diff --git a/.gitignore b/.gitignore index b3d4b6bfd7..28f40a879a 100644 --- a/.gitignore +++ b/.gitignore @@ -147,3 +147,4 @@ docs/stubs/* test/ipynb/mpl/*.png test/ipynb/mpl/*.zip test/ipynb/mpl/result_test.json +test/Debug_RB.py diff --git a/docs/tutorials/rb_example.ipynb b/docs/tutorials/rb_example.ipynb index 941588f546..1196fda898 100644 --- a/docs/tutorials/rb_example.ipynb +++ b/docs/tutorials/rb_example.ipynb @@ -21,8 +21,10 @@ "rb = qe.randomized_benchmarking\n", "\n", "# For simulation\n", + "from qiskit.providers.aer import AerSimulator\n", "from qiskit.test.mock import FakeParis\n", - "backend = FakeParis()" + "\n", + "backend = AerSimulator.from_backend(FakeParis())" ] }, { @@ -38,37 +40,34 @@ "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "---------------------------------------------------\n", - "Experiment: RBExperiment\n", - "Experiment ID: 792f7a4b-4fa6-4a10-923e-df96eae4d8a0\n", - "Status: COMPLETE\n", - "Circuits: 140\n", - "Analysis Results: 1\n", - "---------------------------------------------------\n", - "Last Analysis Result\n", - "- popt: [0.46242383 0.99615594 0.52058961]\n", - "- popt_keys: None\n", - "- popt_err: [1.68690459e-04 2.71196427e-06 1.71806389e-04]\n", - "- pcov: [[ 2.84564709e-08 4.24770960e-10 -2.88930652e-08]\n", - " [ 4.24770960e-10 7.35475018e-12 -4.38101233e-10]\n", - " [-2.88930652e-08 -4.38101233e-10 2.95174352e-08]]\n", - "- reduced_chisq: 1113.6527324644458\n", - "- dof: 11\n", - "- xrange: [1.0, 500.0]\n", - "- EPC: 0.0019220290414467822\n", - "- EPC_err: 1.3612147214267936e-06\n", - "- plabels: ['A', 'alpha', 'B']" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "---------------------------------------------------\n", + "Experiment: RBExperiment\n", + "Experiment ID: 703b5fd1-80a1-4e52-ab82-48e8f75ecbd9\n", + "Status: DONE\n", + "Circuits: 140\n", + "Analysis Results: 1\n", + "---------------------------------------------------\n", + "Last Analysis Result\n", + "- popt: [0.43230292 0.99856134 0.55623197]\n", + "- popt_keys: ['a', 'alpha', 'b']\n", + "- popt_err: [0.12348736 0.00055429 0.12494436]\n", + "- pcov: [[ 1.52491284e-02 6.81301612e-05 -1.54265489e-02]\n", + " [ 6.81301612e-05 3.07233728e-07 -6.89998149e-05]\n", + " [-1.54265489e-02 -6.89998149e-05 1.56110931e-02]]\n", + "- reduced_chisq: 0.1438533698176071\n", + "- dof: 11\n", + "- xrange: [1.0, 500.0]\n", + "- EPC: 0.0007193320210475695\n", + "- EPC_err: 0.00027754263239147705\n", + "- success: True\n" + ] }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -89,7 +88,7 @@ "expdata1 = exp1.run(backend)\n", "\n", "# View result data\n", - "expdata1" + "print(expdata1)" ] }, { @@ -105,37 +104,34 @@ "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "---------------------------------------------------\n", - "Experiment: RBExperiment\n", - "Experiment ID: 30e27585-6311-4da6-8b3e-7908fd0320b7\n", - "Status: COMPLETE\n", - "Circuits: 100\n", - "Analysis Results: 1\n", - "---------------------------------------------------\n", - "Last Analysis Result\n", - "- popt: [0.69765588 0.9647669 0.26493626]\n", - "- popt_keys: None\n", - "- popt_err: [3.04628480e-04 2.88150690e-05 9.45301854e-05]\n", - "- pcov: [[ 9.27985106e-08 -2.50866441e-09 -4.37601950e-09]\n", - " [-2.50866441e-09 8.30308199e-10 -1.76959681e-09]\n", - " [-4.37601950e-09 -1.76959681e-09 8.93595595e-09]]\n", - "- reduced_chisq: 193.60044947837932\n", - "- dof: 7\n", - "- xrange: [1.0, 200.0]\n", - "- EPC: 0.02642482193432663\n", - "- EPC_err: 2.240054216973096e-05\n", - "- plabels: ['A', 'alpha', 'B']" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "---------------------------------------------------\n", + "Experiment: RBExperiment\n", + "Experiment ID: ff2c526a-a888-45e1-a9bc-3c20c70176ce\n", + "Status: DONE\n", + "Circuits: 100\n", + "Analysis Results: 1\n", + "---------------------------------------------------\n", + "Last Analysis Result\n", + "- popt: [0.70646921 0.97206894 0.26277466]\n", + "- popt_keys: ['a', 'alpha', 'b']\n", + "- popt_err: [0.01547605 0.00166093 0.00968495]\n", + "- pcov: [[ 2.39508071e-04 2.78505493e-07 -7.36601626e-05]\n", + " [ 2.78505493e-07 2.75867255e-06 -1.05750604e-05]\n", + " [-7.36601626e-05 -1.05750604e-05 9.37981598e-05]]\n", + "- reduced_chisq: 0.040350819405537176\n", + "- dof: 7\n", + "- xrange: [1.0, 200.0]\n", + "- EPC: 0.02094829359579109\n", + "- EPC_err: 0.0012814872009544206\n", + "- success: True\n" + ] }, { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY0AAAEPCAYAAAC+35gCAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy86wFpkAAAACXBIWXMAAAsTAAALEwEAmpwYAABJb0lEQVR4nO3dd3hUZfbA8e9Jb7SAQEAgQZCqKCBFBSJF0GBDVKxgQ0UF110rFrAtirriiiIW+FlxFVdRUEAhKoKoICxVpQSk9xLSk/P7405CMmSSG8gwITmf57kPmfeWOfM65uTet4mqYowxxrgRFOgAjDHGnDgsaRhjjHHNkoYxxhjXLGkYY4xxzZKGMcYY10ICHYA/1alTR+Pj48t83qFDh4iOji7/gCoxq7OysfoqO6uzsjmW+lq0aNEuVT2puH2VOmnEx8fz66+/lvm85ORkEhMTyz+gSszqrGysvsrO6qxsjqW+RGSDr332eMoYY4xrlfpOw1Ru8fHxbNjg8w8iY46bJk2akJKSEugwjgtLGuaEtWHDBmxGA1MRiEigQzhu7PGUMcYY1yxpuGB/zRpjjMMeT3lJTk7mwIED6NatSEoKGh/PzKVLiYiIsJ4bxpgqz+40ClFVMjIyOOnrr8mLj0f79SMvPp60iRPJyMiwOw5jTJVnSaMQEaFvu3ac9eZbBGdlIQcOEJyVxSXTp9O3Xbsq1dhlSvbqq6+SkJBAREQEHTp04Icffij1nP/85z+cccYZREVF0aRJE8aOHXvEMVlZWTz22GMkJCQQHh5O48