kx3R9u7dy3XXXUfHjh154oknXOVZv8fC/A578/vvv7Nr1y769OnjVp9nnnnG9f198MEH3eYV1IwZM1i1ahUff/wx3377LSNGjMi1TGhoaL7f33NhI/L5IC0tjUaNGrmG3Y2KiiIhIYHMzJ18910jpkyBAQOgEO+quGDcdNNN1K5dmw8++IBatWpRunRpGjdunO/lfV+UKVPG9Trrlpesf5YjR45k1KhRjB49miuuuILw8HCeffZZt57xvqhRowZ79+51K9u7dy+lS5fOc1yC/v3788QTT7B7924qV67M9u3bGThwoFtnpSzjxo2jdevWNG7c2K18zJgxhIWF8dprr7nKpk2bRp06dVi2bBlt2rShWrVqHDx4sEB1yktQUBCXXnopAM2aNWPDhg28/PLL/P3vf3d9tt988w1169Z1Wy/rdzF+/HjXP7Sssho1apCUlOS2/P79+8nIyKBGjRpu5Vl/g1dccQV79+5lyJAhbh2mDhw4ADg69eWlXLlyrnp4079/f3r37k25cuWoWbOm221TDRs2ZMOGDXmuX5RqOgcGy/6dKF26NA0aNPB4wDxu3Djq1KnDdddd51Y+f/58du3a5boLI+tgJuuOkuHDh5OZmcl9993nloyz1KpVi6ZNm7p1QKxVqxa7d+8GHH8L2b8Le/fuzfU73bNnD9deey1NmzZl6tSpbp9z1t/QwYMHXXX+q7z9rWbNy/r+jh071u1SfXbDhg3jySefzLXd33//HVV1xa6q7Nu3L1dds5qwGjduTEZGBvfddx8DBgygdOmzqfjAgQP5fn/PhSV9H8TGxrJw4UK3sqioKJ54AkaOhHXrYPVqaN7c8/rGsz/++IONGzfy3nvvER8fD8CqVavcbl3z5scff3TdnqSq/PTTTwV66uHSpUu5+eabXQlDVdm8eTOVKlUqUB1iYmJcQwxnmTdvHtHR0W4HHZ6IiOsKxCeffEKdOnVyDbGZlpbGrFmzGD9+fK71jx07luvJXFnvs/6BNW/enN9//539+/cXyeBImZmZrtvTGjduTHBwMDt27HC7dSw7T2e8MTExvPTSS/z666/Url0bcHyGwcHBtGzZ0qd9Z/nll18oU6ZMoYz8WLVqVa8HBl27dmXgwIEkJyfnam/OzMwkPT3d1a5fFFq2bElwcDCbNm2iTZs2rv1u3bqVzp07uy174sQJpk6dSt++fXNdSZo7d67bAfaKFSvo1asXCxcupEGDBgC0aNGCdevW5XmQVL58ebf3UVFR1KhRg3nz5nHVVVe54liyZAmvv/66a7ndu3cTHx9PkyZN+OSTT9ySH+C6Arh+/fpcB70FFRMTw9NPP82JEydcVw7mzZtHZGSk6xbNyMhItm7dyj333ONxG9WrV3fdPZN9u+np6Sxfvtx1sLB8+XKOHj3q9eABHL+vM2fOkJGR4ar31q1bXf2Nioy36/4lYSrMNn1v7eCPPqramF906ZUPqb75ZqHtrygFSpt+RkaGVqtWTf/1r3/pli1bdOHChXrVVVdp6dKl3dro8NCmX7t2bZ05c6Zu3LhR+/btq8HBwa72ek/tklnrZfW27t+/v9aqVUuXLFmiGzZs0IcfflgrVKig7dq1c63jS3vstm3btFy5ctqvXz9dv369fvjhh1qmTBlXG6mq6jvvvKOXXXaZ23qvvfaarlmzRn/55RcdNmyYlilTRr/88stc23/xxRe1QoUKuXrzqqrOnz9fRUSHDh2qmzdv1pUrV2rnzp21Tp06mp6erqqO/gDVq1fPte3du3drSkqKTp8+XQGdNWuWpqSk6B9//OFa5tprr3Xrmf/SSy/pvHnzdOvWrbp+/XodOXKkli5dWt9//33XMs8995xWqVJFJ0yYoFu2bNGUlBR9//339YMPPvD6GZ45c0abNm2q8fHxumrVKp03b55GRkbqo48+6lrm7bff1m+++UY3b96smzdv1vHjx2v58uX16aefdtvW4MGD9dprr/W6L1VHm36HDh109+7dbtO+fftcy3jqvZ/diRMntG3btlqpUiUdPXq0pqSk6LZt2/Tzzz/XmJgY19+Yr236KSkpmpKSom3bttWbb75ZU1JSdN26da75nrbTr18/rVWrls6ePVs3btyojz76qMc2/alTp2pQUFCu9nVPPP3t/PzzzxoaGqp9+vTRVatW6ZYtW/Sbb77RBx54IM9tvfrqq1qhQgX9/PPPde3atdqlSxetWbOm6w6V3377TRs0aKDt2rXTnTt3uv0ust+Rcscdd2i/fv3ctn3kyBHXZxYaGqpDhw7VlJQUtzo+88wzbt+FQ4cOaUREhHbp0kXXrl2rn3/+uZYvX15HjhzpWubDDz/UkJAQfeONN3Tjxo26du1anTx5cq62+Zyuu+46bdq0qS5btkyXLVumTZs21Ztuusk1f8qUKfrZZ5/phg0bdOvWrTpjxgyNjIzULl26uG1n0qRJWr9+/VzbL8w2fb8n5qKciiPpbxw9W7Nu38usXVv19OlC22dRCZSkr+pIXE2aNNHg4GBt0qSJzp49O1fHHE9Jf9q0aa5OMQ0bNtRvv/3WtbwvSf/AgQN6++23a3h4uF500UU6YMAAfeihh9ySflbnrfw6uS1cuFCbN2+uZcuW1Xr16rklQVVHInIcX58VHx+vFStW1JCQEL3mmmvc4s+SmZmp9erV04ceesjrvj/55BNt0aKFhoWFabVq1fSmm25ySxaqjn9+CQkJHmPKOWX/3C+++GK3TmXPPPOMXnrppRoSEqKVK1fWmJiYXB3tMjMz9e2339ZGjRpp2bJltVq1atqhQwedO3eu1zqoOjpU3XjjjRoaGqpVqlTRxx57TE+cOOGa/+abb2rjxo21XLlyWqFCBW3evLmOGTMmV0eohg0b6ieffJLnvtk6DyQAACAASURBVHr06OGx7tlv9csv6as6Ev+rr76qV155pYaEhGilSpX0mmuu0bFjx+rJkydV1ffvkKd4Lr74Ytd8T9s5deqUDhgwQCMiIrR8+fLarl07t9tSs8TFxeXZkTI7T387qo4On507d9by5ctruXLltGnTpvr888/nua3MzEwdPHiw1qhRQ4ODgzUuLk7Xrl2bq06epuz1nD17ttaoUcPtQCArzpxT9u9rjx493D5DVdU1a9Zo27ZtNTg4WGvUqKFDhgxxu11PVfXjjz/W5s2ba3BwsFaqVEljY2Pz/U4dOHBAu3XrpuXLl9fy5ctrt27d9ODBg7m2GR4ermFhYdq4cWMdPny42+2DqqqdOnXSV155Jdf2LekHUNLPPH5Cfy9V3ZX49fPPC22fRSWQkn5B5UzeJn979+7VqlWr5tkDuiT4z3/+o40aNdLT58GBtymYVq1a5epVX9KsXbtWq1evrocOHco1z3rvBxAJCWZj2wfOFrxr4/GbwFK9enUmTpyY7x0R57ujR48yadKkXO3C5vz3wQcfuPqplFRpaWlMmTKFihUrFul+7K+jEFz8ch/OtH6F0mTAggWOnn1/4Xakkirrn3Bh9Mg3f80tt9zi7xCK3D//+U9/h2CKyJVXXul2C25JlHOQr+yy/ncWxgGtnekXgjoxtVla7fazBWPG+C+YAJTVY3vTpk3nfP9pvXr1UNVCGaHLGGPOB6tXrwY83/1SUH5J+iLysIikisgJEVkpIm3zWf4REdkgIsdFZJOIeL6fwo+O9nz07JspU+DwYf8FE2AiIyOJjo7m6NGjbrfrGGOMydv+/ftdD+G59dZbz3l7xX55X0S6AKOBh4Glzp/fiUhjVc3V6CgiDwEjgPuB/wJXAx+KyEFV/ab4Is9bm2fj+GVkU5rqL3D0KEyeDH37+jusgPHUU0/RpUsXBg8ezGeffUZcXBzlypUr1OdEG2NMSZGZmclvv/3Gd999x59//knDhg257bbbzn3D3nr4FdWEI3F/mKNsC/CKl+WXAW/mKBsFLM1vX8XRez+7cS3Hnu3F36CBah5PZvKn4u69n2Xy5MlauXJlr7fp2GSTTTbZlHtq1aqV12cqeEIevfeL9UxfRMoCLYGROWbNBbwNXRQMnMhRdhy4WkTKqKrnR5n5QZ1nunHoH09TicOwZQvMmwc5Rse6kN1zzz3861//4ocffmDz5s2cOJHz11pwW7du5ZJLLimE6PzP6hKYrC6BqaTXRUSoVKkS7du3z3fI6IIQx0FB8RCRSOA3oJ2qLs5W/gLQTVUv87DOy0Bv4CYgGcdBw3+ACCBSVXfnWP4B4AGAiIiIlp9++mmhxJ6enp7vAxbOnBG23JjIQ6feZU/9K9nzWE8ONWtWKPsvTL7U5XxhdQlMVpfAZHUJTIVdl/j4+JWq6rm3s7dLAEUxAZE4LlfE5Sh/AdjkZZ1QYCJwGjiD46BhhHM7EXntr7gv76uqDu61U1uQrD48ddNv/HV5vyhYXQKT1SUwWV0CU2HXhQAanGc/kIHjLD27CGCPpxVU9biq9gLKAfWAusB24Ajwe1EF+lfd/HAdVtGSTz6B0wHT8GCMMcYU8y17qnoKWAl0zDGrI44Oe3mte1pVf1XVDOAu4D+qGnBDNLVoAY0bw++/w5w57vOSkpJITU11K0tNTc31WFFjjDGmKPjjPv03gHtF5D4RaSQio3Fc9h8LICJTRGRK1sIi0lBEuotIAxG5WkQ+BZoCz/oh9nyJQNbjvadOBTIzwfls6cjISBITE12JPzU1lcTERNfjVY0xxpiiVOz36avqDBGpCgwCagK/ADeo6g7nInVzrFIK6A9chqNdfwHQWlW3F0/EBdetGwwfmE6dz8eT0WAMpSqEw6pVREVFkZCQQGJiItHR0SQnJ5OQkEBUVJS/QzbGGHMB8MvY+6r6HvCel3ntc7zfADQvhrAKTZ068Pe2pxi25FlKbXMOO7t8ObRuTVRUFNHR0SxevJi4uDhL+MYYY4qNPXCniNzWqwrTl3TjfsY7Ct59lyRVgoKCSE5OJi4ujuTkZEJCQsjMzCQ2Nta/ARtjjCnx7IE7ReTOO2FC8CNnC2bOJPjAAebOnUubNm2Ij4+nTZs2zJ07l6Ag+zUYY4wpenamX0TKl4dL7mzGko/b0JalcOYM1b/6ik69e7N0zhxOzJlDMtCpc+cS/5xoY4wxgcFOMYtQ9+7wLmefvlf322+JiY4mevFiFosQvXgxMTExdmnfGGNMsbCkX4Q6dIBl1W8njZqOgt272ffBByRHRxO3aBHJ0dG57ts3xhhjiool/SJUujT88+6yfEAfV9nJN94gYeZM4hcsIGHmTLf79o0xxpiiZEm/iHXvDuN4gNPO7hN1UlOJ2r4dgKjt20lISCAtLc2PERpjjLlQWNIvYn/7G1RrWpPPudPj/KioKGvTN8YYUyws6RcxEbjnHkeHvoMhNWDwYH+HZIwx5gJlSb8YdO0Ky4ilTsYODvYb4u9wjDHGXKAs6ReDWrWgQ0fh6OmyvPfefrd59pQ9Y4wxxcWSfjHJevLe+PEnSK1XD4DUevXsKXvGGGOKjSX9YnL77VCuHGzfXpsPOvThx2uuYXPDhvaUPWOMMcXGhuEtJuHhjvH4P5l6mn9N/5i/HV+LAqLq79CMMcZcIOxMvxh17w5nKMO+09UBEODQq6/6NyhjjDEXDJ+Tvoj0EJHZIrJeRLblmLYWZZAlRVRUKhUqHGH0mcddZcHTp7N9wwY/RmWMMeZC4VPSF5HngUlAJLAaWJRjWlxUAZYke/emcdddGXzH9eynKgChx45xeto0P0dmjDHmQuBrm35vYLSqPlGUwZR0sbGxVKgA48bBOzzKUIYC0GDuXBg+3M/RGWOMKel8vbxfFfimKAO5UFxxBbQkmTE8yhlKOQqTk+Gnn/wbmDHGmBLP16S/CPhbUQZyIUhKSiI1NZX+vMEfVOMbbj47c8wY/wVmjDHmguBr0n8c6Cki94hINREJyjkVZZAlRWRkJImJiURfvILa7OIVBp6dOWMG7N/vfWVjjDHmHPmarDcDTXF05tsLnM4xnSqS6EqYqKgoEhIS+Oqft3PTJf9hBVezPqyFY+bJkzBxon8DNMYYU6L52pFvGGCjyBSCqKgoopOTOfCPyoS9ns5rRx/jI3o6Zi5b5t/gjDHGlGg+JX1VHVLEcVwQkpKSCAoKIjkujk6cZNnVa5ixvAtdIr/n+pkPQ0yMv0M0xhhTghV4GF4RCQcqAwdVNb3wQyq5goKCmDt3Lp06dyYmJobDzVZy551luXnvVLbVFuqKvyM0xhhTkhVkRL7OIpIMHAK2A4dE5CcR6VhUwZU0mZmZdOrUiaVLl7JgwQK2bPmBDh0OkJEhjB7t7+iMMcaUdL6OyNcZmAWEAy8CDwMvAeWBby3x+yY2NpaYmBiio6NZvHgx0dHRvPLKRYBjwJ5Dh/wcoDHGmBLN1zP9IcBcoLGqDlXVD5zt/E2AeeAcWs7kKzU1leTkZOLi4khOTqZy5VSuvRbS0+Gjd47A2LHQrh0cO+bvUI0xxpQwvib9vwFjVDUze6Hz/XtAs4LsVEQeFpFUETkhIitFpG0+y3cVkdUickxE9ojINBGpUZB9BoLU1FQSExNJSEggPj6ehIQEEhMT6dZtDwAdXoyDhx6CxYsd9+0bY4wxhcjXpH8SqOBlXnnnfJ+ISBdgNPAy0BxYBnwnInW9LB8LTAUm47iycBvQGJju6z4DRVpaGgkJCURFRQFn79tv0GArjRvD5NNdzy48Zgyo3SVpjDGm8Pia9BcCL4pIVPZCZ6IeAiwowD77Ax+p6oequkFVHwN2Aw95WT4G+FVV31TVVFX9EXgHuKYA+wwIsbGxroSfJSoqirZtY/n3v2EivTghIY4ZK1faePzGGGMKla9J/2mgIrBJRBaLyAwRWQRsASo55+dLRMoCLXH0D8huLtDay2pJQE0RuVkcqgF3Ad/6GPt5oVs3KBNRlU/0rrOFNh6/McaYQiTq4yVkEakJ/BtoC1QBDuB4EM+bqrrbx21EAr8B7VR1cbbyF4BuqnqZl/XuAD4CQnGMLTAPuFVVj3tY9gHgAYCIiIiWn376qU/1y096ejrh4eGFsi1vpk2ry+oJB0jmKgAyy5Rh+WefcbpSpULdT3HUpbhYXQKT1SUwWV0CU2HXJT4+fqWqRnucqarFNgGROIbzjctR/gKwycs6jXEcKAwArgQ6A2uAKfntr2XLllpYFixYUGjb8mb/ftXQUNX/cpWqo0Vf9ZVXCn0/xVGX4mJ1CUxWl8BkdQlMhV0XIFm95MXifjrefiADiMhRHgHs8bLOQOAnVX1dVdeo6hwc4wR0F5HaRRdq8ataFXr1gjE8crZw7FjIyPBfUMYYY0oMr0lfRH4Qkcuzvc5rmu/LzlT1FLASyDmYT0ccvfg9KYfjQCG7rPcl7pG+TzwBn9GF/VR1FOzYAd+WqO4Lxhhj/CSvpJl9JPgg53tvU0GS7xvAvSJyn4g0EpHROC77jwUQkSkiMiXb8t8At4rIQyJS33kL39vAKlXdWYD9nhcuuQRuuCOECfQ+W2gd+owxxhQCrw/cUdX4bK/bF9YOVXWGiFQFBgE1gV+AG1R1h3ORujmW/0hEygOPAqOAw8AP+HjHwPnoySeh6xcPMoDXkSpVkObNITMTgkrchQ1jjDHFyKen7InIPcAsVf3Dw7wqwE2qOiX3mp6p6ns4RvLzNK+9h7J3cNybf0GIiYHI1lF0XjaH259ry8P9Q/wdkjHGmBLA11PHScAlXuZFOeebQvTvf8P3dGTkuyHWj88YY0yh8DXp5/Wk9zDgTCHEYrK59VZH+35qKnz5pb+jMcYYUxJ4vbwvIs2AFtmKbhaRpjkWC8UxOt6WIojtglaqlKMn/6OPwsiRcOedILvToGZNkLyOwYwxxhjP8mrTvxUY7HytwHNelvsDsnc1N4Xl3nvhhRcg9L8LONDubaomfQ1JSdCqlb9DM8YYcx7K6/L+Wzja6+vjuLx/h/N99ikSqK6qXxdxnBeksDB4+GHozlSqLvk/Rw/+9zz2fzTGGGPy5TXpq+phVd2hqttxJPhvne+zT3ucQ/6ZIvLIIzCudLYR+mbMgN9/ByApKYnU1FS35VNTU0lKSirOEI0xxpwnfOrI50zwp4o6GJNbjRrQtEdLfsx6kvCpUzBhAgCRkZEkJia6En9qaiqJiYlERkb6K1xjjDEBzOfRXkTkARFJEZFjIpKRcyrKIC90/fvDezx8tsA5Hn9UVBQJCQkkJiayYMECEhMTSUhIICoqyn/BGmOMCVg+JX3n4DzvACuAEBz35U8D/gS2AsOKKkADjRvDn53/ye9UcxRkG48/KiqK6OhoFi9eTHR0tCV8Y4wxXvl6pv848ArwkPP9e6raA0cnv+M4evCbItTvac/j8aemppKcnExcXBzJycm52viNMcaYLL4m/QbAYiDTOZUFUNWDwHCgX5FEZ1zat4eljfuQmTVO0pw57PrhB9cl/fj4eNelfkv8xhhjPPE16R8Hgpw99ffgOMPPko7j1j1ThESg63NRzOJGV1nQuHFubfhZbfxpaWn+CtMYY0wA8+mBO8Ba4FLge2AJ8KyIpOIYfncIsLFIojNu/vEP6NXvEW7e/x8Aas2ZAxMnwpAhrmWihgyxdn1jjDEe+Zr0x3H27P55HMl/qfP9EeC2Qo7LeFCmDDR7qhMbn7qMvdWa0O7ThyE0FIYOPbtQtgMAY4wxJjufkr6qzsj2+n8i0gSIAcoBy1R1fxHFZ3K4v08Ql7z4M/v3B7OiIkTbMPzGGGN85PN9+tmp6lFV/V5Vv7aEX7wqVIB7+wQDMGqUn4MxxhhzXvGa9EWkbkGm4gz6Qte3L5QuDTNnOm7ZN8YYY3yR15n+diC1AJMpJnXqQJcukJEBg/691W3e8uXLmT59up8iM8YYE8jyatPvheORuiYA/fvfED59LMM/P/vE4+WtWjF37lw6derkx8iMMcYEKq9JX1U/KsY4TAE1bw4XNwqj6oYDAKSHhTGvY0c6depETEyMn6MzxhgTiP5SRz4TGFq88g/XePzhR48Sm5RkCd8YY4xXPt2yJyIT81lEVbV3PsuYQhZWdSUzK3Tn4T/fBCAqNZVZs2ZRqVIlYmNj/RydMcaYQOPr4DzXkrt9vwpQHjjknEwxO3z4EClXX07m90IQSv3UVJKnTIF77vF3aMYYYwKQT5f3VbWeqkblmCoC7XGMxX9nUQZpPDt06BA3PlKHKaXOJvlb5s7l2K+/+jEqY4wxgeqc2vRVdTHwJvBO4YRjCqJbt27cdtv1pNRuRho1AQg5dIh/LF/u58iMMcYEosLoyLcNaF4I2zF/QWpqKjXu3MuwkOfPFk6eDHPm+C8oY4wxAemckr6IlAbuBex6sh+kpqaSmJjIXV98St8T7/AZ/zg784EH4Phx/wVnjDEm4PiU9EXkBw/TUiAN6AqMLMhOReRhEUkVkRMislJE2uax7Ecioh6mowXZZ0mUlpZGQkICUdu305gNHKIi+6nKXong4AtvOp7AZ4wxxjj5eqYfBEiO6QjwBfB3Vf3Q1x2KSBdgNPAyjmaBZcB3eYzf3w+omWPaBnzm6z5LqtjYWKKiolzv72MCL0V/RSNdT/cv70BtPEVjjDHZ+Ppo3faFuM/+wEfZDhQeE5HrgIeAgR72fRg4nPVeRGKB+kD3QoypRAhCefLLWCZfAbNmwbhx0KePv6MyxhgTKIp1RD4RKQu0BObmmDUXaO3jZu4H1qnqssKMraSoXRvGjnW87t8fNm/G8WQeY4wxFzxRH68Bi0gDYBAQA9QCfsNxaf4lVf2fj9uIdK7Xznm7X1b5C0A3Vb0sn/UrAruBgao62ssyDwAPAERERLT89NNPfQktX+np6YSHhxfKtgpb+/h41+uFCxYA8NJLjZg/P4Kedb7i3cxHWf/8INIvc3y8gVyXgrK6BCarS2CyugSmwq5LfHz8SlWN9jhTVfOdcAzCcwz4A5gCvO78+QdwHEcS92U7kThG9ovLUf4CsMmH9R8BTgBVfNlfy5YttbAsWLCg0LZV6ODs5HTwoOrgSm+dLf/b31RPnVLVAK9LAVldApPVJTBZXQJTYdcFSFYvedHXYXhHASlAZ1VNzyoUkfI4Ls2PAjwfVbjbD2QAETnKI3CM7Jef+4HPVfWAL0FfMAYPzlVUqRJ0fucmjnUfSDmOw88/w4gRMGiQHwI0xhgTCHxt028MjMie8AFU9QgwAmjiy0ZU9RSwEuiYY1ZHHE0FXonI1cDfAJ/vFLhgDBlydsom5u5LmNfuJdd7ffFFWL++WEMzxhgTOHxN+r8CZb3MK4ujnd5XbwD3ish9ItJIREbjuOw/FkBEpojIFA/rPQBsUdWFBdjXBa/zrH6sCb0aADl1Cnr3to59xhhzgfI16Y8Ahjo74rmISC1gMI577n2iqjOAx3F0ClwNtAFuUNUdzkXqOqfs+ykP3AWM93U/xiEkrBRlp07kFGUcBT/+SO0vv/RvUMYYY/zC16TfDqgAbBORhSIyQ0QWAluBcKB91hm6iEzOb2Oq+p46ntwXrKotNVtPflVtrznGBVDVI6oarqqv+Voxc9bldzbhp45n2/LrjZ8A27b5MSJjjDH+4GvSbwOcwXG73MXA1c6fu4FMoG2OyRSDpKQkUlNT3cpSU1NJSkrKtWzMV8+wNewKAEqfPIHefz82ZJ8xxlxYfEr6qhpVgKl+UQdtHCIjI0lMTHQl/qwH8ERGRuZatlRoWcp9PIEM569cfvgBJkwo1niNMcb4V7GOyGcKV1RUFAkJCSQmJrJgwQISExMdD+DJNh5/djVvuYqN1/cH4Cjl2L8vszjDNcYY42c+J30RKScij4rITBGZ7/z5sIjYo9z8KCoqiujoaBYvXkx0dLTXhJ+l8cyhzKrxL65gLbd9+4B15DfGmAuIr4/WrQGsAt7GMQhPOefPd4FVIpJzsB1TTFJTU0lOTiYuLo7k5ORcbfw5SVg5zox9mBM165OUBK9Z10hjjLlg+Hqm/xpQGWjrbLePUdUoHB38KuG4pc8Us6w2/ISEBOLj412X+vNL/BUrnmHSJMfrF16AlJRiCNYYY4zf+Zr0r8fxkBu3buHqeNLdIODGwg7M5C8tLc2tDT+rjT8tLS3fdTt3hkcfhTNnYPCdv3D6hReLOlxjjDF+5uvY++GAt0zyq3O+KWaxsbG5yqKiovJt188y4lWl4afD6JM6nDIvnoarm8NNNxV2mMYYYwKEr2f6m4DuXubdDWwsnHBMcSoXJnS9eitlOQ3AiZ4PwuHDfo7KGGNMUfE16Y8E/iUi34tILxG5XkR6isgcoCuOR+2a81DVKW+SHlYdgJD9v3Gi31N+jsgYY0xR8XVwnmnAg0BTHOPfzwImAFcCD6rqx0UWoSlaVasSOv5d19uQyePQHxb4MSBjjDFFxef79FV1HI6n4TXBMdRuE6CWqtqjbs9zpbokkN7pdtf79H/dB0eP+jEiY4wxRSHPpC8i94rIahFJF5FfcVzG36qqSaq6QVVtSLeSQITwj8ZwslwlAMrv28af/Z73c1DGGGMKm9ekLyJdgYk4BuKZhePhOk9QgMfomvNIzZqUfecN19vwCW+RuexHPwZkjDGmsOV1pt8P+BJopKpdVPUqYCjwiIiUKpboTLGSnvdyql1HAIJQDtzRG06e9HNUxhhjCkteSb8h8KGqZh+d/T0gGKhbpFEZ/xCh7EfjOBMSBkDlvRtJnWid+owxpqTIK+lXBA7kKMt6X7lowjF+V68epV97hd8qNyGG5dz6/nV2sm+MMSVEfr33g0TENQGlPJU755mS4pFHqLhlJQcvvZq1a+F569NnjDElQn7JOgk4nW067iz/b47yU0UVoPGDoCDCqwYzdSqUKgUjR8KiRf4OyhhjzLnKa+z9ocUWhQlIrVrBc8/BsGHwZLfdzP/2JBWurOfvsIwxxvxFXpO+qlrSNwx6Tik9ZRKPbe9PWscrqbB7IQRZa44xxpyP7L+3yVOZ7VsY9GsfKnGYy/ctYdUDY/0dkjHGmL/Ikr7JW8OGyNNPn3074Wn2rtjpx4CMMcb8VZb0Tf4GDUIvvxyAcNLZeX0fMjPUz0EZY4wpKEv6Jn8hIcjEiagIAFf9MZv59071c1DGGGMKypK+8U1MDNK3r+tty2mPs3nxHj8GZIwxpqAs6RvfvfQS1KsHQBUOsvPWxzhlIzQYY8x5w+ekLyK1ROQNEUkWkW0i0tRZ/riIXFN0IZqAER4OH37oetvhUCKJXb/wY0DGGGMKwqekLyJNgLVAdyANuBgo65x9MY4n8vlMRB4WkVQROSEiK0WkbT7LlxWRYc51TorIThHpm9c6poh06AC9erneXvv5w/z4fbofAzLGGOMrX8/0RwEbgCjgDkCyzVsGtPJ1hyLSBRgNvAw0d67/nYjk9eS+T4HrgAeAy4B/AGt83acpZKNGQc2a/BlWgwcZS7c+4Rw54u+gjDHG5MfXpN8GeFVV04Gc92rtBWoUYJ/9gY9U9UNV3aCqjwG7gYc8LSwinYC/Azeo6jxV3a6q/1XVhQXYpylMlSrB118TsnU9O5rdxrZt8MQT/g7KGGNMfnxN+pl5zKvG2Qfx5ElEygItgbk5Zs0FWntZ7TZgBdBfRH4VkS0i8raIhPuyT1NEoqMpG1GZadMgOBgmTICvvvJ3UMYYY/IiqvkPsiIi3wN/quodIlIKx5P1olV1lYh8CpRT1Vt82E4k8BvQTlUXZyt/Aeimqpd5WGc20B6YDwwDKgHvAGtUNcHD8g/gaAYgIiKi5aeffppv/XyRnp5OeHjJOM4o7LokJtZmzJhLqVbxKB9MXE2VKqcLbdv5sd9LYLK6BCarS2Aq7LrEx8evVNVojzNVNd8JaIcj0c8F7gUygKeAycBJ4BoftxOJo3kgLkf5C8AmL+vMxXEloWK2sk7O7UTktb+WLVtqYVmwYEGhbcvfCrsuGRmqA5rN1a1E6VOtl2hmZqFuPk/2ewlMVpfAZHUJTIVdFyBZveRFny7vq+oiHJfZo4CJODryvQq0BW5T1f/6eACy33nAEJGjPALwNtLLbuA3VT2crWyD82denf9MMQl6fwyvre5EfVLptaw3E8f41NpjjDGmmPl8n76qzlLVBkBDHB37GqlqfVX9rgDbOAWsBDrmmNURRy9+T5KAyBxt+A2dP3f4um9ThG69FcqXB+AyNnPwiWFs2eLnmIwxxuTi6336Lzjb41HV/6nqMlXd5JxX09km76s3gHtF5D4RaSQio3Fc9h/r3N4UEZmSbfmPgT+ASSLSRERicdzyl6iq+wqwX1NUateG1193vX38zOu8dMcqzpzxY0zGGGNy8fVMfzBQ28u8SOd8n6jqDOBxYBCwGsdVgxtUNeusvS7ZLtur4zbBDkBFHL34PwMWAb0wgeP++6F9ewBKk8ETv/Ti1ReLr0OfMcaY/Pma9CWPeZVxdObzmaq+p6r1VDVYVVtqtp78qtpeVdvnWH6TqnZS1XKqWktVH1FVGw4mkAQFOYboDQ0FoBk/c/LF11ixws9xGWOMcSntbYaItAeuzVbUR0RuyrFYKHAjsK7wQzPnnUsvhRdfhCefBGCQDuP2f9zOzHWNCQvzc2zGGGO8J30ct+kNcr5WoKeHZU4B6wEbB984PP44fPYZ/PQTwZzi+R29eerfSxkztpS/IzPGmAue18v7qjpUVYNUNQjH5f1WWe+zTSGq2kJVlxdfyCaglSrlGJ6vTBkAYviRMh+8w3c+3+NhjDGmqPh6n36Qqv5U1MGYEqJpU3juOQBOlQ3jGOXo1Qv27/dzXMYYc4Hz+T79LCJSXUTq5pyKIjhzHhs4EB5+mFLrf2FT3APs2QMPPAA+jPpsjDGmiPh6n36QiLwsIn/gGCEv1cNkzFlly8KYMZS6pB6TJzvG7vnyS5g82d+BGWPM7SFrAAAAIABJREFUhcvXM/3HgUeAUTja918GXsKR7LcC9xdJdKZEqFcP3n3X8bpvX0i1Q0RjjPELX5N+TxxPuBvhfP+lqg4GGuF4ap5d3jd56t4d+ndcy8AjA7mnu5KR4e+IjDHmwuNr0q+P46k9GcAZHPfno6qngbew0fFMXlSRF4cxcmFLBvIqtZJmZB+11xhjTDHxNekfBkKcr9OA7M+9Lw1UKcygTAkjArt3I6cdw/K+w2OMfn4/KSl+jssYYy4wvib9FKCx8/UcYKiI/EtE/gG8AqwqiuBMCTJihOPBPMBF7GfkmX7cfTcct6fwGmNMsfE16b8FHHO+HgzsAaYDM4AywKOFH5opUSpUgA8+cL3txsdErf8Pzz7rx5iMMeYC4+vgPPNU9QPn6z3A1Tiead8MaKiqa4ouRFNi3HAD3H236+1YHmTiW//f3p2HR1ldDxz/HrawyhIwJErMiBuiuBBliyxVoqK0LkFURLHuWK0/21J30ap1q4rWrW6AiiBxKVSpoLKYgEiwgiCiyASUCLIGAgSQnN8fdyZMJpNkApOZSXI+z/M+k3nXexlm7vve5dxCPv44hmkyxph6JNxx+peLSKL/vTorfIV9KxG5vMZSaOqWp56CDh0AOJQ1PMooRoyATZtimyxjjKkPwq3efw3oXME2j2+7MVVLTNw3aB+4jn9x5JqZ3HhjDNNkjDH1RLiFvlSyrQVuGJ8x4RkyBM47r/TtK3I1UyZuZ8KEGKbJGGPqgQqn1hWRE4GTA1YNFpHjgnZrBlwMfF8DaTN1lQg89xzMnAmFhaSRzwBmMnLkuZx2GnTqFOsEGmNM3VRhoQ/8DtdTH0CBOyvYbyNwVSQTZeqB5GR44gl46inklVdp+EA6hVPgiivg44+hQbWngjLGGFOVyn5an8K11x+Oq96/wPc+cEkBDlbVKTWcTlMXXXklLFyInJLOSy/BwQe7h/8xY2KdMGOMqZsqLPRVtVBVV6lqPq6A/8D3PnBZq2qTpZr9JAKNGwOuwH/5Zbf69tthyZIYpssYY+qocMfpr/LF2UdEEkRkpIj8U0TuEJGUmk2iqS8GD4Y/X1pAp13fM2wY7NoV6xQZY0zdUmGhLyL3i8jSoHUJwHzgGWAkbnrd/4mIp0ZTaeo+VXjlFR794FjeSbiUpYt/5Z57Qu+am5uLN2h+Xq/XS25ubhQSaowxtVdlT/pnAB8GrbsR6AY8BrQGegJ7gLtqJHWm/li1Cm68ESkspNuuPG6VJ3nsMZg9u/yuKSkpZGdnlxb8Xq+X7OxsUlKs0skYYypTWaHfGfgiaN15wM/A7aq6TVW/wN0AnF5D6TP1RVoa3Htv6dsHG97DEfodl18OhYVld/V4PGRlZZUW/NnZ2WRlZeHxWIWTMcZUprJCvzWwzv9GRJrgYu7PDOq8twhIrpnkmXrlz3+GE08EoPGvxUxseQ0/ri7h5pvL7+rxeEhPT2f16tWkp6dbgW+MMWGorNBfA6QFvO8BNAHmBu3XGNge2WSZeqlxY3j1VWjYEICTi+ZwU+MXGT8esrPL7ur1esnLyyM1NZW8vLxybfzGGGPKq6zQ/wy4RUTaiIgANwMlwAdB+50I/FRD6TP1zUknwV//Wvr2sQaj6MRqrrsOCgrcuqlTpzJp0qTSKv2srCwmTZrE1KlTY5RoY4ypHSor9O/DPemvA7YAFwL/UtVVQftdDORU56K+IX9eESkWkYUiclol+/YXEQ2xHFOda5pa5O674Rj38TbZVcQ77a9j0ybl9793nfxLjRtH2tixMG5cTJJpjDG1TWXBeby4p/hHgPHAFao6MnAfEemIq+4Pe5Y9ERkKjAEeAk7yHT9NRFKrOLQrru+Af7F4/3VV06bwyisueA9wyob/cn3LN/joIxeyf/DgwQwdOpTsHTvwrl5N9o4dDB06lMGDB8c44cYYE98qDc6jqqtV9R5VvUlVXw+xfa1v24JqXPNWYKyqvqSqy1T1JtyIgBuqOO4X3/X8y95qXNPUNr17w003lb59ils4iEL+/Gf49ltfR768POb060d6Xp515DPGmDBEdVoT3wiA7sD0oE3Tgd5VHJ4nIj+LyCciMqBGEmjiy4MPuqF8KSkkvPka51/RmuJiuOwy+O47L3np6fSdPZu89HTryGeMMWGQaIbO94XsXQP0U9U5AevvAYap6tEhjjkaGAAswI0eGA5c7zvHZyH2vxa4FiApKan7xIkTI5L2oqIiWrZsGZFzxVptykuLH35gV1ISv7ZsyfbtDbnqqlNYt64p/fvn8Gr+cDz5+XjT0njrmmvo0qULbdu2jXWS91tt+lyqYnmJT5aX+BTpvAwYMGChqqaH3KiqUVtws/Ip0Ddo/T3A8mqc50NgSlX7de/eXSNl5syZETtXrNXmvMyZoypSog0alGguvVRd3z5duXKl5uTkxDp5B6Q2fy7BLC/xyfISnyKdFyBPKygXoz1r+QZgL5AUtD4JWFuN88wHjoxUokztcdppcPutuykpEYbzOkW0AFwbf58+fWKcOmOMiW9RLfRVdTewEBgYtGkg5YP+VOZEXOc/U9989BF3v5HGVZ3+w0o6cwHvspF2NuGOMcaEIdpP+gBPACNE5GoR6SIiY3DV/i8AiMh4ERnv31lEbhGR80TkSBHpKiJ/x80B8M8YpN3E0osvwlln0XTdWh4tHMGhspoZZHJ8w8U88USuTbhjjDFVaFTRBhH5tBrnUVUNa9IdVZ0kIom4mfmSgSXAIN0X9Cd4vH4T3KQ+hwI7gaXAOaoaPAOgqevOPx/uvBM2bqTd1o283XUoV/34Csu2HsuLLw7j6KOFG28sHd5vjDEmSGVP+g0ACViOAfrjovQ18732B472bQ+bqj6nqmmqmqCq3TWgJ7+q9lfV/gHvH1XVI1W1maq2U9XTrMCvpw4+GMaMKX3ba+nn/N95T3DBoe+wZ49w001w6aWwbVsM02iMMXGssoh8/VV1gKoOwEXQ2wP0UtXDVbWXqh4O9PKtH1PReYyJqEsvhUGDSt9e9vabZA6azjPP/ELLljBxIpx6KixdGsM0GmNMnAq3Tf9vwN2qOj9wpe/9aOCBCKfLmNBEWH3HHexKSACgWXExV7/8Mu2+Gc377/9E164uYt+pp8Ibb8Q4rcYYE2fCLfSPBNZXsO0X4IjIJMeYqv0IbHnyydL3DUtKuOTll0n99Hnmz4fhw2HHDvd6/fVQXBy7tBpjTDwJt9D3AtdVsO06ID8iqTEmDH369CHphrJTNciePRz50EO0eOpBxo1zHf2bNHGvGRlgUXqNMSb8Qv8+YLCILBGR0SJyg+91CXAOrorfmNhKSIDTT0cErr0W5s4FjwcWLoSTT4apU2OdQGOMia2wCn1VnQicCRQCtwPP+l63AGeq6qQaS6Ex4TjvPHj5ZejZs3RV9+6uwB88GLZsgd/+Fm6/HX79NYbpNMaYGAo7OI+qfqyqfXDD9ToCzVQ1Q1U/qbHUGROud9910+8FadtkO++/D488Ag0bwsMPwxlnwNrqBH02xpg6otoR+VS1RFV/UdWSmkiQMfslVESeFSugc2cavPISo0bBJ59Ax44wezacdJJ7NcaY+qTCiHzBRORw4CJcxLymQZtVVa+KZMKMqUhurgu56wlY5/V6KSgo2DfpTmGhq89ft8418C9dSr/HH+d//2vEJZfArFnwm9/AQw/BX/4CDWIRkNoYY6IsrJ86ETkP+BY3Hv883Pz2wYsxUZGSkkJ2djbetDQAvGlpZGdnl429X1TkOvb5jRkD555Lx2aFzJgBt90GJSXu9fzzYfPm6ObBGGNioTrBeWYByaqaoqqeoOXwmkuiMWV5PB6ysrLIHjKEmQMGkD1kCFlZWXg8Ac/+hxwCOTmuRPf76CPo2ZNG+Sv4+99hyhRo08a9du8OX34Z/bwYY0w0hVvoHw48rqoVBegxJqo8Hg/peXnM6deP9Ly8sgW+X4sWkJ0Nd921b50/XN+nnzJ4sCvoTz7ZjePv3Rv+9S9QjV4+jDEmmsIt9L8FEmsyIcZUh9frJS89nb6zZ5OXno63oug7DRrA3/4GEybsq+7fvBnOPBNeeAGPB3JzXeS+Xbvguuvgiitg+/bo5cUYY6Il3EJ/FHCHrzOfMTHl9XrJzs4ma/JkBsycSdbkya6Nv7Kwe5dcAnPmuO774Abr33AD3HQTTROU55+H11+H5s3da48esHx5dPJjjDHREm7v/dG4J/1lIvI9sClou6pqv0gmzJiKFBQUuDb87dvJz8/Hk5ZGVlYWBQUFoav5/U49FRYsgN/9bl8DfpMmpcP9LrvMDeW78EI3S196OrzyClx0URQyZYwxURBuob8XsOceExdKh+WNHk3+rFmk9e+PByov8P0OPRQ++wxGjHA9/B99tMzmrl3dfcE118CkSTB0qKv+f+wxd39gjDG1WViFvqr2r+F0GBM9zZvDxIlu+r2GDctuU6VVK+Gtt9xEPbfeCk8/DfPnw9tvQ2pqbJJsjDGRYCFJTK2Tm5tbrv3e6/WSm5sb/kkaNHCFf6C9e13d/j//iaD84Q+uUqBTJ1fon3yyG/VnjDG1VbjBefpWtdR0Qo3xKw3O4yv4/R37ygTn2R933gnvvQc33QQjR8KePfToAf/7H5x1FmzcCGefDffe6+4PjDGmtgm3TX8WUNXo5YZVbDcmIkqD82Rn0759exYsWFA+OE917doFM2fue//CC677/uTJJCYm8sEH8OCDrsC//343be+ECdChw4HnxxhjoiXc6v0BwG+CliHAOCAfOLcmEmdMRTweD+np6axevZr09PQDK/DBjeGfNcsN7fObOdON3Vu2jAYN4O67Yfp0V9B//LHr6T937oFd1hhjoimsQl9VZ4dY3lXV3wNTgME1m0xjyvJ6veTl5ZGamkpeXl7lY/TD1awZvPkmPPDAvnU//AA9e8J//wu4aXn/9z/o0wfWrIF+/eDJJy2KnzGmdohER74PcLPvGRMVpcF5fFX6/qr+iBT8Iq5t/5139nX027oVzjkHnnoKVDnkEFcJcOutLsbPrbfCkCFuN2OMiWeRKPSPBkoicB5jwlIanMdXpe8v+AsKCiJ3kQsucAP0O3Vy70tK4P/+z03Tu3s3jRvDP/7h7g0OOsi9pqfD4sWRS0JtE5FRFcaYGhVu7/3LQyxXi8hTwMPAtJpNpjH79OnTp1wbvsfj2Re0J1JOPBG++MJV7/vNnl0mMP8FF0BeHnTrBt9/77oAjB0b2WTUFjU2qsIYEzHh9t4fW8H6XcAk4I8RSY0x8aZjR1eXf8018J//wNSp0LZtmV2OPBI+/xxuvBFeew2uvNLN6vvMM66bQGVyc3NJSUkpcxPj9XopKCiI/E1MDauRURXGmIgKt3rfE2JJVtVmqjpCVQtrKoHGxFzTpjB+PCxcCEcfHXKXZs3g1VddrP6mTd1rr16wYkXlp65rT8cRH1VhjImocHvvrwqxrKvpxBkTN0Tg8BCTTE6cCI8/Xtp9//e/h3nzoHNnWLQIund38X4qEtwRMbCDYm1UI6MqjDERU62OfCJyrog8JiKv+F7P2Z+LishIEfGKSLGILBSR08I8LkNEfhWRJftzXWMi6osvXF3+X/7iSvtduwDXFWDhQjj/fNej/4IL3C579oQ+TV15Oq7RURXGmIgItyNfKxGZjRuT/0dgkO91iojMEpGW4V5QRIYCY4CHgJOAucA0Eal0KhMRaQuMBz4J91rG1KjHHnOT9oDrvXf66fDLLwC0bu169P/jH25On8cfh9/8xo3tD1ZXno6jMqrCGHNAwn3Sfwg4GRgONFPVZKAZcLlv/UPVuOatwFhVfUlVl6nqTcDPwA1VHPcKLgLgvGpcy5ia88Ybbopev9xcOPXU0nF7Im4M/6xZkJLiOvedfDJ8EnDbWpeejqM2qsIYs9/CLfQvBO5S1TdVdS+Aqu5V1TeBu33bqyQiTYDuwPSgTdOB3pUcNxJIAh6oaB9joi4hwfXee/xxV8IDrFoFvXvDv/9dultGhovi568IyMx0cfxLSuzp2BgTXaJhxA8VkV3Auao6I8S2gcBUVW0axnlSgDVAP1WdE7D+HmCYqpbrGi0ixwMfAz1V1Ssio4EsVT2ugmtcC1wLkJSU1H3ixIlV5i8cRUVFtGwZditGXLO8RF67efM49oEHaLRjBwAqgvfqq1l9ySWlNwR798K4cWm8/noaAD16bOT225fRuvWvQPzk5UCljR3L7t27adKkCfmBNSG1VF35XMDyEq8inZcBAwYsVNX0kBtVtcoF+BYYU8G2J4FlYZ4nBTdbX9+g9fcAy0PsnwB8AwwPWDcaWBLO9bp3766RMnPmzIidK9YsLzVkyRJVj0fV9eV3y/Dhqjt3ltntww9V27Vzm5OSdup77/2keu+96r3iCtV779WVK1dqTk5ObPIQCYH5rwPi6v/YAbK8xKdI5wXI0wrKxXCr918EbvL12v+NiHQRkQEi8iJwM/BCmOfZAOzFVdUHSgLWhtg/GegCvObrtf+r7wahq+99ZpjXNabmde3qevT37btv3RtvwPz5ZXY7+2z48ks45RRYt64pWVkduf+ZvRw2bhzeceNq9Th9Y0x8C3ec/pO4cLvDgBnAElwv+iuAh1V1TJjn2Q0sBAYGbRqI68UfbA1wPHBiwPICsML3t01sauJL+/YwYwZcdZV7/9hjbiq+IIcdBp99Bn/4A+zd25B7N/2N4w/6mnu63c9559XOcfoWe9+Y+BfukL3WwP24J+9zcb32z8FF5buzmtd8Ahjhi93fRUTG4Kr9X/Bda7yIjAdQ1T2quiRwAX4BdvneF1Xz2sbUvCZN4KWX4KOPXPf9CiQkuFC9b70FLRttY+nW43hjynDOOMPDY4/B5s1RTHMElEYXTEsDwJuWZrUWxsSZKgt9EWkEbAQGqupmVZ2mrhf/NFWt9s+Sqk4CbgHuAr4CMoBBqrrKt0uqbzGm9hJx3fT9vfr91q4tnaLXr0cPL3+98RFuPnIM7duuZ/VqGDUKDj0URo6Eb7+Nctr3U+mQwyFDmDlgANlDhtTq6ILG1EVVFvqq+iuwDtcWHxGq+pyqpqlqgqp214Ce/KraX1X7V3LsaK2g574xca24GM47z03RO2wY7NxZOk5/2L/fZMz3t/D5QT248spsMjJ2smMHPP88dOni+gF89FGZe4W45PF4SM/LY06/fqTn5VmBb0ycCbcj3xvA1TWZEGPqvDFj9nXqe+st6NePDYsXu6fh/HwAOq/ycvfd3Xn44S9ZsgSuvdZN5vPf/8JZZ8Gxx8ILL5SZ3TeueL1e8tLT6Tt7Nnnp6bUyyJAxdVm4hX4+cIqILBCRu0TkKhH5feBSg2k0pm649Va4/vp97xcs4JQbb8SzaVOZ3fxR7Lp2hRdfhB9/hL//HQ45xFX133CDq/r/619h9eoo56ESpdEFJ09mwMyZZE2eXGujCxpTV4Vb6D8LHIKLpnc/8BLwcsDyUo2kzpi6pHFjeO4513uvYUO3bs0aOK3y+aYSE+G228DrdZP69ewJW7bAo4+6if8uushFAI511X9pdEFfrYUnP9+iCxoTZ8It9D1VLCHmHDXGlCPixulNm+Zm5QHYubPsPh98UP64NWtovGkdQy/Yw7x58Pnn4A/2N3myC/V76qkuLMDu3TWfjVAs9r4x8a9RODsF9Kw3xkTCwIGufX/wYPj++7Lbli6Fc4JmrR4xAj7+2P3dujU9EhOZkJjIq6e1Z9n6ROavSOSnvPbkDk/kn/+XyTk3d+a66+Dgg6OSG2NMLRFWoR9IRIJrB/xh/4wx1XH00Xw+Zgzd/v53mn/2Wenq9ap8l5tb9gl548Z9fxcWumXlSpri5qc+KeC0WRsmc889nXnwQbj0UvjjH+GE4d1cm0Biolvat6/472OPhebNazr3xpgYqLDQF5GOuOlsJ6nqeN+6hkBw5WGRiBylqutqLpnG1E1JxxzDs+eey6UrVnDIzz+zPjGR9zdu5IzggDZt2rgCedMmNz1fJe54PJHds+E//4HXXnPLtkaraflroesVWJWFC90cwH4lJXDGGdCuXdU3DW3a7Me/gjEmWip70h8JnAxkBa0XXMe9At/fQ4HrgftqIoHG1GUej4esIUOYsGED6Xl55KWnk3XDDeXHt3/6qXstKXFP7Bs3umXDhnJ/n3yhhyl/cq0GzzwD41/9lZbbC8NPVGJi2feFhTBzZnjHNgi3m5CJltzcXFJSUsr8n/J6vRQUFFh/i3qoskL/LOAlVQ3qZYQCL6rqlwAish4XltcKfWP2Q2BAm76zZ1ce0KZBA/fE3a4dHHlkpec98kh4+mn42/0N+efzW3n7uQ3s+GkjiWykU9MNnH3qRgZ020i7kqAbh+BCP7BpYX/s2eMiC2VmuhDFJqr84ZGzstzzW+nQyqzg5zlTH1RW6B+Nm9EuWFBcUb7z7WuM2Q/BAW3SvN6IRrJr3Ub4w+2tuGFUK6ZO9TBmDLwyC16ZA/IZnHsu3HILDBhQPmowAMnJrhNhiFqFwL93r12LJCTQeMuW0kPnzZtH8XvvMeCxx6BtW7jwQjfsoF+/fcMWTY0qDY+cnU379u1ZsGCBhUeuxyor9JsCZSa0UdW9IpKMmyLXr9i3rzGmmgID2njy80nzesnu0KFGfpQbNnRRgM87D776ytUCvPkmTJ3qluOPd53+Lr3URQEs1aIFnH56ledfOG8eM6ZNY+CMGfT6/HPm9ezJ9OnTGfn1126HzZvh5ZfdkpzsAgxccokbaxjybsNEisfjIT09nTlz5tC3b18r8OuxyhrgfiHE+HtVXaeqgXH4PcD6SCfMmPpg7ty5ZGRklAlok5GRwdy5NTtr9Iknwquvun59998PHTvC11/D1VdDp05w111Q3Zg6vXr1YuDZZzP9zDN57cormX7mmWRmZtKhTx83l3Cgn392YYl79oTOneGOO1wC4kRdmybY6/WSl5dHamoqeXl5FiWxHqus0M8BhodxjsuB2vlNMCbGevfuTU5OTpnpaHNycujdu3dUrn/wwXD33ZCfD+PHQ/fursb+wQddOT1sGHzxRfjn69WrF6mrV7P6sMNIXb2aXr16uTsIr9eFDfzDH8oHD/B6XZzhbt1g3LiI5m9/lU4T7Csc/TUytXGa4MA2/MCqfiv466fKCv2ngd+IyOO+6XXLEJFGIvIE0B8YU0PpM6ZOKygoICMjo8x0tBkZGVEPXZuQAMOHw4IFkJMDWVluoMCECdCjB/TuDZMmuT55lZk3bx6rU1NJXbWK1ampzJs3z20QcSd55hkXenj6dLjyyn1RCcF1UjzrrLInVIV10R8NHFw4BhaatU1peGRf2v15s/DI9VOFbfqqOk9ERgGPApeJyAzAP71HKjAQaA/crqrzajylxtRB/ifKI1asYE6/fnRbtIicnJyY9awWgT593LJqFTz7LLz0Esyb55ZDD3UP69dc4wYQBJo3bx7Tp08n86OP9rXp+9rqe/XqtW/HRo1cRMKBA93cwdOmuVkHi4shKansSRctcjED+vd37f8XXlj+wjWkrrSDhxqW5/F4am1+zIGpdFCtqv4DOAP4CrgQuN23XOhbl6mqj9V0Io2pqzweDxkZGSzu1o2Dd+5kcbduro0/Dn6QDzvMTerz009unqCjj3Z/33abK/yvvx6++Wbf/itXriQzM5Nen38OQK/PPyczM5OVK1dWfJGEBNezcNIkeP/98tsnTHBP+zNnunmGO3Z0oYsnTICiovL7R5DX6yVv9my6bd5M3uzZVh1u6oQqI2mo6kxVPQtoBXT0La1U9SxV/bSmE2hMXeb1esnJyaHbCSfwS7NmdDvhBNfGH0cFTIsWbjrfb75xD+VnnunmCHrxReja1b3/8EO45JJhZZ/ocU/4w4YNC+9CoXrwr11bdv2ePS7U4LBhrm/AxRfDv/8Nu3YdQA7LK63SHzeO88eMIWvcOGsHN3VC2OGzVHWvqv7iW/ZWfYQxpir+Nv0VK1aQmprKihUrYtKmHw5/k/t//+tuAK6/3oXonz7dzQ/UpYtrDiiiReQuOn68q1548kk3tC/Qzp2uhuC881yzwDvvROyysRpVYUxNs5iZxsRQSkpKaRu+v4NVTk5O3PcS79LFNcf/+CM88ogb5vfdd669/1B+4lLe5EWuZflyVzt/QFJSXPSg+fNhxQp44AFXxRCosBCOOqr8sft58ViPqjCmplihb0wM1fae1e3awahRsHIlvP2266BfSBve4lKu50WOOcaV2Rdf7G4Sli07wJuAzp3hzjthyRJYvBhuvx3S0txNwPHHl913zRq3/223uQ6B1bhwvIyqiKjRo0kbOxZGj451SkwMWaFvTAz16dOnXKc9j8dT6yZCadQIhgxxQ/G/oQvPMpIhvM3BB7tm+UmTYORIN2tvx45u32efdWV3FZMGVuz44+Ghh9wdx8cfl98+aZKLAfDIIy4aUdeu8Le/udqCKvhrYPyjKo5YsaJW1MBU6r77SBs3Du6zaVLqMyv0jTER1YVvGcnzvM1Q1q517f/PPw9Dh7oC/5dfIDvbNQUcf7xrjr/wQjeEf/Hi/bgJEHEnDvbBB2XfL1sG99zjZiI65RR44glXGxBC4KiK1FWr4mpUhTEHwgp9Y0yNEXHt/9dfDxMnutC+y5e7nv+XXuqq/jdsgHffhZtvhhNOgA4d4Pzz4amn3BwB+10T8OGHrmf/xRe7HoeB8vLgT39ynRH694dZs8psLh1VsWgRqw87rDR+gvXeN7VdZRPuGGNMRIm4/nZHHeWG3avCDz/A7Nmu3J01y3XWf//9fcP227SBvn3dxHz9+7sbg7Am6EtIgN/+1i3bt8OUKe7OY9q0faEFVd3Fg0IN+tv0c9avL5390N+mb0/7pjazQt8YE1n33kt+fj5pvp7vlRGBI45wy1VXuTLY6913EzB7tosMOGWKW8BF7j3tNHcT0K8fnHSS61NQqRYtXES/Sy4ADPwqAAAd10lEQVRxs/29+66LAjhzJrRv7+YVDnBI69bsGD6cy9esIWn9ejw//MDk9u3JGjJkv/5JYik3N5eUlBQCb1W8Xi8FBQW1ru+IOXBW6BtjImv0aPJnzSKtf/9qHyoChx/uliuvdOvy813h778R8HpdfJ7//Mdtb9UKMjJcLUC/fi5qb+PGlVykbVt3h3HVVa6X4bfflrtr2DV