aNefnll4u93ocffoiI0L9//yLlubm5PProowXxJSQk8Mgjj5CTk3N0H9alvXv3cv3111OjRg1q1KjB9ddfz759+0o9r7S6VFVGjRpFgwYNiIyMJDExkRUrVhQ5JjMzk7vvvps6deoQHR3NxRdfzKZNm8olPuNFVSvt1qFDBy2z+fM1PSxGFQq2vBo1VOfPL/u1qpC5c+ce9/d0vr7H35QpUzQkJEQnTpyoK1eu1Lvuukujo6N1w4YNPs+ZMWOGBgcH6/jx43Xt2rX65ZdfalxcnP773/8uctyAAQP0rLPO0lmzZun69ev1p59+KrZu165dqw0bNtRu3bppUlJSkX1PP/201qpVS6dNm6br16/Xzz//XGvWrKlPPPGE6884d+5cbdKkievjVVX79eunrVu31h9//FHnz5+vrVu31v79+5d4jpu6HDNmjMbExOgnn3yiy5Yt0yuuuELj4uL0wIEDBcfcfvvtGhcXp7NmzdJFixZpjx49tF27dpqTk3NM8bkVqO9iSY7l/0ngV/XxezXgv9j9uR1N0sjbskWzQ8OKJI2csDDN27KlzNeqSk7EpHHo0CG99dZbtXr16lq7dm19+OGH9eDBgxoZGakpKSk+z+vUqZPecsstRcqaNWumDz74oM9zrr76ar300kuLlL388st68skna15enqqqzpw5U6tXr647d+4sMe6srCzt1KmTTp48WQcPHnxE0khKStIbbrihSNkNN9xwxHElKWvSWLlypQI6b968grIffvhBAV29erXP80qry7y8PK1fv74+9dRTBfvT0tI0JiZGJ0yYoKqq+/bt09DQUH3vvfcKjtm4caOKiH799dfHFJ9bVSlp2OOpQlSVmUuX8vPNN5NDMArkhIbxeVISM5cudbKsqTRuvPFG5syZwzfffMOHH37IuHHjuOuuu2jVqhVNmjQBICUlBRFh8uTJgPP4aNGiRZx//vlFrnX++eczf/58n++VmZlJREREkbLIyEg2bdpUMEDxs88+46yzzuLFF1/k5JNPpnnz5gwfPpzU1NQi540cOZL4+HgGDx5c7Hude+65zJ07l9WrVwOwcuVK5syZw4UXXui+cspowYIFxMTEcPbZZxeUnXPOOURHR/usFzd1uX79erZt21bkmMjISLp3715wzKJFi8jOzi5yTKNGjWjVqlXBMUcTnyme9Z4qRESIiIhg1wX9eGVBR+5ZejNzb51C1EWRREREWJtGJbJr1y4+/vhj/u///o+zzjoLgCuvvJJJkybx5JNPFhwXGhpKixYtqFGjRsF5ubm51KtXr8j16tWrxzfffOPz/fr27cuIESOYNWsWvXv3Zs2aNbzwwgsAbN26lfj4eNatW8e8efMIDw9n6tSp7Nu3j7vvvpstW7bwySefADBr1iw++ugjlixZ4vO9HnjgAQ4ePEjr1q0JDg4mJyeHkSNHMmzYMJ/nbNy4kdatWxe8zs3NJTMzk5iYmIKy6667jgkTJhR7/rZt2zjppJOK/D8iItStW5dt27YVe46busw/t7hjNm/eXHBMcHAwderUOeKY/POPJj5TPNdJQ0QGA1cDjYEIr92qqqeUZ2CBkpiYSHJyMnn9zyVv6S2k/rScS195xBJGJbNmzRpUla5duxaUde7cmUmTJjFgwICCsoYNGxb8xV6Y9/dBVUv8jtx6662sXbuWSy65hOzsbKpXr86IESMYNWoUwcHBAOTl5SEifPDBBwVJ6pVXXqFv375s376d4OBghgwZwgcffECtWrV8vtdHH33EO++8wwcffECbNm1YsmQJI0aMICEhgZtvvrnYcxo0aFAkES1cuJAHHniA5OTkgrLq1av7fM/i6gRKr5fizivunLLWd3HHHG18pihXSUNEHgVGA8uBJUCmH2OqEM7sFct/n76MNVur25eqEgoPDwcgLCysoKxevXrUqlWryF/c3urUqUNwcPARf53u2LHjiL+GCxMRnn32WZ555pmCv3q//fZbwJlDCyAuLo6GDRsWJAyAVq1aAc6dwKFDh9i6dSu9e/cu2J+XlwdASEgIK1asoEWLFtx333384x//YNCgQQCcdtppbNiwgX/+858+k0ZISAjNmjUreL1p06YjykpSv359duzYUeSXsKqyc+dOn/Xipi7r168POHcKjRo18nlMbm4uu3bt4qSTTipyTPfu3Y86PlM8t20aNwPjVPV0Vb1GVW/03vwZZCCcdRZcFTyVkTtGcOhQoKMx5S0hIYGgoCD+/PPPgrJp06axd+9e9u/f7/O8sLAwOnTowOzZs4uUz549u8jzcl+Cg4Np2LAhYWFhfPjhh3Tt2pW6desCzjP2LVu2FGnD+OOPPwBnQryzzjqLZcuWsWTJkoLt4osvplu3bixZsoSEhAQA0tLSCu5eCr9vfoLxh65du5KamsqCBQsKyhYsWMChQ4d81oubukxISKB+/fpFjsnIyOCHH34oOKZDhw6EhoYWOWbTpk2sWrWq4Jijic/44KuFvPAGHAR6ujm2Im1H1eVWD/c6OPNMVSFXv5l26KiuU5WciL2nBg4cqD179tRDhw7p6tWrtVq1atqgQQN99913C47ZtGmTtmjRQj/99NOCsilTpmhoaKi+8cYbunLlSh0+fLhGR0cX6XH14IMPas+ePQte79y5U1999VVduXKl/vbbbzp8+HCNiIjQhQsXFhxz8OBBPfnkk3XgwIG6fPlynTdvnrZp00YHDhzo8zMU13tq8ODB2rBhQ/3yyy91/fr1+umnn2qdOnX03nvv9XmdnJwc3bp1a4nbvn37SqzPfv36adu2bXXBggU6f/58bdu27RFdWlu0aFGkm7GbuhwzZoxWq1ZNp06dqsuWLdOrrrqq2C63DRo00NmzZ+vixYs1MTGx2C63pcV3tI71u+gPAe1yC3wJ/M3NsRVpO9akcc9tabqDOvpN99FHdZ2q5ERMGtu3b9dLL71UY2NjNTY2Vl944QWdMWOGNmjQQB9//HFVVV2/fr0COmnSpCLnjh8/Xps0aaJhYWHavn17/e6774rsHzx4cJEuqzt37tQuXbpodHS0RkVFaa9evfSnn346IqbVq1drnz59NDIyUhs0aKDDhg0r8svRW3FJ48CBAzpixAht3LixRkREaEJCgj700EOanp7u8zr5n7OkbfDgwT7PV1XdvXu3XnvttVqtWjWtVq2aXnvttbp3794ixwAFdZuvtLrMy8vTxx9/XOvXr6/h4eHavXt3XbZsWZFj0tPT9a677tLY2FiNjIzU/v3768aNG8sc39GqSklDnP0lE5FmwKfA88AMYE8xdyz+u/c9Sh07dtRjWbnv/feh1XXtCapVkzP2zPFDhJVHIFZVExHcfH+N8beK+F08xpX7Fqlqx+L2uW3T+ANoC0wCtgPZXlvWUUVWwXXtCskk0nLvfDQ9I9DhGGNMwLntcvsEzi1qlZKQAL/VPI+Iff/ir6kLaXRdj0CHZIwxAeUqaajqKD/HUSGJQN7Z3cibIWyfMteShjGmyivzNCIiEiMijUQk2h8BVTTtetTk77zAjLx+gQ7FGGMCznXSEJG+IvIrsA9IAfaLyM8i0sdPsVUIZ58NL/E3PljXJdChGGNMwLlKGiLSF5gOxABPAsOAp4BqwIzKnDjOOguiwnI46fcf2Ld4XaDDMaUYMmQIInLE1qXL4aQfHx9fUB4VFUXbtm15/fXXi1wnKyuLsWPHcuaZZxIdHU1sbCxdunTh9ddfJzOzbBMifPfdd3To0IGIiAiaNm3qc/6mwkpb+2Hp0qVcffXVNGrUiMjISFq0aMHYsWOPGMCnqrz00ku0bNmS8PBw4uLiePDBB4t9z3nz5hESEkLbtm3L9PmOhmrpa2QUx01dTp06ldatWxMeHk7r1q3573//e8Qx5bGGR5Xlqy9u4Q1YgNPVNsirPMhTPt/NdY73dqzjNPJdcPY+zSFIlw987KiuVxVUlHEagwcP1t69ex8xMG337t0FxzRp0kQfe+wx3bp1q/755586cuRIBXTKlCmqqpqZmamJiYlao0YNHTdunC5evFjXrVunH330kXbu3LlMn3XdunUaFRWld911l65cuVInTpyoISEh+sknn5R4XmlrP7z11lt6991369y5c3Xt2rX64YcfakxMjD799NNFrvO3v/1Nmzdvrp999pmuXbtWFy9erNOnTz/i/fbs2aMJCQl6/vnna5s2bVx/vnyArl+/3vXxbtbI8OamLufPn6/BwcH61FNP6cqVK/Wpp57S4ODgImNiymsND+/PX9EEenBfGpDkY19/IM3NdY73Vl5J45FHVH+mo/7RoPtRXa8qqEhJo7R1I5o0aaJjx44tUta8eXMdNGiQqqo+++yzKiL6yy+/HHFubm6u7t+/33WM999/vzZr1qxI2c0336xdunTxec7Rrv1w3333afv27Qter169WkNCQnTlypWlxnnZZZfpqFGj9PHHH/d70nCzRkZx3NTllVdeqb179y5yTK9evQr+26qWzxoe3qpS0nDbppEJ+JrishqVfALDbt2c8RpNtv4E6emBDsf4QUREBNnZ2QC8//779O7dm44djxzbFBQUVDDb6+TJkxERUlJSfF53wYIFR6wX0bdvX3799deC9yvunKNZ++HAgQNFZr/9/PPPadq0KV9//TVNmzYtWINjx44dRc579dVX2bZtG4888ojPa5cnN2tkFMdNXfo6Jv+65bWGR1XmNmkkA0+KSELhQhFpDIwC5pZvWBVL167wQ1AiYZpF+pwFpZ9gAurrr78mJiamyPbAAw8Ue2xOTg6TJ09m2bJl9OrVC4A///yzYHbZktSoUYMWLVoQGhrq85ht27YVuxZETk4Ou3bt8nlOWdd+WLx4MZMnT+aOO+4oKFu3bh0bNmxgypQpTJ48mXfffZfVq1dz0UUXFbR9LFu2jNGjR/P+++8fMclhSdq0aVOkfr3L2rRp4/PcktbIKGltCzd16euY/OuWtIZH4bU3jia+qsLt4L4HgB+B30XkJ2ArUB/ogtObqvj/IyuJatXg4BndyF0cxKb3kmme1DPQIZkSdO/enYkTJxYpq1mzZpHXI0eOZNSoUWRmZhIWFsZ9993HbbfdBuB6OojLLruMyy67rNTjilsLorjyks7JP6+48t9//52kpCTuueceLr/88oLyvLw8MjMzeffddzn11FMBePfdd2nRogW//PILZ5xxBoMGDeL5558vmCHXrRkzZhS5U