5Msd+9VXp+7TVq/nzXXex59FHXf+B9u0hMdEtffvCZZeVvcamTe61TRsX4CCG/KGes9LS8OTn401LK51LwNQ/VugbY+JaWppbrrjCvV+9uuxNwA8/uBr7adPc9pYt3dwB/puA9PRKbgI6dgzZCbDDJ5+UWyd79tBk40Y3DWEg1fKF/j33uOEJDRq4cY3+G4TAmwX/3z16lB9uGEGlkwetX096Xh556em1dvIgc+BiUuiLyEjgL0AysBS4RVU/q2DffsDfgaOB5sAq4GVVfTxKyTXGxJHUVDcj4HDfxN9r1pRtDvjuO/joI7eA68PXp8++PgGnnAJNmlR+jR13383yRx/l5JwcEnbvpkSEBhWN809MLL9uwwb3WlLi/va/D+XBB8sX+kOHus6GFd0oBP59+OGuuqMSHo+H9Lw85vTrR9/Zs63Ar8eiXuiLyFDcVLwjgRzf6zQROVZVV4c4pAg3ze/XwA6gD/CiiOxQ1eeilGxjTJw65BA3EuDSS937ggKYM2ffTcC338KMGW4BaNbMBRHy3wSceqrr8xfo0DPPZM9RR/H088+XPh0PufJK0lq1ck/6Gza4140bXQyAYE2auM4HhYVVZyDUTYPX6+IPVDZZkd+ECa6vQqCrroIdOyAxkdU7drC3TRuKWrTgzGnT+KxvX5rOm0dJSYm16ddDsXjSvxUYq6ov+d7fJCJnATfgZvArQ1UXAgsDVnlF5ALgNMAKfWNMGf4IgBdf7N6vXetuAvy1Ad98A5984haApk2hZ899zQE9e7p1wU/HaY8+6g449NCqEzF+vHvds8e17wfeKAS/Hndc+eODmxAqE+qmYcqU0tqFVN8q/7P9CYsWsXjOHNoMHeo6QDRrFv61TK0X1UJfRJoA3YHgqvnpQFhBrUXkJN++oyOaOGPMASntJR5QdRwPvcQ7doSLLnILuOBAgTcBS5bsGy4I7qm/Rw844YTNFHe4mLNnTGNBr3TSvN7qV4s3buyiDyUlVe+4BQvK3xxU9HdyctljS0r2dSQMoVlxMT3mz3dzGdx5p/vHSE+vXvrMAYvV90X0gGfDqMbFRFKANUA/VZ0TsP4eYJiqHl3JsT8BHXA3Kvep6v0V7HctcC1AUlJS94kTJ0Yk7UVFRbRs2TIi54o1y0t8qu152bx5M8uWLaNLly40btyYPXv2lL5v27ZtrJNXocLCxixa1JpFi9qwaFEbfvih/GfQUrZyUPJ20tL24vHs5ZBDdpYu7drtDjkrcMzs3Uu7hQtpXFhIo8JCGm/dSuPCQprm5NBq2zaaBMQk2JuQQO6//01JQPtGg507ab10KVu6dUOr6vwQQ/Z9qdiAAQMWqmrIO7naVOh7gJZAT+AR4I+q+npl10tPT9e8vLyIpH3WrFn0348hSPHI8hKf6kJe/PPQt2/fng0bNtTKXuKbNsG//rWM775LYclry/mOoyikTYX7N2++L9aAfznySPeakhLzEXuA73N5/nlOnT+fdUlJnNmyJa3nznUTEgWHK54yBX73O5ex00+Hs892SxhxF2pa4NOx//sSD7VJ+6umvi8iUmGhH+02/Q3AXiC4risJWFvZgarqj3/5tYgk4ar3Ky30jTHR5fF4SE9PZ86cOfTt27fWFfjgRtjddlsX9+a1HiiwkURWzNvAihWUWzZudHMGLF5c/lxNm7pyNfim4IgjXATgsCILHiB/wZI1eXLpOP1/jRxJ1t134wnV7OAf+7hjB0yd6hZw8ZQHDXI3AKedVvUQiBpQGnPAF2OgNG+1NOZALL4vUS30VXW3iCwEBgKTAzYNBN6pxqkaAAlV7mWMiSqv10teXh6pqank5eWRlpZWKwv+QAK0ZyPte7pOfsE2b3axAvw3Ad9/v+/vX36BpUvdEqxxYzfaLrh24Igj4LDDwogyGKbS6ZtHjQLAk59fOn1zyM+mUyeXmO+/L7t+2TK3/OMfLhjC6afDjTfCwIGRSWgYSmMO+J6OFyxYUCtrk/xi8X2JRe/9J4DXReQLIBe4HkgBXgAQkfEAqnq57/1NgBdY7ju+L/BnrOe+MXEl8Klr1apV9O/fv/R9bf1RDkfbtq4fXKi+cFu3lr0hCFz8kw8tX17+uEaNXG16qBoCj6d6D9mhqr09Hk/Fn8kdd7hlxQr31P/hh66XY3Hxvn2KitxkRoMHlz9elZrs5FAXapMgdt+XqBf6qjpJRBKBu3DBeZYAg1R1lW+X1KBDGuLa8NOAX4EfgNvw3SQYY+JD6ROlx8OqVatKn8rq8yQ1Bx3k5gY46aTy27Zvd8PwQ9UQ/Pjjvr+DNWjgAhSF6kNw+OGuSSHQfsfeP+IIuOkmt+zY4Qp+/02AP37A2WeXPaakBI45xg1D9DcFHHJIOP9UYasrtUmx+r7EJCKfL6hOyCd1Ve0f9P4p4KkoJMsYcwCq/URZz7Vo4QLxhYrAu3Oni88TqoZg1So3H0F+Pnz8cdnjRFwYgcAbglatjuCdd6ZxVWoXuq5etn+x95s3d4X4oEHw9NPuDuXzz11PxUD/+5/b9v338N57bl23bq7wHzQIevWqYmKEytWl2qRYfV8s9r4xxlSkGjMGRlKzZnDssW4Jtnu3K/ADbwT8tQRer6sl+PFHN4GgkwSM4ElG0LzhdppuLqbTmy2YNaspHTq4aL4VvbZpE6KmPnB+5GBz5pRf5+/l+MgjruojM9PdBJx1VvmbhipYbdKBs0LfGGMqcgAzBtaUJk0qLnP37HETEoWqIfhh+W527G3BjsIWbFoEixZVfa1GjVzAv6puDjp0cEviDbeQcNZZ+2ZAmj3bJcpv61bIznZLjx6utqAaSp+OR48mLT8fZs3CM3p07S7wA/LC6NE1fjkr9I0xpo5o3NgNEezcGc48063Lzc2lQYMGfPbue3T5fBk5x2TQ+ZRBbN7ckA4djmXDBli/npCvW7fCunVuCY9w0EFdaN++Cx063MqhvynitD2fcurGDzl21TRab9k3vcrGHoNouMVNUVBam/D2267/wFlnhZz9sLR/wn33keZb573iilo7Th+AgLxYoW+MMeaANGjQgOnTp5OZ8xm9Pv+c9r9uYPqhxWRmZtKrV+XH7trl4hBUdFOwfn3oG4WtW11fv/m05B1+C/wWUI7lG85mGmczjVFPn8OXT7vaBH9tQbb3EY4q+hKAguSTWdNtEIW9z0Z69qBDx4Y0bNiJiRMnc3FaWmnMgdo8Tj8WrNA3xpg6rKSkhMzMTHJUWdujByvatCEzM5OSkpIqj01IcM3u4Ta9q8KWLRXdIAgbNnRl2fqufLbhz2xeD602wLZtblIkXbuWo/iy9FwpP39Jys9fwkcPsJF2TCeTDxnERwzn/iYjaZlQhGxVEl9tzoQJe0lJceEDWrRwr9X9O44jDkeUFfrGGFOH+au9i4uLS8e296rqEX8/ibi4BW3bumGE4SgudrUJm75vzPdvPsJBuR/SfnkuDUt+Ld0nkU1cwkQuYSIlCB/vPoMzmQ67YL1vbqGVXxWSyEZ+Jpliqj9zYOPG+3/DUNHfLVrE382EFfrGGBMkXmcM3F/xPLa9aVM3lP+QQxKh/yhgFBQWuvGI/rgAP/9cun8DlOM7fM3j/f+Et4OHzkecSKNGrcnIX8RJTwwHYFfT1mxrlUxh8xQ2NU1mQ+Nk1jdMZq0ks6YkmR9KPKzYcxhFRS5ewrZtrr/hli1uiaTGjUPfFOzZs4l27ZpwMC8zkBlczKSo/B+zQt8YY4LUpRjvtXJse+vWcOGFblF1Q/4+/JCtkybRcvFitJFQ1PUgeqyYz8qtG8nMzOSk4n03BgnFhSQUF9Keb+kc6vznnrtvTgHcJX7Nfp+9n8xiZ9tkdhyUTFGrZLY0T2Fz02S2SFuKtkvpTUJREWH/vWePC9W8eXNwItr5Xq+iNYX0SJsflf9jVugbY0yQuhTjvdaPbReBE06AE07g6759KfrpJ1bOmkXqqlWs7NyZ9PR01z+hcWM3b8DatWWHCYaSnFzuEo1nf0zjF5+lKVBuYtuEBDeaIDnZLRdfDBddVHafoiIXxChgWkVVF1ch1M3AggXLKC5uyNdzF+PZ4CX79CFkZGTUzYh8xhgT7+pKjPe6FCkxJSWF7LlzOWLdOhafcALdFi3imxYt3NPxLbe4paTEzY9cUOCaBYKXggIXJjhYQBNCObt2uVCIq3zR4rt3L7/PJZe45oikpNKbA0lOJiElhYTkZBL9NwwpydCxIx5PU7KzsxnWfC6Lf3si3b76ipycHHvSN8aYWIjndvD6qqCggIyMDHLWr6fv7NnkpaeXfzpu0MCNAWzf3oUADtd117kwwaFuFrZtK7tvUE0B4Pbbu9cdX1BQ+bVefx3PZZeRkZHB9O3bSV21isUnnEBmRoaF4TXGmGirle3g9UBpX4vJk/Hk55Pm9ZLdoUNkno4zM90SSlFR2ZuAU08tv0/gLIRVSU7G6/WSk5NDt8WLS2stclq0oGPHjla9b4wx0VTr28HrqNLPZdQoADz5+dH5XFq2dGMQKxuHuGSJK/jXrg3drBC4pKSEV2tRA6zQN8aYIHWpHbxOitFESFVq2hTS0txShU1Tp7J06VKGBtRaTGrXjq5du9ZoEq3QN8YYUyuUVu9fcQWrVq1CDzus1g6ljBUr9I0xxtQKdWko5eDBgznuuOPI3rSJ9Lw88tLTGTp0aI3npUHVuxhjjDHxwT+UcvXq1aSnp9fKAt/P4/GQnpfHnH79SM/Li0perNA3xhhTawQPpfR6vbFO0n7zer3kpaeXduSLRl6s0DfGGFMrBA6lDKzqr40Ff2leJk9mwMyZZE2eHJW8WKFvjDGmVggcSgmUGUpZ25TmJT8fKDv8sCZZRz5jjDG1Ql0aShmrvNiTvjHGGFNP2JO+McYYEytRDjRkhb4xxhgTK6NHkz9rFmn9+0flcla9b4wxxtQTVugbY4wxUZabm1tueJ7X6yU3N7dGr2uFvjHGGBNl/nkE/AW/f9x+SkpKjV7X2vSNMcaYKIvVPAIxedIXkZEi4hWRYhFZKCKnVbLvBSIyXUTWi8g2EZkvIr+NZnqNMcaYSIvFPAJRL/RFZCgwBngIOAmYC0wTkdQKDukHfAqc49v/Q+C9ym4UjDHGmHgXi3kEYvGkfyswVlVfUtVlqnoT8DNwQ6idVfWPqvqwqn6hqitU9T5gIXBeFNNsjDHGREys5hGIaqEvIk2A7sD0oE3Tgd7VOFUrYHOk0mWMMcZEU6zmERBVrdELlLmYSAqwBuinqnMC1t8DDFPVo8M4x43Aw8BxqroqxPZrgWsBkpKSuk+cODEiaS8qKqJly5YROVesWV7ik+UlPlle4pPlpWIDBgxYqKrpITeqatQWIAVQoG/Q+nuA5WEcfyGwAxgczvW6d++ukTJz5syInSvWLC/xyfISnywv8cnyUjEgTysoF6Pdpr8B2AskBa1PAtZWdqCIZAGvA5er6tSaSZ4xxhhTd0W10FfV3bhOeAODNg3E9eIPSUQuwhX4I1Q1u+ZSaIwxxtRdsQjO8wTwuoh8AeQC1+Oq/V8AEJHxAKp6ue/9xbgC/8/AHBHp6DvPblXdFOW0G2OMMbVW1At9VZ0kIonAXUAysAQYpPs65QWP178el86nfIvfbKB/zabWGGOMqTtiEoZXVZ8DnqtgW//K3htjjDFm/9iEO8YYY0w9YYW+McYYU09YoW+MMcbUE1boG2OMMfVEVMPwRpuIrAfKherdT+1xwYXqAstLfLK8xCfLS3yyvFTsMFXtEGpDnS70I0lE8rSiWMa1jOUlPlle4pPlJT5ZXvaPVe8bY4wx9YQV+sYYY0w9YYV++P4V6wREkOUlPlle4pPlJT5ZXvaDtekbY4wx9YQ96RtjjDH1hBX6xhhjTD1hhX4VRGSkiHhFpFhEForIabFOU1VE5HYRWSAiW0VkvYhMFZHjgvYZKyIatHweqzRXRERGh0jn2oDt4tunQER2isgsEekayzRXRETyQ+RFReQD3/ZK8xpLItJXRKaIyBpfukYEba/ycxCRtiLyuogU+pbXRaRNVDNC5XkRkcYi8oiILBaR7SLys4hMEJHUoHPMCvFZTYynvPi2V/k9F5EEEXlGRDb48jxFRA6NakYIKy+hvjsqIs8G7BPz37Uwf39j9n2xQr8SIjIUGAM8BJwEzAWmBf8AxKH+uFkMewO/AX4FPhaRdkH7fYyb3ti/DIpiGqtjOWXTeXzAtlHAn4CbgFOAX4AZItIq2okMwymUzcfJgAJvB+xTWV5jqSVuGuw/AjtDbA/nc5iAy/NZvuVk4PUaTHNFKstLc1y6HvS9/g7oBPxXRIJnJX2Nsp/VdTWY5opU9blA1d/zp4ALgUuA04CDgP+ISMOaSHAlqspLctAy2Lf+7aD9Yv271p+qf39j931RVVsqWID5wEtB674H/h7rtFUzHy2BvcDggHVjgf/EOm1hpH00sKSCbQL8DNwZsK4ZsA24LtZpDyNvdwJbgGZV5TWeFqAIGFGdzwHogrvB6ROwT4Zv3dHxkpcK9jnWl87jA9bNAv4Z68+iqrxU9T0HWgO7gWEB6zoBJcCZ8ZSXEPu8BCyvTn5jlJcyv7+x/r7Yk34FRKQJ0B2YHrRpOu4OrjZphavV2Ry0PkNEfhGR70TkJRE5OAZpC8fhvmowr4hMFJHDfes9QEcCPiNV3QnMIc4/IxER4CrgDV+a/SrKazwL53PohfshnxtwXC6wnTj/rHBPvlD++3Oxr0p8qYg8Hqe1S1D597w70Jiyn92PwDLi+HMRkZbAxbiCP1i8/a4F//7G9PsSXF1l9mkPNATWBa1fB5wR/eQckDHAV8C8gHX/Bd4FvEAa8ADwqYh0V9VdUU9hxeYDI4BvgYOBu4C5vvavjr59Qn1Gh0QrgftpIO7LH/ijVWFeVXVj1FMYvnA+h47AevU9sgCoqorILwHHxx3fzf8/gKmq+lPApgm4eT0KgK7A34FuQGbUE1m5qr7nHXFPocFx39cRx58LcCnQBBgXtD4ef9eCf39j+n2xQr+OE5EncNVCGaq6179eVQM7HX0tIgtxP2Ln4L40cUFVpwW+93XKWQlcAcRdx8NquAZYoKqL/CuqyOsT0U2e8bXhvwG0AX4buE1VA4OpfC0iK4H5InKyqn4ZxWRWqrZ8z/fDNcC/VXV94Mp4y29Fv7+xZNX7FduAuwNOClqfBMRFj+qqiMiTuM45v1HVlZXtq6oFwE/AkdFI2/5S1SJgKS6d/s+hVn1GvurG3xG6arJUUF7jWTifw1qgg69ZAyht4jiYOPysfAX+W7in99PDqGnJw/1exPVnFeJ7vhZXo9k+aNe4/Q6JyIlAOlV8fyC2v2uV/P7G9PtihX4FVHU3sBBXDRtoIGXbWeKSiIxh33+4b8PYvz2uaunnmk7bgRCRpsAxuHR6cV+AgUHbTyO+P6MRwC5coVKhoLzGs3A+h3m4Dk29Ao7rBbQgzj4rEWkMTMIV+ANUNZwf2eNxhWdcf1YhvucLgT2U/ewOxXUki6vPJcC1uP9zH1e1Y6x+16r4/Y3t9yXWPRvjeQGG4nq2Xo37EozBda44LNZpqyLdzwJbccNFOgYsLX3bWwKP+/4TpeGGmMzD3RG3inX6g/LyONAP1/7dA/iPL2+H+bb/FSgELgCOAybi2lnjKh8B+RHgO4JGhYST1xinuyVwom/ZAdzj+zs13M8BmAZ87ft/18v399R4yguuyfN9YA1uiFTg98c/yqKz75h03/dnEK7j25dAwzjKS1jfc+B537ozcEOTZ+LaoOMmLwH7NPf9P7uzguNj/rtGFb+/sf6+RPXLVhsXYCSQj3syWwj0jXWawkizVrCM9m1vBnyEGxu6G9fmNRboFOu0h8iL/8uw2/dD/A5wbMB2wQ11+xkoBmYDx8U63ZXkZ4Dvszi1unmNcbr7V/B/amy4nwPQFtdGvtW3vAG0iae8+AqLir4/I3zHd/Llb6Pvd2EF7oGgXZzlJazvOZAAPOPLzw5gaix+C6r6P+bb50rcuPeUEMfHxe9aJf9/RgfsE7Pvi024Y4wxxtQT1qZvjDHG1BNW6BtjjDH1hBX6xhhjTD1hhb4xxhhTT1ihb4wxxtQTVugbY4wx9YQV+sbEgIj0EpG3fTPq7RaRjSIyQ0Su8M9jLiIjRERFJC3guHwRGRt0rsEi8rWIFPv2byMiDUTkKRH5WURKROT9GsxLmu+6I6rYz5+fI2oqLftLRM4TkVtDrO/vS3Ntm2TLmJBswh1jokxEbsFNoPMpLjLXKlwgjkxcdLQtwL8rOPx8XKAO/7kaAW/iQnPeiAtKsg3IAv4I/AkXlSyeZ+mLB+fhItLZxEamTrNC35goEpG+uILln6p6c9Dmf/tm5WpR0fGq+r+gVYfg5ut+W1XnBFyni+/Pp1S1JALpTtD4mnLZGLMfrHrfmOj6K7AJGBVqo6r+oKqLKzo4sHpfREbjQkQDvOKrhp4lIvm4EJ8AewOr3kUkWUTGi8gGEdklIotF5LKga/ir4fuKyGQR2QLM921rLiLP+ZojikRkCnDo/vxDVJLHa0Vkka+5YoOIvCIi7YL2URF5QERuFhGviGwTkdki0jVov4a+/X4WkR0i8qmIHOM7frRvn7G46YsP8a1X379hoOYi8k9fejaIyBsi0iaS+TYmGuxJ35go8bXVDwDeV9XiCJzyZWAJMBl4APgAV/WfANyMm83PP0vXDyLSAhfjuy1wB/AjcBnwuog017JzxINrNngL11Tg/614ETcR1X3AAtxMYRMikBcARORhXJPE08BfcDUZDwDHiUhvLTsn+WXAclwzRhPgMVxtyTGq+qtvn/t8eX0MNytbd2BK0GX/BnQATgF+61sXXKsxBjcB0qXA0cCjuKl0rziQ/BoTbVboGxM97XGTgqyKxMlU9ScR+cr39gdV/dy/TUTW+PYJXPcH3LziA1R1lm/1NBFJAh4QkVeCCtVsVR0VcPzRuELvTlV92Ld6uoi0BK4/0Pz4Oiz+BbhPVe8PWP8dkAMMxs2A57cHOFdV9/j2A3cDdCowV0TaArcAL6jqX33HzBCR3cA//CdR1R9EZD2wO/DfK8gcVb3J9/d037/F1SIyQm0CE1OLWPW+MfVHX2BNQIHv9wbuSffYoPXvBb3vgfvNeDto/cQIpW+g7/xvikgj/4JrWtiGS3+gGf4C3+dr32uq7/V4XP+IyUHHZe9H2j4Iev81rkYlaT/OZUzM2JO+MdGzEdgJHBaj67fDTeUZbG3A9kDB+yb7XtcFrQ9+v78O9r2uqGB7YtD7TUHv/VXyTX2v/vT+ErTf/qS3qmsZUytYoW9MlKjqryIyCxgYo97wm3Dt0cE6BmwPFFxt7b8JSAJWBqyP1NOuf1hhJrC5ku3h8qf3YGBpwHp7Ojf1llXvGxNdD+OeWB8NtVFEPCLSrYauPRs4VET6BK2/FPc0/E0Vx88HSoCLgtZfHJnkMcN3/lRVzQuxeKt5vq+B7cCQoPXB78E9uTerfpKNqV3sSd+YKFLVOb7Ib0+IyLHAWGA1rkf96cDVuEK4wmF7B2Asrqf7uyJyJ/ATMAzXln5dUCe+UGlfLiITgPtFpAGu934mMKia6ThLRNYGrStU1Rki8gjwT19HudlAMdDJl8aXVXVmuBdR1c0i8hRwh4hsw/XePxm4yrdLYPyCb4B2InIDkAcUq+rXGFPHWKFvTJSp6lMi8gXwf8DjuF7923CFzXXA1Bq67nYR6YerZXgYF9RnOTBcVd8I8zTXAUXAn3HD5D7F3aTkVCMpz4RYtxQ4TlXvEJFluOiCN+KaGH4EPgG+r8Y1/O4FBFfQ34yrrRgB5AKFAfu9DPQEHgLa4EZYpO3H9YyJa2KjTYwx9YmIZOF69PdV1c9inR5joskKfWNMnSUiPYBzcE/4xbjgPLfhajh62xh7U99Y9b4xpi4rwo3vvxE4CNdh8W3gdivwTX1kT/rGGGNMPWFD9owxxph6wgp9Y4wxpp6wQt8YY4ypJ6zQN8YYY+oJK/SNMcaYesIKfWOMMaae+H9HKOwngRPEdAAAAABJRU5ErkJggg==\n", 