2revDkzZsygYcOGACUmUl+f0dfnK+0c7/LyWp/jaOKrCtwO7vtDRE4H/g50A9rjzD81DviXqm71X4gVQ/vE6py1+Bf61GvDs4EOxpQoKiqq1HUg7r33Xm6++WaioqKIi4sr8svg1FNPZdWqVeUSS/369YtdLyIkJITatWv7PMft2g+rV6/mvPPOY9CgQYwZM6bIvri4OEJCQgoSBji/3ENCQti4cSP16tVj5cqV3Hjjjdx4o7O6QV5eHqpKSEgIM2bMOOIxTr785XC9y/LXBimJmzUyfJ1XWl36Oib/uuW1hkdV5nqchqpuVdV/qGpnVW3u+ff+qpAwALp3h99oz/cLwwMdiikHtWvXplmzZjRo0OCIvx6vueYavvnmG4qb7DIvL48DBw64fp+uXbsesQzs7Nmz6dixo8+/xt2u/bBy5UoSExO54oor+Ne//nXEdc455xxycnJYu3ZtQdm6devIycmhSZMmNGzY8Ij1OW6//XaaNWvGkiVL/LbOhJs1Morjpi67du1a4voc5bWGR5Xmq4W8Mmzl1XtKVXXXLtU67NAXgv+hmT8sPPKkKq4i9Z4qrsvtjh07Co4prvdUYRkZGdq9e3etWbOmjhs3Tn/77Tddt26dTp06Vbt27VrwWT/99FNt0aKFbtq0yee18ruJjhgxQleuXKlvvPGGhoaGFukmWtx1Slv7Yfny5Vq3bl296qqrjvis+XJzc7V9+/bavXt3Xbx4sS5evFi7d++unTt31tzc3GLjddt7aseOHSWuvVG4vovjZo2M66+/Xq+//voy1eWPP/6owcHB+swzz+iqVav0mWee0ZCQkCO63JbHGh6FFfddDLTj3uUWmAO0LPRzSdu3vq4TyK08k4aqaqeW+zWbYE25/pGjum5lVpGSBsWsBdGwYcOCY0pLGqpO4hgzZoyefvrpGhERoTVr1tTOnTvrhAkTNDMzU1VVJ02a5KqraXJysp555pkaFham8fHx+tprrxXZX9x1Slv74fHHH/e57kVhW7Zs0YEDB2pMTIyedNJJes011+i2bdt8xuo2aTRp0qTEtTcKryVSHDdrZPTo0UN79OhRpKy0ulRV/fjjj7VFixYaGhqqLVu21KlTpx5xTHms4VFYVUoaPtfTEJG5wB2qulpEkilllltVPa8MNzjHxbGup+Ft2DAY/Fpn6jcKpcnGeeUQYeVh62mYqqwifhf9tZ6Gz4bwwklAVY/unSuZbt1g7mvn8fdNL8KhQxBdJeZsNMaYAm7nnrpBRIrt6iEisSJyQ/mGVTH16OEM8gvVbHK+t0E+xpiqx23vqUnAKT72JXj2V3oNGsDO5uewnbqsmb890OEYY8xx5zZplDSiJRrIKYdYTgide1ejPtt4T68LdCjGGHPc+WzTEJEzcAbx5btIRLznTI4EBgF/ln9oFVOvXvDaa0JyMqDqLO9njDFVREkjwi8BHvf8rMBIH8ftBm4uz6AqssREaMkq3p5/KZlf/pvwi4ofMWv8r0mTJjatg6kQihshX1mVlDReAibjPJpaBwwAfvM6JhPYrhWtr5kf1a4NtU5rRMKydfz13lyaWtIImJJml63oAtFF+URndVYxlNTldj+wH8Azu+1WVc06XoFVZF37xPDLsrNo8ENyoEMxxpjjylVDuKpusIRxWM+eTtfbk7f+AqmpgQ7HGGOOG9cTForIUBH5TUTSRCTXe/NnkBVN9+7wfdB5hJBL2uwfAx2OMcYcN64H9wH/Bn4BInDGZbwHHADWAk/4K8CKqFo1yOp4NpMYws9ri5/e2hhjKiO3dxr3AP8E8pcFe1VVBwNNgXScHlSuicgwEVkvIhkiskhEupVyfF8RWSAiB0Vkl4h8LiKnlnSOv53dJ5qbmMRHa4udnsUYYyolt0mjOfA9kOfZwgBUdS/wNDDC7RuKyFU4izc9A5wJzAe+8iwdW9zxCcDnwA+e43vjjA+Z4fY9/aFnTwBl4+zfIS0tkKEYY8xx4zZppANBnq6123DuMPKlAg3K8J73ApNV9Q1VXaWqd+MsH3uHj+M7AKHAQ6q6RlWX4Nz1nCIidcrwvuXq7LOhb1gy09e2ZM9/vwtUGMYYc1y5TRrLgPz1M38AHhaRriJyFjAKWO3mIiIShpMEZnntmgX4WhLrVyAbuEVEgkWkGjAY+EVVd7mMv9xFREBEj85kEcpf7yYHKgxjjDmuXK0RDkzk8N3Fo8A3QP6CEgeBS11epw4QDHjP9rcd57HTEVQ1RUT6AB8D43ES3W/ABcUdLyJDgaEA9erVIzk52WVoh6Wmpro67+RTT2bh7M7UWTCb5ORiw6ky3NaZcVh9lZ3VWdn4rb58rc5U0oYzSWFv4GKgThnOa4AzJUk3r/LHgdU+zqkP/AE8h9Om0R1I9mxBJb1fea/c523lStXRPKrZBGvu3v1H9V6VRSBW7juRWX2VndVZ2fhr5T7X4zS8Es0hVf1GVadp2R4R7QJyPYmgsLocefeR707gkKrer6q/qer3wHVAD3w/0jouWraElSclEkIuaybbSn7GmMqvpFlui+3N5IuqbnRxTJaILALyHzfl6wNM9XFaFE6iKSz/9VElvfIiArX7d2XApKl03HI2DwcyGGOMOQ5KatNIoZR1wb0EuzzuReBdEfkZ+BG4Heex1QQAEfkn0ElVe3mOnw78TUQeBz4AquF01/0LWFSG+PyiZ1IkAycNYMv3WNIwxlR6JSWNmyhb0nBFVT/yLB37CBAHLAcuVNUNnkPiKLRKoKrOEZFrgPuB+3C6//4E9FPVQ+UdX1n16gXxQRtJ/GUKB/66jeqNagQ6JGOM8ZuSZrmd7K83VdVXgVd97BtSTNkUYIq/4jkWNWtC/1ZrGbPiAeaNb825Y/oHOiRjjPGbgLYJVBZxl3UhkzBSv0wOdCjGGONXrsZpiMjbpRyiqlplVu/z1qt/JAue6kqDP5NtBVhjTKXmdnBfT45s34jFaZTe59mqrI4d4YXIRP6e/iSrf9pHq641Ax2SMcb4hdtFmOJVNcFrqwEk4sxFdbk/g6zogoMhu1siuQSz8M1lAPkDE40xplI5pjYNz0C7f+GstVFlJScnk9E+mhasZt5PIejWrcycOdOmPDDGVDpuH0+VZB3O9B5VkqqSkZFB22WvsoIvyV4ZSm58BmlJSTB0KKqKWCOHMaaSOKakISIhwBBgU7lEcwISEfq2a0fe7M8JJgtIhyy4ZPp0gsaPt4RhjKlU3PaemlNMcRhwKlAbZ1R3lSUpKQRFREBWVkFZUGQkkpICcXGBC8wYY8qZ2zuNII7sPXUQ+BSYoqrJ5RnUiUbj48nLyCgyj0peejpB8fHYfYYxpjJxlTRUNdHPcZywVJWZS5eSlpTEJdOnk50dRIRm8Enbi6mxdCl969e3R1TGmErDRoQfIxEhIiKCqKFDCUpJ4YPLPiGHIEL2xxIREWEJwxhTqbhuCBeR5jiTDHYFGgKbgfnAU6q6xj/hnRgSExMLekk1vyeJZz4dybaDp/NqYmKgQzPGmHLltiE8EZiBM8PsdJwFk+oBFwFXiUg/Vf3OTzGeEPLvKLp2hUtjn2DPDrhzBbRpE+DAjDGmHLl9PPUCzrrcTVT1BlW9T1VvAOKBJZ79BggJgYsvhjrsZMnozwMdjjHGlCu3SaM18KyqphYuVNWDwLOA/T1dyIABcB9jufLjgbDd1yq2xhhz4nGbNDbhjMsoThhO+4bx6NMH/hN1I6HksPvl9wIdjjHGlBu3SeNZYLSINCxc6Hn9OM7yq8YjIgKaXdSK+XRF33gLbPJCY0wl4TZp9MCZBn2tiCSLyEcikgysBWKARBF5x7P9n59iPaEMGABvcxN1dq6ChQsDHY4xxpQLt0njXCAX2Ao0ATp5/t0K5AHdvLYq74IL4POwKzlIDPtn/BjocIwxply4HRGe4O9AKptq1aBr3+o0/mIjo2rXYkSgAzLGmHJgI8L9aMAA2EctPv0UyM0NdDjGGHPMyjIiPAq4Cad9IxbYDSQDk1U1zS/RneAuvthZ1e+KH+4mq+cqwr77JtAhGWPMMXF1pyEi9YHFwMtARyAKOAt4BVgkIvX8FuEJLDYWevWCLRpH2PffwpoqPduKMaYScPt46jmgFtDNsz54V087x7lATZwuuaYYgwbBO9xALkEweXKgwzHGmGPiNmlcADykqkW6AanqfJxJDJPKO7DK4rLLYGfYycykL7lvT7a2DWPMCc1t0ogBtvjYt8mz3xSjZk2n++3b3ETw1s0we3agQzLGmKPmNmn8DlzvY991wOryCadyGjQIvuAi/t1wDLRrF+hwjDHmqLntPfU88I6nwfsDnEF99YFBQG98JxQDXHQRhESFM3zzA/TPABv0Yow5Ubm601DV94DbgbbAmzhrarwFnA7crqof+C3CSiA62ul+C8qvD3wMX3wR6JCMMeaouB7cp6oTgQY406B38/zbUFXf8FNslcqgQQBCqy+eg8ceC3Q4xhhzVEpMGiIyRESWiEiqiGwCxgJrVfVHVV2lqnnHJ8wTX79+UKMGjM+4CZYsgd9+C3RIxhhTZj6ThohcDbyNM5BvOk47xt+wadCPSng4DBwIH3I1WcER8PbbgQ7JGGPKrKQ7jXuA/wKtVPUqVT0LGA3cKSLBxyO4ymbwYNhPTb4MHYC+/z5kZAQ6JGOMKZOSksapwBuqWng02qtAONDYr1FVUueeC02bOo+o0qJOgpSUQIdkjDFlUlLSqAHs8SrLf13LP+FUbiJwww0wh54M7rwaWrYMdEjGGFMmpfWeChKRgg0ILq7cs8+4cMMNAML0GcKB7elw4ECgQzLGGNdK+2X/I5BdaEv3lC/0Ks/yV4CVTUICdO8OoRkHCDvlZHjppUCHZIwxrpU0Inz0cYuiihk8GL7/vjr/C25Pp0mT4JFHIMhu1owxFZ/PpKGqljT8ZOBAuOsu+NeBm/jwwDWQnAw9ewY6LGOMKZX9eRsA1avD5ZfDZ1xKWnhNG7NhjDlhWNIIkKFDIYNIpgRdg06dCvv3BzokY4wplSWNADn3XGjVCp5Ov5dvH/vOuf0wxpgKzpJGgIg4dxvrOIVnZndyCowxpoKzpBFA11/vzEn1x9zNHBh0KyxfXrBPVQMYmTHGFM+SRgDVru30pMognKiP/w99+WVYsADdupWZM2eSnJwc6BCNMaYI10lDRBqKyIsi8quIrBORtp7ye0Sks/9CrNxuvVXZTR1+09PgjTfQfv3Ii