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" ] @@ -428,10 +430,9 @@ "source": [ "## Predicted Gate Fidelity\n", "\n", - "If we know the errors on the underlying gates (the gateset) we can predict the fidelity. First we need to count the number of these gates per Clifford. \n", + "If we know the errors on the underlying gates (the gateset) we can predict the error per Clifford (EPC) without running RB experiment. First we need to count the number of these gates per Clifford. \n", "\n", - "Then, the two qubit Clifford gate error function gives the predicted error per 2Q Clifford (predicted EPC). \n", - "It assumes that the error in the underlying gates is depolarizing. This function is derived in the supplement to [5]. " + "Then, the two qubit Clifford gate error function `calculate_2q_epc` gives the estimate of error per 2Q Clifford. It assumes that the error in the underlying gates is depolarizing. This function is derived in the supplement to [5]." ] }, { @@ -443,19 +444,24 @@ "name": "stdout", "output_type": "stream", "text": [ - "Number of u1 gates per Clifford: 0.263974\n", - "Number of u2 gates per Clifford: 0.975218\n", - "Number of u3 gates per Clifford: 0.467358\n", - "Number of cx gates per Clifford: 1.468341\n" + "Number of u1 gates per Clifford: 0.270742\n", + "Number of u2 gates per Clifford: 0.919105\n", + "Number of u3 gates per Clifford: 0.499017\n", + "Number of cx gates per Clifford: 1.491485\n" ] } ], "source": [ - "#Count the number of single and 2Q gates in the 2Q Cliffords\n", - "gates_per_cliff = rb.rb_utils.gates_per_clifford(qobj_list, xdata[0],basis_gates, rb_opts['rb_pattern'][0])\n", - "for i in range(len(basis_gates)):\n", - " print(\"Number of %s gates per Clifford: %f\"%(basis_gates[i],\n", - " np.mean([gates_per_cliff[0][i],gates_per_cliff[1][i]])))" + "# count the number of single and 2Q gates in the 2Q Cliffords\n", + "qubits = rb_opts['rb_pattern'][0]\n", + "gate_per_cliff = rb.rb_utils.gates_per_clifford(transpiled_circuits_list=transpiled_circs_list,\n", + " clifford_lengths=xdata[0],\n", + " basis=basis_gates,\n", + " qubits=qubits)\n", + "\n", + "for basis_gate in basis_gates:\n", + " print(\"Number of %s gates per Clifford: %f\"%(basis_gate,\n", + " np.mean([gate_per_cliff[qubit][basis_gate] for qubit in qubits])))" ] }, { @@ -467,26 +473,50 @@ "name": "stdout", "output_type": "stream", "text": [ - "Predicted 2Q Error per Clifford: 1.549505e-02\n" + "Predicted 2Q Error per Clifford: 1.568297e-02 (qasm simulator: 1.674174e-02)\n" ] } ], "source": [ - "# Prepare lists of the number of qubits and the errors\n", - "#Prepare lists of the number of qubits and the errors\n", - "ngates = np.zeros(7)\n", - "ngates[0:3] = gates_per_cliff[0][0:3]\n", - "ngates[3:6] = gates_per_cliff[1][0:3]\n", - "ngates[6] = gates_per_cliff[0][3]\n", - "gate_qubits = np.array([0,0,0,1,1,1,-1], dtype=int)\n", - "gate_errs = np.zeros(len(gate_qubits))\n", - "gate_errs[[1,4]] = p1Q/2 #convert from depolarizing error to epg (1Q)\n", - "gate_errs[[2,5]] = 2*p1Q/2 #convert from depolarizing error to epg (1Q)\n", - "gate_errs[6] = p2Q*3/4 #convert from depolarizing error to epg (2Q)\n", - "\n", - "#Calculate the predicted epc\n", - "pred_epc = rb.rb_utils.twoQ_clifford_error(ngates,gate_qubits,gate_errs)\n", - "print(\"Predicted 2Q Error per Clifford: %e\"%pred_epc)" + "# convert from depolarizing error to epg (1Q)\n", + "epg_q0 = {'u1': 0, 'u2': p1Q/2, 'u3': 2 * p1Q/2}\n", + "epg_q1 = {'u1': 0, 'u2': p1Q/2, 'u3': 2 * p1Q/2}\n", + "# convert from depolarizing error to epg (2Q)\n", + "epg_q01 = 3/4 * p2Q\n", + "\n", + "# calculate the predicted epc\n", + "pred_epc = rb.rb_utils.calculate_2q_epc(gate_per_cliff=gate_per_cliff, epg_2q=epg_q01,\n", + " qubit_pair=qubits, list_epgs_1q=[epg_q0, epg_q1])\n", + "\n", + "print(\"Predicted 2Q Error per Clifford: %e (qasm simulator: %e)\" % (pred_epc, rb_fit.fit[0]['epc']))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Given that we know the errors on the single qubit gateset, we can predict 2Q gate error from the EPC of two qubit RB experiment. The two qubit gate error function `calculate_2q_epg` gives the estimate of error per 2Q gate. In this tutorial, we prepare 1Q EPGs of each qubit using the deporalizing error model. In the experiment, EPG of each single qubit gateset (eg. `[u1, u2, u3]`) can be estimated with 1Q RB experiment." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Predicted 2Q Error per gate: 8.195786e-03 (model: 7.500000e-03)\n" + ] + } + ], + "source": [ + "# use 2Q EPC from qasm simulator and 1Q EPGs from depolarizing error\n", + "pred_epg = rb.rb_utils.calculate_2q_epg(gate_per_cliff=gate_per_cliff, epc_2q=rb_fit.fit[0]['epc'],\n", + " qubit_pair=qubits, list_epgs_1q=[epg_q0, epg_q1])\n", + "\n", + "print(\"Predicted 2Q Error per gate: %e (model: %e)\" % (pred_epg, epg_q01))" ] } ], @@ -506,7 +536,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.9" + "version": "3.7.3" } }, "nbformat": 4,