48nbeJEMjIy7I7DGFOhuEoaItIGWIazrOsWoAkQ5tndBBjhl+iqgO7dhXObbeV0XY4AcuAAwVlZXDJ9On3btUOsrcMYU4G4vdN4AViFs7z1AKDwb7L5QJdyjqvKEIG7klLIIKJIeVBkJGKz4BpjKhi3SeNcYIyqpgLez0u2A/XL8qYiMkxE1otIhogsEpFupRwvnsdgq0UkU0S2isiYsrxnRdbvjnhCyS5SlpeejsbHByYgY4zxwW3SKGlZ1zocnsiwVCJyFTAOZwXAM3HuVL4SkZLW6HgBGAY8ALQCLgS+d/ueFZmqsmD9Up5tej9pRJIWWoPcsDA+T0pi5tKl1qZhjKlQSpqwsLCfgRuBL4rZdyXObLhu3QtMVtU3PK/vFpF+wB3AQ94Hi0gL4G7gdFVdVWhXpVhkW0SIiIig0UO9aHrr7bRkHdNe30ps0CGIiLA2DWNMheL2TuNJ4CIRmYXTGK5AbxH5P+Ay4Gk3FxGRMKADMMtr1yzgbB+nXQKsA/p5em2liMj/iUhdl7FXeImJidx887mc0bc+q7JPIfqWq+nxyy8kJiYGOjRjjClC3D7+EJEk4CXglELFKcCdqvqVy2s0ADYDPVT1+0LljwHXqmqLYs6ZAAwBlgL34SSs5z27u6pqntfxQ4GhAPXq1eswZcoUN6EVkZqaSkxMTJnPO1Y//RTLQw+dzocRN3AFH7Pgo4/IOUGmFwlUnZ2orL7KzuqsbI6lvs4777xFqtqx2J2qWqYNaIZzV9DiKM5tgPNLv5tX+ePAah/nTPScc2qhslM9ZZ1Ler8OHTro0Zg7d+5RnXescnNVmzVTbcMyVVB9+umAxHE0AlVnJyqrr7KzOiubY6kv4Ff18XvV7TiNxzx3CajqGlWdr6q/e/bFee4U3NgF5HJkb6u6OL2wirMVyFHVPwqV/QnkACU1np9wgoJg+HBYQVt+rN7PGSGemRnosIwxpoDbNo3HgZN97Gvg2V8qVc0CFgF9vHb1welFVZwfgRARKfxYrClOI/4GN+97IrnpJoiNhccO/IPsLIXVqwMdkjHGFHCbNErqwlMLKMufwy8CQ0TkFhFpJSLjcBLPBAAR+aeIfFvo+G+AxcDbInKmiJwJvI2zTvmvZXjfE0J0NNx5J8yhJwPab4B27QIdkjHGFPDZ5VZEEoHCa5DeJiL9vQ6LBJKAFW7fUFU/EpHawCNAHLAcuFBV8+8a4ijU2K6qeZ73fRlnbEY6MBu4V70awSuLu+6CsWOF6d9GsGJpDm3q7oS4uECHZYwxJY7T6IHzix2cRucbizkmC1gJDC/Lm6rqq8CrPvYNKaZsK3BFWd7jRFa3Ltx4I7z2GgSd3wtaB8HcuYEOyxhjfD+eUtXRqhqkqkE4j6e65L8utEWoantVXXD8Qq4a7r3XaRiftOtiSE6GXyvdkzhjzAnIVZuGJ0H87O9gzGHNmsGAATAh71bSw6rDCy8EOiRjjCn7IkwiUldEGntv/giuqnv4YThIdSbkDkU//hhs1ltjTIC5HacRJCLPiMhunHET64vZTDk780y46CJ4MXc4eSrwzjuBDskYU8W5vdO4B7gTZ7ZZwZmh9imcZLEWuNUfwRl47DHYRCO6hS5k522PlH6CMcb4kdukcSPwBPCs5/V/VfVxnGnKN1PJRmZXJB07woUXwoLM9ox5LghsqnRjTAC5TRpNceYiycWZviMSQFWzcSYxvMkv0RkAHveMt9//yrvktDndphYxxgSM26SxHwrWI90CFJ6NNgSILc+gTFGdOkG/frAxqx4hq5bDhx8GOiRjTBXlNmn8BrT2/DwTGC0iV4vIFcA/cab5MH40ejTMpg/L5DSyxzxvj6mMMQHhNmm8BKR5fn4c2Aa8D3wEhAJ3lXtkpohOneCyy4Sx+g9Cf18BM2cGOiRjTBXkdnDfbFV93fPzNqATzpoWZ+Csc/E/v0VoCjz9NPxHBrGZBqQ98XzpJxhjTDlzO07jBs8kgwB41ulY40kW1UTkBr9FaAq0agXXDAljGK/yZNiTgQ7HGFMFuX08NYmiy7wWluDZb46DUaNgZvgljPmuK4sWBToaY0xVUx7raUTjdMM1x0Hjxs56Gw3YzOb+t6EbNgY6JGNMFVLSehpnAO0LFV0kIm29DosEBuEsv2qOk4cfhllv5nLhtrdYM6IazT+z9g1jzPFR0noal3B4GVcFRvo4bjdwc3kGZUpWuzbc8kRjPrrnKi6eNpGsnY8SdlKNQIdljKkCSno89RJOe0VTnMdTAzyvC28NgLqqOs2/YRpvw4bBJ43/TjU9yI9D3gh0OMaYKsLnnYaq7scZCY6IJABbPNOGmAogNBRufa093yb1pOVXL7Fz83BOahgW6LCMMZWc23EaG/IThoiEi8gwEXlFRB4WkQb+DdH4cuGF8G2HB5imFzHq/rSCcrXR4sYYP/GZNETkCRFZ4VUWDiwE/g0Mw5ke/TfPnYgJgBZ31+Tu4PF8+kE6yyYuQLduZebMmSQnJwc6NGNMJVTSnUZvYIZX2Z3A6cBYoAbQBcgGbKGHAFBV6tXbw+NNH2U9CTS9vQ958fGkTZxIRkaG3XEYY8pdSUnjFMB7XfBLcVbue0hVD3rWDR8L9PJPeKYkIkLfdu14eMPzRJBJtB4iOCuLS6ZPp2+7doiUNLzGGGPKrqSkUQPYnv9CRMJw5pyaq0X/hF0KxPknPFMaSUkhKCKiaFl4JGLriRtj/KCkpLEZiC/0ujMQBsz3Oi4UOFS+YRm3ND6evIyMImU5qelofHxgAjLGVGolJY0fgHtEpKY4zzmGA3nAdK/jzgQ2+Sk+UwJVZebSpXyelERuWBh5MdXJQ9iv1Xji+TXWpmGMKXclJY3ROHca24F9wOXARFXd4HXcEGCeH2IzpRARIiIiiBo6lKCUFIJmfc2MO6bxDCN56c2z2LXL2jSMMeWrpMF96z3zT90C1AJ+VtV3Cx/jGaPxLfCOP4M0viUmJqKqTqN3XBwXdobnVyr7vhOG357Fh1NtwJ8xpvyUOLhPVTeq6mOqerd3wvDs3+LZ94v/QjSlKdxLKigI3n5b6BuezNhPmzLr5dUBjMwYU9m4nRrdnECaNoUrRp5KBBnE3Xs1O/7KDHRIxphKwpJGJXXjyAa80HYyp+UuYV63B7E2cWNMebCkUUkFBcGwGf2ZEDacARte4ss7vDu9GWNM2VnSqMQaNYKT3n6WJbQj5c3ZrFwZ6IiMMSc6SxqV3OXXRjDh6u8ZnvsSV14J6emBjsgYcyKzpFEFjH29OqecArpiBe+f/3+BDscYcwIraY3wOWW4jqqqTVpYQVWrBp98Ais7jOGKeVP48rFW9H+iU6DDMsacgEq60wjCWeY1f2sJJOKMEo/0/JsItPDsNxXYGWdAzosvs4UGtH7qalYsOBDokIwxJyCfSUNVE1X1PFU9DxiHs25GF1VtqqpdVbUp0NVTPu74hGuOxfXDa/Fuvw9ooimsOf8O9u+zfrjGmLJx26bxJPCoZ/2MAqq6EBiFs4KfqeBE4N6p5/Ba3VFckvoBLyV+Rm5uoKMyxpxI3CaN5sBOH/t2AM3KJxzjb1FRkPTjwzwQ9W+eWXoh998f6IiMMScSt0ljPXCbj323ASnlEo05LhKaBXPB9LvICwnnrRf38e5bWQX7bDp1Y0xJfPae8jIaeF9ElgOf4EyXXg8YiNNAfq1/wjP+kpgI/7h1EUNfG8jMW/uxJOsG2l0az8ylS4mIiCAxMTHQIRpjKiBXdxqqOgXoC+wHHgLGe/7dB/RV1Y/8FaDxD1Wlx8U72VGtDrfrBJoP601ukwTSJk4kIyPD7jiMMcVye6eBqn4DfCMiQUAdYJeq5vktMuNXIkLfdu3Iy/wfANGkQTZcPH0GwePHF5lu3Rhj8pV5RLiq5qnqDksYJz5JSSEoIqJIWWZOKGkrUgITkDGmwnN9pyEiTYErgcZAhNduVdWbyzMw438aH09eRgbBhcokL5erHorno7OdnlbGGFOYq6QhIpcAH+PcmewAvFf1sQfgJxhVZebSpaQlJXHJ9OkERUaSm5bBiKCXmPtrNR7tNpdn5p9HeHigIzXGVCRuH089BSQDcaraQFUTvLam/gvR+IOIEBERQdTQoQSlpCBffUXwhvV0+ncrngt7mDGLz2f8OR+QnR3oSI0xFYnbx1NNgb+rqq8BfuYElJiYiKo6jd5xcQhwyy31WdaqHQsSl3HPout446z93PTLHYSGBjpaY0xF4PZOYzVQu7zeVESGich6EckQkUUi0s3lec1F5KCIpJZXLFWddy8pEeH0c6sT8e0Mvg7uz21Lh/Fhu3+SlWlPII0x7pPG/cDDnsbwYyIiV+FMcPgMcCYwH/hKRBqXcl4YMAX4/lhjMKXr1COSevOm8lHotfRZ9TKDL9pDRkagozLGBJrbx1OjcO40VonIn8Aer/2qqj1cXuteYLKqvuF5fbeI9APuwBkw6MuzwP+A7wC372WOQYcuocj8dzi/92aWz67N1r7KtM/yqF4ruPSTjTGVkrgZ+SsiyZTSQ8ozhXpp1wkD0oCrVfXjQuXjgba+Eo+IJAEvAe2By4FXVDXGx7FDgaEA9erV6zBlypTSwjpCamoqMTHFXr5KWrcumvvuO52H9oykdcw6Mt8cTq16RY+xOisbq6+yszorm2Opr/POO2+RqnYsdqeqHrcNaICTfLp7lT8G/O7jnDhgC85aHgBDgFQ379ehQwc9GnPnzj2q8yqzdetUn459XhX0u8i+umZpapH9VmdlY/VVdlZnZXMs9QX8qj5+rwZqjXDvuxYppizfe8BrqvqTf0MyJUlIgFtW/Z3Rjd7knPTZ7OzQl/kz9h0xR5X3a2NM5eJ2cF/30o5RVTcN1LuAXKC+V3ldnJlzi9MT6CEij+eHAwSJSA4wTFUnunhfUw7q1oV7V9zMP7vV4P6l17AkqS8P/mMCY+6tT/UVK9AWLWyWXGMqObcN4cmUPuq71NZRVc0SkUVAH5wR5vn6AFN9nHaa1+tLgJFAJ2Bzae9pyle1avDgrwOZMKAac79IJer55WT962xODw8m7957SUtKgqFDD4//MMZUKm6TRnGN3LWB/jg9me4qw3u+CLwrIj8DPwK347R1TAAQkX8CnVS1F4CqLi98soh0BPK8y83xExICd03rS+TTW7n6kVMIz81wujeAMyWJzZJrTKXlKmmo6nc+dn0qIv8CLgK+cnmtj0SkNvAITiP3cuBCVd3gOSQOOMXNtUxg3dwzhZyoUEhLP1wowcjatRAXF7jAjDF+Ux4N4dNxZr91TVVfVdV4VQ1X1Q6F20NUdYiqxpdw7mT10d3WHF8aH4/kFB3xF5yZzr5LrkdnfxOgqIwx/lQeSaMFYGtrVDHqmSX386QkcsPCyI6KJjMognHcxZo9tblndE127gR27IBUm/XFmMrCbe+pG4opDgPaAjcDn5ZnUKbiy58ll6FDCRo/nqWffsqZAwaw+7WtnPfiC6T+GMaUtvBr2/tptOQLuPtuZ6tdblOYGWMCwG1D+GQf5ZnAR8CIconGnFAKz5J7oE0bJC6O0aPrc8stwjXXwI8/wsA5d/Bao320Hz0ann8ehg6Fe++Fk08OdPjGmKPg9vFUQjFbnKpGetog9vsrQFOxFTdLbuPG8P33To5YGt6ZDn99Rs+TlvFXx8vg5ZfhuecCFK0x5li5ShqquqGYzddgPGMICoK//x1++w06dIC5O9vS+Lt3uaP3n2y76WHnoO++gyuvdA4yxpwQytQQLiL9RWSsiLwlIs95JhI0xqdWrWDhQnjxRWfN8QkzEzi1e31eeAFy1qTAzJnQvj306+fcntg0JMZUaK6ShohUE5HvgGk47RcXAvcA00QkWUSsC6zxKTgY/vY3WLUKLrgADh6Ef/wDTnt+MHMmb4RnnnHuNnr0gMsvD3S4xpgSuL3TeAZnWvLrgUhVjQMigRs85c/4JzxTmTRuDDNmwJdfOhMgrl4NvQbUoG/yQyz7IgVeecXJKgA5OTB1qvOvMabCcJs0LgceUdX3VTUXQFVzVfV94FHPfmNcSUpy7jqeftqZy2rWLGjXJZLBP9/Jul63OgdNnw4DB0KLFjBhArZsoDEVg9ukURtY6WPfSspx/XBTNYSHw8MPw7p1cMcdziOsd95xcsTtt8NfZ1wEn30Gdeo4ByQkOL2uMjMDHboxVZrbpLEeZ3LC4lzo2W9MmdWpA6++6jyqGjQI8vLg9dfhlOZB3PrlJax57yf49lto2xbefhtCQ50Ts7MDG7gxVZTbpPE6zlreb4lITxFpJSLnicjrwHA8M9Qac7ROOQU+/BCWL4cBA5ymjDffhBYthUETe/LLM7OdblhBQc60JE2bwvDhsHFjoEM3pkpxO07jX8AY4BpgNs7MtN/iNISPUdVxfovQVCmtWjnt36tWwbXXOjnio4+gUyfo1r8Gn34KOQfSoHdveO01J9vceKNzq2KM8Tu3XW5rAE/grHvRHydZJAENVHWk/8IzVVWLFvDee06bx/DhUL06zJvn9MiN71SXJ5tOYvv8tTBsmJNVWreG338PdNjGVHqlJg0RCQF2A31Uda+qfuXpRfWVqu71f4imKmvUCMaNg82b4YUXnBuLzZvhscegYdfGXJIyjq8mbCBn/AQn04DTdXfOHBsoaIwflJo0VDUHZ/3uXP+HY0zxYmKceQ7//NPpopuUBCIwbRpcOPgkGo4ayj33wKIFWegLL0CvXtCli9MDK89m7jemvLhtCH8PuMWfgRjjhgj06eMMENy0CZ56Cpo1c5btGDcOOp4dxukhq5h24Wtkbd4Jl10Gp50G8+cXXEO97kC8XxtjfHM7NXoKcI2I/AJ8DmwFivyfpqpvl29oxpSsXj0YOdIZ77FwIUye7DSiL18TwSVrbieYW7gn7j/cu2ssO7bU5fQ8+Gnqx6SGhdGnUyckJQWNj2fm0qVERESQmJgY6I9kTIXnNmmM9/zbEOhQzH4FLGmYgBBxnkR16eI0Z8ye7XTf/fLLEF7Yeg0vcDVcIdSvr3ya+wbn7vkBJRuiosjLzCQtKQmGDi1YG8QY45vbpJHg1yiMKSchIc70VRdc4Iz/mzMHPvlE+Oor2LxZeI7b+YRvCSIPDh4kGLjk8y/IfWm8kzBmznRa3+PjnWl5jTFFuEoaqrrB34EYU95CQ6FvX2dTdSbSXfRKHOnvxBCTe6DguKy8EPq3TKHmOdFM/abf4QvUq+ckj7vvdgaNZGY607cnJDizL4aFHf8PZUyAub3TKCAi3o3nqtaSaCo4EWfZjjOfjifv/QyvvoDCivQEdn8Tydn8SDwpNA9ez1msp8XWFPb+IjRMhIb71sD55zunBAVBw4ZOUnnkEad83z5YutRJKg0bOhNqGVPJ+EwaIlIfeAv4SFXf8ZQFA1leh6aKyKm2kp+p6FSVmUuXkpaUxCXTpxMUGUleejpfJfXjxYGryMpKZM6cs/n557P58HecjuYA45ytab14ruyaTOe662kVuZ6Ts1OI2rEeCfL8HbVwobOYFDjPyRo3PjzRYvv2sG0brF/vlNnfWeYEVdKdxjCctTIGepUL8AawxfPzVcDtwGh/BGhMeRERIiIiYOhQgsaPR1JSCIqPJ2rpUmIjhMREYcgQ59g9e5zVaH/8EX75BZYsgXXboxmzvQfQo+CasbHQ7hloOw06NO1Ex3/NIl7XE70zxUkQ69cffoz1xRcwdCgA3cLCnPmzEhKcqd8bN4aUFNi927l7iY11bo+MqWBKShr9gDdUNd2rXIHXVXUxgIjsxJlWxJKGqfASExMP95KKi0OAvvXrH9FrKjbWGeJx2WXO67w8+OMP+Okn54bif/+DlSud5DJ3rrNBLaAPAPXrO/NotTgTWnwDp26Elm0uovFnXxKyKYUt339Po5wcJ1HkN7i//TY8+aTzc7VqTkKJj4cPPoDoaCeAjAynvFq1Mn92795h1lvMHI2SkkYL4LFiyr2/ZX94jjXmhOD9i9LNL86gIGjZ0tny70ZUnUl2Fy1ymjKWL3d+r69Z4zyJ2rYtP5nkq09wcBKNG0PNmtfQvn0tmnaAJjOhSRNo2u8m6rVrT/DG9YfvUjZvPpxUxoyBSZOcn2vXdhJKy5bOJF0AK1Y47Sjx8RARUST+5ORkMjIy6NuunY1PMcekpKQRAaQWLlDVXBGJA3YVKs7wHGtMlSLi/LJv0sSZzj1fXp4z0eKyZc7ku6tXO683bHBGsa9fD1CL337zvmI8QUHxNGjg9Ppt2BAangsNxkKDBnDKuffRqG0/YvevJ3LbemRDCuwq9L/iPffAN984P9ev79yRdO2KPv88GRkZhD33HHnz5jltORkZNj7FHJWSksYOoCkwr3BhMQ3eCcDOco7LmBNWUJAztUmzZkfuy8x07kS++OJ/hISczrp18NdfTjLZvBl27nR+3rSpuCu38mxOd+J69aBuXah7AZx0EpxR71maXbmKBpnrqZOaQo2965E/d5K+TejRoh0Rc+c6jwmyswkGLvvvfyE8HMlvvL/nHuf2KTbW2WrVcu5kOnZ09u/eDTVqOI38psLy92PIkv7rzwOuB94p5Ro3AD+WW0TGVGLh4dCmDezcuYfingplZTl3JCkpzqOvv/5yksn27c78Wjt2ODcXBw8emVzepT1O35VCFgNfQBdSmE00MRwq2JUjYcxeXI8v7nBywb1T5lBj/wbCMw6PYdmZNISdz00iJlpp1DwOyc5Gq1VD8hPL4MEwYoSzatajjxZNOLGx0Ly5c8uU31vsON/RVLV2nMKPIauvWIG2aFHujyFLShovA/NE5HngQc9stwU8U6Y/ByQC3colGmOquLAw5/ds8+YlH5eWBlu2ONv27U77yc6dzrZ7tzNkZN8+2LsX9u+HTXvjCcopOttvtgZz0x8Psv0P5/Wz/A+AYHKoyT5i2UPG9Aj+mg5B5HEHLxLLHmqn7uGkjL2ctG0P3z0dyRdvQ/2wA0xf9DwhRX9NMKPrE/zQ41HqZm3m7nGnkBkdS1Z0LbJiYsmpHsvGC25n39kXEpW+m5PnTYFatZA6sQTVjiW4Ti2CGzckpHoUYWGQnh5EdrZzo+Pm935Va8dRVTIyMkibOJG86dM5PTSUvHvvLffHkD6ThqouEJH7cRLDdSIyG8hfW7MxTjeROsBDqrrgmCMxxrgWFeX7EZg3VeXrr5fy5atJDJj5BYRHoJkZTGo/hKu7bqdevXocOCAcOODcwRw8GEJqah0OHapDaipEpENqajDvHrqLtDTnpoL8Jdp3Otv/iCWULKI5RCx7qMVeYtnDxgWNWbcAahNBJn+j1oG9xB7YQyx7iOUvXlp0kP8AHVnHL9x1ROzX8h4fcC2d+Yn3uZElxLKXWuyTWPYFx/J2xJ2sjWjDybKZDnm/cDA0lkPhsRwKq8n27Fb02vsRPQ5cRm5QGCF5Wcyq/RTfNR5M/fpKcLAQEuL0HShtCwry+jlICZY8gkKCCAoWQjSbEHIICcpzysXZnx1dk6AgCM86SFhOGkHkOZsoQaKk12mECEQd3E545gEELdhPUBCpDVsQFAQxO9YRlrbv8H7yyAsN5+ApZyAC1dcvJezQXk46kEq7aV8SnJvt3LaCMyZp/Phyu8Mq8eGkqr4gIouBB4DLOdzgnQF8DzynqnPKJRJjjF+ICJGREcidNxM88eWCv7pPWbqUNhF7SUws2y+TzExnmfaDBw//m5YGhw4Jhw7FkJYWQ3p6Y9LTKbTVISVjDL9nOr2Gs7KcLTMTemRBTmZ7+qRtJzJjL1EZe4jO2kNM5h7+CD6H2DwgI5qfM7pQM28vNdlLE91AbM4ePki9kl2p0JN5vMmgI2LNIJwIMiE3A4Dnd93Htl3PA0IQeZzDj6zjFIYxnqd4hCDyivxiPoW1bCOOR3iSxxntKT88MLM6+zlIdZ7nQf7Oi0fWPXmAMIF/cCMTi+xLJZpqnr5G7/M3LuXDIvu3Up8GbAVgGiO4gC+L7P+D5rTAuU2cywi68F2x/72CIiORlBSIi/P537QsSm3RUtW5wFzPaPDaOF1ud6mqLcpkzAnC7fgUN8LDna127fKMMBio69mKcxrJybeSmJhIbq4zGWVWFnyS5fycvfsCNq5djO7eA3v2oHv2ErH6N+rMfB/SMgu9TQhyZkfSasSRlyf889JoUmOg7orW/LX4BhQhV4PIkyDy8oT7z4siPQwa/XkO89c+6KQSlYJ/7+gQRlYQhG9I4vNtdY/Yf1NrZ2Db3i1XM3nvGZ6UI+QSRK6EcmUTp7fdql3DGJd6Ibnq7M8jiAyJpH99pzlozu6RLM68pcj1M4Kj6VXT2f/hgRf5PHs/0dl7eWzV1YTp4Yk78tLTCYqPP2KsxNFy3Q3CkyR2lNP7GmOOs6MZn1IR5T8mKjIUJa46tD2zyHG6dSt58UVXbNBgof60iYjnr+7DTUfnebaiOhX81NOzFXV+KfuvKvgp0bMVdUfBT+d6Nl+6lLAPoL0zTc7MmXw50ZkmR0NDkexsPk9KImrp0qP+I8Gb25X7jDHmhJE/z9jnSUnkhoWhNWqQGxbG50lJzFy6tFKu1pg/TU7U0KEEpaTwv2efJSglhaihQ4mIiDg+bRrGGHMiKmmesfL8BVrRFH4MeaBNGyQurtzuMPJZ0jDGVErl2Y5zIvH3Y0h7PGWMqbQqSztORWJJwxhjjGuWNIwxxrhmScMYY4xrljSMMca4JpWxv3I+z6qCG47i1DoUXTPElM7qrGysvsrO6qxsjqW+mqjqScXtqNRJ42iJyK+q2jHQcZxIrM7Kxuqr7KzOysZf9WWPp4wxxrhmScMYY4xrljSKN7H0Q4wXq7OysfoqO6uzsvFLfVmbhjHGGNfsTsMYY4xrljSMMca4ZknDGGOMa5Y0vIjIMBFZLyIZIrJIRLoFOqaKQERGiYh6bdsK7RfPMVtEJF1EkkWkTSBjPp5EpLuITBORzZ66GeK1v9T6EZFwEfm3iOwSkUOe6518XD/IceSiziYX8537yeuYKlNnIvKQiPwiIgdEZKeIfCEibb2O8fv3zJJGISJyFTAOeAY4E5gPfCUijQMaWMXxOxBXaDut0L77gb8DdwNn4SwNPFtEqh3vIAMkBlgOjADSi9nvpn5eAi4Hrga6AdWBL0Uk2H9hB1RpdQbwDUW/cxd67X+JqlNnicCrwNk4a8vmAN+ISGyhY/z/PVNV2zwbsBB4w6vsT+CfgY4t0BswCljuY58AW4GRhcoigYPAbYGOPQB1lQoMKUv9ADWALODaQsc0AvKAvoH+TMe7zjxlk4EvSzinqtdZDJALXOR5fVy+Z3an4SEiYUAHYJbXrlk4md1AU8+jhPUiMkVEmnrKE4D6FKo7VU0HvsfqDtzVTwcg1OuYv4BVVO06PFdEdojIHyLyhojULbSvqtdZNZynRXs9r4/L98ySxmF1gGBgu1f5dpz/EFXdQmAIcAFwK06dzBeR2hyuH6u74rmpn/o4fzV6TzBXlevwa+AGoBfOI5dOwBwRCffsr+p1Ng5YAizwvD4u3zNbI/xI3qMdpZiyKkdVvyr82tMguQ4YDOQ3Tlrdlexo6qfK1qGqTin0cpmILMKZtToJ+LSEUyt9nYnIi8C5wLmqmuu126/fM7vTOGwXTgb2zrZ1OTJzV3mqmgqsAJoD+b2orO6K56Z+tuHc6dYp4ZgqTVW3AJtwvnNQRetMRP6F04jdU1XXFdp1XL5nljQ8VDULWAT08drVB6cXlSlERCKAljgNb+txvox9vPZ3w+oO3NXPIiDb65iTgVZYHQIgInWAhjjfOaiCdSYi44BrcBLGaq/dx+d7FugeABVpA67C6Vlwi6cSx+H06mgS6NgCvQHPAz1wGts6A18CB/LrBnjA83oA0BaYAmwBqgU69uNUPzHAGZ4tDXjM83Njt/UDvAZsBnrjdPmei/PMOjjQn+9415ln3/NAVyAep7vpApw7jSpZZ8B4z3eoJ87dRP4WU+gYv3/PAl4RFW0DhgEpQCZOVu4e6Jgqwlboy5fl+cJNBVoX2i843XK3AhnAd0DbQMd9HOsnEeeZsPc22W39ABHAv4Hdnl+iXwCNAv3ZAlFnOF1FZ+KMM8jCacuY7F0fVanOfNSVAqMKHeP375nNcmuMMcY1a9MwxhjjmiUNY4wxrlnSMMYY45olDWOMMa5Z0jDGGOOaJQ1jjDGuWdIwFZaIdBWR/3gWlMkSkd0iMltEBufP/S8iQzyL88QXOi9FRCZ7XesiEVnmWVxLRaSmiASJyEsislVE8kTkMz9+lvjiFhoq5rj8z9PMX7EcLRG5VETuLaY80RNz70DEZY4vm7DQVEgicg/wIjAHZ5TrBqAWcD7OiNZ9wOc+Tr8MZ1Rs/rVCgPdxpkm4E2ew2EFgIM4CQH/HGW28u9w/SOVyKc4o4hcDHIcJIEsapsIRke44v5heUdXhXrs/98zwGe3rfFX9zauoIc7aA/9R1e8LvU8rz48vqWpeOcQdrqqZx3odYyoyezxlKqIHgT04S1ceQVXXqur/fJ1c+PGUiIzCmRYG4C3PY5RkEUnBmW4BILfwoyMRiRORdzxrKGeKyP9E5Dqv98h/jNRdRD4WkX04a44gIlEi8qrncVqqiEwDynXdahG5VUSWeh637RKRt7yW/cQT31MiMtyzcNZBEfmumDWjgz3HbRWRNBGZIyItPeeP8hwzGWca/IZyeL3uFK+wokTkFU88O0XkPRGpWZ6f2wSe3WmYCsXTVpEIfKaqGeVwyTdx1qH+GHgKmI7z6CocGI6zsFRXz7FrRSQaZ76eWsDDwF/AdcC7IhKlqhO9rv8+8CHOo678/59ex5n8cjTwC86Moh+Uw2cBQETG4DxSexm4D+dO6imgrYicrUXXV7gOZ233EUAYMBbnbq2lquZ4jhnt+axjcdbkbg9M83rbJ4GTcNadvthT5n1XNQ5nIstrgBbAczjLDQw+ls9rKhZLGqaiqYMzWd2G8riYqm4SkSWel2tVNX/BKERks+eYwmV34azXcJ6qJnuKvxKResBTIvKW1y/lT1T1/kLnt8D5pTlSVcd4imeJSAxw+7F+Hk+D/33AaFV9olD5H8A84CLgs0KnZAP9VTXbcxw4CbQTzsqLtYB7gAmq+oDnnNkikg28kH8RVV0rIjuBrML15eV7Vb3b8/MsT13cIiJD1Ca5qzTs8ZQxRXUHNhdKGPnew/lLu7VX+X+9XnfG+f/qP17lUygffTzXf19EQvI3nEdjB3DiL2x2fsLwWOb5t7Hn39Nw2oc+9jrvk6OIbbrX62U4d3T1juJapoKyOw1T0ewG0oEmAXr/WA4v8lPYtkL7C/M+Ns7zb3HrNJeHup5/1/jYX9vr9R6v1/mPlCI8/+bHu8PruKOJt7T3MpWAJQ1ToahqjogkA30C1BtpD87zeG/5S2h6d8v1fuySn0Tq4ayhTqHX5SH//c8H9paw3638eOviLN+bz+4OTLHs8ZSpiMbg/MU8tridIpIgIqf76b2/A04WkXO8yq/B+Wt8VSnnLwTygCu9ygeVT3jM9ly/sar+Wsy2vozXWwYcAq7wKvd+Dc6dQ2TZQzaVid1pmApHVb/3jDx+0TOWYjKwEadHUy+c5XivAXx2uz0Gk3F6Gn0qIiNxlhe9Fqct4TavRvDiYv9dRD4AnhCRIA73nrqwjHH0E5FtXmX7VXW2iDwLvOJpaP4OZ4W2Rp73eVNV57p9E1XdKyIvAQ+LyEEO95662XNI4fErK4FYEbkD+BXIUNVlmCrFkoapkFT1JRH5GfgbzlrRdXBGcf8K3IazRKU/3veQiPTA6S46BmdQ4O/A9ar6nsvL3Iaztvw/cLq5zsFJcvPKEMq/iylbgbN058MisgpndPudOI/I/gK+Bf4sw3vkexxnmdCbcbohL8TpivwjsL/QcW8CXYBngJo4Pdzij+L9zAnMlns1xhxBRK7A6QHWXVV/CHQ8puKwpGFMFScinYEknDuMDKADzqj834GzbYyFKcweTxljUnHGd9wJVMdp8P8P8JAlDOPN7jSMMca4Zl1ujTHGuGZJwxhjjGuWNIwxxrhmScMYY4xrljSMMca49v+Atbrn+mv4uQAAAABJRU5ErkJggg==\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -156,7 +152,7 @@ "expdata2 = exp2.run(backend)\n", "\n", "# View result data\n", - "expdata2" + "print(expdata2)" ] }, { @@ -172,41 +168,37 @@ "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "---------------------------------------------------\n", - "Experiment: InterleavedRBExperiment\n", - "Experiment ID: 2bf05234-9ce5-411a-b3c5-e14575da2d24\n", - "Status: COMPLETE\n", - "Circuits: 280\n", - "Analysis Results: 1\n", - "---------------------------------------------------\n", - "Last Analysis Result\n", - "- popt: [0.47626118 0.99623266 0.99535996 0.51013026]\n", - "- popt_keys: None\n", - "- popt_err: [1.77332947e-04 2.66140415e-06 2.83096706e-06 1.79438603e-04]\n", - "- pcov: [[ 3.14469741e-08 4.28977668e-10 4.58298066e-10 -3.17868436e-08]\n", - " [ 4.28977668e-10 7.08307204e-12 6.39967732e-12 -4.36846717e-10]\n", - " [ 4.58298066e-10 6.39967732e-12 8.01437450e-12 -4.66354152e-10]\n", - " [-3.17868436e-08 -4.36846717e-10 -4.66354152e-10 3.21982123e-08]]\n", - "- reduced_chisq: 800.0272045289706\n", - "- dof: 24\n", - "- xrange: [1.0, 500.0]\n", - "- EPC: 0.00021900082002077048\n", - "- EPC_err: 1.9493171174852214e-06\n", - "- systematic_err: 0.0016646691935545965\n", - "- systematic_err_L: -0.001445668373533826\n", - "- systematic_err_R: 0.001883670013575367\n", - "- plabels: ['A', 'alpha', 'alpha_c', 'B']" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "---------------------------------------------------\n", + "Experiment: InterleavedRBExperiment\n", + "Experiment ID: f180d9c2-4802-42c1-bfd8-ed3370105c89\n", + "Status: DONE\n", + "Circuits: 280\n", + "Analysis Results: 1\n", + "---------------------------------------------------\n", + "Last Analysis Result\n", + "- popt: [0.45521149 0.99869098 0.99906658 0.52983366]\n", + "- popt_keys: ['a', 'alpha', 'alpha_c', 'b']\n", + "- popt_err: [0.04251155 0.00017159 0.00016453 0.04329273]\n", + "- pcov: [[ 1.80723211e-03 7.06812871e-06 6.40084436e-06 -1.83883578e-03]\n", + " [ 7.06812871e-06 2.94425710e-08 2.40670557e-08 -7.23479207e-06]\n", + " [ 6.40084436e-06 2.40670557e-08 2.70685875e-08 -6.53292038e-06]\n", + " [-1.83883578e-03 -7.23479207e-06 -6.53292038e-06 1.87426083e-03]]\n", + "- reduced_chisq: 0.11377780079833481\n", + "- dof: 24\n", + "- xrange: [1.0, 500.0]\n", + "- EPC: 0.0004667115741436856\n", + "- EPC_err: 8.226266996891632e-05\n", + "- EPC_systematic_err: 0.000842313005943951\n", + "- EPC_systematic_bounds: [0, 0.0013090245800876366]\n", + "- success: True\n" + ] }, { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY0AAAESCAYAAAABl4lHAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy86wFpkAAAACXBIWXMAAAsTAAALEwEAmpwYAABozklEQVR4nO2deZyN5RfAv2f21djN2GYGZc2+JoyyJolfUaGElIgSqSSULFFRkUikREJR9n1fJyRbtiFryM6Y7fz+eO9MM9cs15jNzPP9fJ7PnfdZ3uc879x7z32Wc46oKgaDwWAwOIJTZgtgMBgMhnsHozQMBoPB4DBGaRgMBoPBYVwyWwCDIbV4enqeCQ8PL5TZchgMHh4eZ2/evOmf2XJkBGI2wg33KiKi5v1ryAqICKoqmS1HRmCWpwwGg8HgMEZpGAwGg8FhMlxpiEh9EZkvIidFREWkkwNtHhCRNSJy09buPRHJEVNBg8FgyEpkxka4D/AnMM2WkkVEcgHLgLVADaA0MBW4DnycXNv8+fNrUFBQqoS8fv063t7eqWp7r5ITx5xaxo8fz6hRozh9+jTly5dnzJgx1KtXL9k2s2bNYtiwYfz1118UKFCAnj170q9fvwR1xo0bxxdffEFYWBjFixdnwIABPPfccwnqXLlyhXfffZfZs2dz4cIFihUrxrBhw2jbti0Aw4cPZ+7cuRw4cAB3d3dq167N8OHDqVChQto+BDsuXrxIr169mD9/PgCPP/44n3/+Oblz5062XUrPUlUZMmQIEydO5OLFi9SqVYtx48ZRvnz5uDq3bt2ib9++zJgxg5s3b/LII48wfvx4ihYtetfyOUr16tWz1Abb3XyeQ0NDz6tqgUQLVTXTEnAN6JRCne7AFcAzXt67wElsG/lJpWrVqmlqWbVqVarb3qvca2O23r4Zz8yZM9XFxUUnTpyoe/fu1Z49e6q3t7ceO3YsyTYLFy5UZ2dnHTdunB4+fFh/++03DQgI0M8//zyuzvjx49Xb21t/+OEHPXz4sM6YMUN9fHx0/vz5cXUiIiK0Vq1a2qxZM123bp0ePXpU161bp1u3bo2r06RJE/3mm2909+7d+scff+gTTzyhhQoV0gsXLjg8xlWrVmlgYOAdPZdmzZppuXLldMOGDbpx40YtV66cPvbYY8m2ceRZjhgxQn18fHT27Nm6e/dufeqppzQgIECvXLkSV+fll1/WgIAAXbp0qYaGhmqDBg20UqVKGhUVdVfyOUpmvReT424+z8B2Teo7OamCjEgOKo1pwAK7vBqAAsHJtTVK486418Z8tx/U69ev64svvqi5cuXSfPny6TvvvKNXr15VT09PDQsLS7JdzZo1tWvXrgnySpUqpW+99VaSbZ555hl94oknEuR99tlnWrRoUY2JiVFV1Tp16uhrr72WoE6fPn20bt26cddfffWVBgcH661btxwe59WrV9XJySmB8kmJO1Uae/fuVUDXr18fl7du3ToFdP/+/Um2S+lZxsTEqL+/vw4dOjSu/MaNG+rj46MTJkxQVdVLly6pq6urfv/993F1jh8/riKiixcvviv5HCUnKY17wU7DHzhhl3c2XtnR+AUi0g3oBlCoUCFWr16dqk6vXbuW6rb3KjltzC+88AKhoaEsX76cS5cu0bp1a06ePEnZsmUJDAwEICwsjODgYKZMmUKnTp2IiIggNDSUvn37JrhXkyZN2LhxY5J93bp1Cw8PjwR5np6enDhxgmPHjhEUFJRkna1btxIZGYmrqyu//PILdevW5dVXX2XevHnkzZuXtm3bMmDAAFxdXRPt++rVq8TExJAnT57UPCaH2LRpEz4+Pjz44INxeXXr1sXb25uNGzdSunTp29o48iyPHj3KmTNnaNKkSVy5p6cn9evXZ+PGjbz00kuEhoYSGRmZoE6xYsUoW7YsGzdupGnTpqmS707Jap+d9Po83wtKA6xZRXwkiXxUdSIwEaw1xpCQkFR1uHr1alLb9l4lJ435/Pnz/PTTT3z77bfUqFEDgLZt2zJlyhQ++OCDuHqurq6ULl0aPz+/uHbR0dEUKpTQprBQoUIsX748yf6aNm1K7969Wbp0KY0aNeLQoUN8/LG1JXf69GmCgoJo2rQpkydPpk2bNlSvXp3Q0FC+/vprIiMjOX/+PAEBARw5coSVK1fy7LPPsmDBAsLCwujRowfXrl1j9OjRifbdu3dvKleuTJ06dZKU7/jx45QrVy7uOjo6mlu3buHj4xOX16FDByZMmJBo+zNnzlCgQAHin08REQoWLMiZM2cSbePIs4xtm1idkydPxtVxdnYmf/78t9WJbZ8a+e6UrPbZSa/P872gNM5gzSjiU9D2ehaDIRUcOnQIVU3wRVqrVi2mTJlCmzZt4vKKFCnC/v37b2tvf3hPVW/Li8+LL77I4cOHadWqFZGRkeTKlYvevXszePBgnJ2dARg4cCBnzpzhwQcfRFUpVKgQzz//PB999FFcnZiYGAoWLMikSZNwdnamWrVqXLhwgddff51Ro0bdJkOfPn1Yv34969evj7tHYhQuXJidO3fGXW/ZsoX+/fsn+KWaK1euJNsn9kwceS6JtUuszZ0+78TqpFY+Q0LuBTuNTUA9EYk/b28MnALC0qNDtbMytr823Pu4u7sD4ObmFpdXqFAh8uTJk+AXtz358+fH2dn5tl+n//zzz22/huMjIowcOZJr165x7Ngxzpw5Q82aNQGIPeHn6enJN998w40bNwgLC+P48eMEBQXh6+sb9ys6ICCA+++/P4ECKFu2LDdu3OD8+fMJ+nz99deZMWMGK1eupESJEsk+DxcXF0qVKhWXihQpcltewYIFk2zv7+/PP//8k+CzoqqcO3cuyefiyLP097d+L6ZUJzo6+rbx29e5U/kMiZMZdho+IlJZRCrb+i9uuy5uKx8uIiviNfkBuAFMFZEKItIGeAv4RNPh23z16tUsXrwYt3//hU2b0NOnWbx4cZZbrzTcHcHBwTg5OXHw4MG4vPnz53Px4kUuX76cZDs3NzeqVavGsmXLEuQvW7YswXp5Ujg7O1OkSBHc3NyYMWMGderUue3L2NXVlaJFi+Ls7MzMmTN57LHHcHKyPqp169bl0KFDxMTExNX/66+/8PLySrA807t3b3744QdWrlxJmTJlUpTrbqlTpw7Xrl1j06ZNcXmbNm3i+vXrST4XR55lcHAw/v7+CeqEh4ezbt26uDrVqlXD1dU1QZ0TJ06wb9++uDqpkc+QBEntkKdXAkKw9iLs01Rb+VQgzK7NA1h2GuHAaWAQKRy31VScnoqJidFJkybpnNatNcrVVWO8vDTK1VXntG6tkyZNijvlkl3JaaennnzySX344Yf1+vXrun//fvX19dXChQvrd999F1fnxIkTWrp0aZ07d25c3syZM9XV1VUnTZqke/fu1V69eqm3t3eCE1dvvfWWPvzww3HX586d0/Hjx+vevXt1x44d2qtXL/Xw8NAtW7bE1Tlw4IBOmzZN//rrL92yZYu2a9dO8+bNq0ePHo2rc/z4cfX19dWePXvq/v37dfHixVqkSBHt27dvXJ1XXnlFfX19dcWKFXr69Om4dPXq1SSfRVRUVIK6iaVLly4l+zybNWumFSpU0E2bNunGjRu1QoUKtx1pLV26dIJjxo48yxEjRqivr6/OmTNHd+/ere3atUv0yG3hwoV12bJl+vvvv2tISEiiR25Tki+13O17MT3Ilkdu0zulRmmsmD5dI8XJejS2FCmiK6ZPN0oji3G3H9SzZ8/qE088oXnz5tW8efPqxx9/rAsXLtTChQvroEGDVFX16NGjCuiUKVMStB03bpwGBgaqm5ubVq1aVdesWZOg/Pnnn09wZPXcuXNau3Zt9fb2Vi8vL33kkUd08+bNCdrs3btXK1eurJ6enporVy5t1apVosdBN23apHXq1FEPDw8NCgrSgQMHJjiCm8SPsrgxJUbsOJNLzz//fLLP88KFC9q+fXv19fVVX19fbd++vV68eDFBncTkSOlZxsTE6KBBg9Tf31/d3d21fv36unv37gR1bt68qT179tS8efOqp6enPvbYY3r8+PE7li+15CSlka293FavXl23b99+R23011/h8ceJvzWmAPPnIy1bpqV4WY577fSUzbNoZothMGTJ9+LdfJ5FJFRVqydWdi9shBsMBoMhi2CURjxUlZVXrhDtlPCxROPMyitXstwvCYPBYMhojNKw4+jNm8xv1YpoV1cixDqOGYkzm1ZHZLJkBoPBkPk4rDRE5HkRWSwie0XkiF06nJ5CZhQiQqlSpfDq1o0tM2dy5rvlHCYYV6L5/bt8nD5tjIAMBkPOxiGLcBEZCAzBcmm+E7iVjjJlKiEhIagqa9asoXhIPSYt+44/v93OvFstONUG1q2DJFz8GAwGQ7bH0ZlGF2CsqlZU1WdV9QX7lJ5CZjTx3Qp0nFCX+UG9icGJOls+ZdqT8zJRMsO9wMWLF+nYsSN+fn74+fnRsWNHLl26lGybq1ev8tprrxEYGIinpycPPvgg27ZtS1Dn7NmzdOrUicKFC+Pl5UWzZs0SGCeC9aNHRBKkp59+OtE+w8PDqVSpEiLCnZ4yTA1r1qyhWrVqeHh4UKJEiST9WMXHkWd5/PhxWrZsibe3N/nz56dXr15ERCRcTt69ezcNGjTA09OTIkWK8P7779+2R5ka+XIkSZ3FjZ+Aq8DDjtTNSimtXKMvX676LN/rv+TWf/HTheOPpvq+WZmcZqeRXqQmbkPbtm21TJkyumrVKj148KAOGjRIc+XKpSdOnFBVy1ahdu3a+uCDD+qWLVt0//792q1bNy1evLheu3Yt7j4NGjTQF154wSGjvB49euijjz6qgG7btu2Oxvj8888na/dhz5EjR9TLy0t79uype/fu1YkTJ6qLi4vOnj072XYpPcuoqCitUKGCNmjQQENDQ3Xp0qUaEBCgPXv2jKtz+fJlLVSokD711FO6e/dunT17tvr4+Ojo0aPvWr5YsuJ7MVON+4DfgNcdqZuVUlrG05hU+2tV0Ot46Fanmnpgt+PxDO4VsqPSSG3MjPj89ttvWrNmTfXw8NC8efPqY489pjdv3ky0bmriNty4cUOdnZ31l19+SZBftWpVHTBggKpa1uKA7ty5M648OjpaCxQooJMmTYrLa9Cggfbo0SPFMf3yyy9arly5OHnTW2m8+eabWqpUqQR5Xbp00dq1ayfZxpFnuXDhQhWRBIZ83333nbq7u+vly5dV1Qpu5evrqzdu3Iir88EHH2jhwoXjDHZTI198cpLScHR56jXgBRF5TkTyi4iTfUqrmU9WpdUvL7DF5UEicaNGzFY21e/PtWuZLZUhJV544QVWrlzJ8uXLmTFjBmPHjqVnz54JYmYkx+LFi2nVqhWNGzcmNDSUVatW0aBBgzjfT4MHD06wnJlS3IbEiIqKIjo6OtFYGuvXrweseBxAgjpOTk64u7vH1Yll5syZ5M+fn/Lly9O3b1+uXr2aoPzEiRN0796d6dOn4+npmeIzSAs2bdqUIN4FWO7it2/fTmRkZJJtUnqWmzZtomzZshQrVizBfW/dukVoaGhcnXr16iUYa9OmTTl16hRhYWGpli+n4uiX/V9ABWAKljvySLuU7c+jFijkxOn3JuDNdf6kPB0ufsaAJw9gTDeyLrExMwYNGkSNGjVo3Lgxbdu25dtvv6V169YO3eODDz7gySefZOjQoZQrV46KFSvSt29fvLy8AMtTa/wAPqmJ2+Dr60udOnUYOnQoJ0+eJDo6mu+//55NmzZx+vRpAMqUKUNgYCDvvPMO//77LxEREYwcOZITJ07E1QF49tlnmT59OqtWrWLgwIHMmTMngav36Oho2rdvzxtvvEHlypUdfpbDhg3Dx8cnLk2fPv22vHXr1iXZ/syZM4nGxIiKirrNO238Nik9y8Tua+89N6m+Y8tSK19OxdF4Gu+TSMCjnEardx9g1tev0+74aDrK93y/pDTFP4E33shsyQyJ4WjMjOTYsWMHnTp1SrK8Z8+e9OzZM0FeauI2fPfdd3Tu3DnOu23VqlV55pln+P333wHL8+2cOXPo0qUL+fLlw9nZmUaNGtG8efME9+nWrVvc3w888AAlSpSgVq1a/P7771StWpVhw4bh6upKnz59HBl+HC+//DJt27aNu+7fvz9FihShV69ecXlFihRJ9h6JxcRILD+5NrHtUoqTYZ/vSN+pkS8n4pDSUNXB6SzHPYEI1Ph1EK1rNOSXiEcBmPfmeqpVqEFIUys+Q0pfDoaMI7UxM+6G+HEbYt8HqinHbShZsiRr1qzh+vXrXLlyhYCAANq1a0dwcHBcnWrVqrFz504uX75MREQEBQoUoFatWlSvnqiLIACqV6+Os7MzBw8epGrVqqxYsYJ169bdFhq2du3atGvXjunTpyd6n7x585I3b964a19fX/LmzUupUqUcfi6JxcRwcXEhX758SbZJ6Vn6+/uzYcOGBO3sIwIm1TeQYp3k5Mup3PFehC0eRjER8U4PgbI6JSr6UPdDS2HUdNvGqpgGHG/1Cmd+tmJvLFmyxMTeyCKkNmZGfKpUqcKKFStSrmjjbuM2eHt7ExAQwMWLF1myZAmtWrW6rY6fnx8FChTg4MGDbN++PdE6sezevZvo6GgCAgIAmDJlCrt27WLnzp3s3LmThQsXAjB9+nRGjhzp8DjvlDp16twWDnfZsmVUr149ydjmjjzLOnXqsG/fPk6cOJHgvu7u7lSrVi2uzrp16wgPD09Qp3DhwnEBsFIjX44lqR1y+wQ0BbYDUUC07XUr0NjRe2R0SsvTU/GJjFTtHzRDz5NX1zg9qAp6Qzw1ys1N57RurYsWLbon3ahnx9NTjsTMSI4FCxaok5OTDhgwQPfs2aN//vmnfvLJJ3r9+nVVVf3888+1dOnSCdqkJq7E4sWLdeHChXrkyBFdunSpVqpUSWvWrKkRERFxdWbNmqUrV67Uw4cP6y+//KKBgYHapk2buPJDhw7pkCFDdNu2bXr06FFdsGCBlilTRqtUqZIgrkR8Yl2ip3R66urVqynG24jvnt2e2COtvXv31r179+qkSZPU1dU1wZHWuXPnaunSpeOOGTvyLGOP3DZs2FB///13XbZsmRYuXDjBkdtLly5poUKFtF27drp7926dM2eO+vr6JnrkNjn5ksOR92JGk9lHbpvalMR+rABILwGDgX1YG+FZUnGkl9JQVd2z4KjewEMjSRh7I8rNTWNOnUp1v5lJdlQajsTMSIl58+Zp1apV1c3NTfPly6ctW7aMO3I7aNCg2+RITVyJH3/8UUuUKKFubm7q7++vPXr0uM2+YuzYsVq0aFF1dXXV4sWL67vvvpvgi/r48eNav359zZs3r7q5uWnJkiW1V69eeuHChSTH5qjSiB1nciml98/q1au1SpUq6ubmpkFBQfrll18mKJ8yZYoCCYJOOfIsjx07pi1atFBPT0/Nmzev9uzZU8PDwxPU+eOPP7RevXrq7u6u/v7+Onjw4Nt+2KUkX3LkJKXhUDwNEdkEXAQeU9WYePlOWDYcuVU1y8VMTE08jVgc8UW//P4ONDqYcA34pqs3+z4dSdUePVLVb2Zi4mkYDKkjK74XMzueRiVgXHyFAWC7Hg9UTpVk9zCqSkz/psTY5TtFRrDuRIks9wYyGAyGtMBRpXELyJVEmS/Z2IFhcjh7OKG32TUKP43JzeFDRmncC5QvXz6BrYG9LYLBYEiIo3Yaq4EPRGSzqh6NzRSR4lh7G6vSXrSsjYiQ59IlYjw9cL5xIy4/ysmVxeGNmFqnH88dGUyupFStIUuwcOHCJC1+kzsiazDkVBxVGv2BDcABEdkMnAb8gdrAJVt5jqNKmzbE2BlJuRDFcp9W9LwwhI/q3ccbv7fH2TmTBDSkiCOuRAwGw384tDylqn8BFYHPAHegKuABjAUqq+rBZJpnS1SVJbt2Ma9FC6Ld3Ijy8kWBnTEPsOWVPqx3bkDvPzrzZfv1Kd7LYDAY7hUcNu5T1dOq2ldVa6nqfbbXN1X1dMqtsx8igoeHB17duuEUFobL8iWsqDOAWmwnYuIWbk6fyzECafdja34afSyzxTUYDIY0Idt7p01PQkJCaNq0KRIQAHXq8NDy91nn3ZTBl95k+y8n2D54ATN5ms7v+LPeTDgyhE6dOt0WhEhEqF27dlydoKCguHwvLy8qVKjAV199leA+ERERjBo1iipVquDt7U3evHmpXbs2X331VZzHWUfJysGH5s6dS5MmTShQoAC+vr7UqlWL+fPn39H4UoOqMnjwYAoXLoynpychISHs2bMnxXaOPMs5c+ZQrlw53N3dKVeuHD///PNtdcaPH09wcDAeHh5Uq1btNmeLqZUvR5CUAQewEigT7+/k0oqk7pOZKT2N+5Jix5Kzegp/3UsZXbP4hvboYdn9Bef+Vw/vj0j5BplIdjDue/7557VRo0a3WSvHN3ALDAzU9957T0+fPq0HDx7UAQMGKKAzZ85UVdVbt25pSEiI+vn56dixY/X333/XI0eO6I8//qi1atW6o+eU1YMP9erVS4cPH65btmzRgwcP6uDBg9XJyUnXrl3r8BhV9TajvJQYMWKE+vj46OzZs3X37t361FNPaUBAgF65ciXJNo48y40bN6qzs7MOHTpU9+7dq0OHDlVnZ2fdvHlzXJ2ZM2eqi4uLTpw4Uffu3as9e/ZUb29vPXbsWKrlS+y9mNlkuEU41omoWKWx2nadZErqPpmZMkNpqKp+02GFvsSXWrRIjF64oNqy4VU9wH36o19XvXQx67oXyS5Ko0WLFsm2CwwM1FGjRiXIu++++/Tpp59WVdWRI0eqiCRqJR0dHR0X3McRsnrwocSoUaOG9unTx+Exqt6Z0oiJiVF/f38dOnRoXN6NGzfUx8dHJ0yYkGQ7R55l27ZttVGjRgnqPPLII3H/W1XVmjVrateuXRPUKVWqlL711lupli8nKY0kl6dUtaGq7rf9HWK7TjKlxawnu9Dhm4fZVvVlTpwU+r0aznc/+7AyX1vaXv6amTVHExX1X101RoBZAg8Pj7ijt9OnT6dRo0aJeo91cnIil+0c9dSpUxGRuEA+iZHVgw8lxtWrV8mTJ0+S5XfL0aNHOXPmTILn4unpSf369ZMMVAWOPcuk6sTeNyIigtDQ0NvqNGnSJK5OauXLKTi0p2GL2Jeof2ARySsiz6WtWPc2rq7w/ffQwnUpH/xQgq3f/0XTLe/zs+v/ePFgf757eDK60XjFTS8WL158m6Fe//6JnwqPiopi6tSp7N69m0ceeQSAgwcPUrZs2RT78fPzo3Tp0sl6Qc3qwYfsGTduHCdOnKBjx45JjgluN4q0zytfvnySbWP7Tky2pORKbjzxn2VSdWLva+82PbE6qZUvp+ConcYUoA5wIZGyYFv5tLQSKjtQtiw88U453IfcolDvdnge2sjfH/TkyFs76LSuK5ENvXEmkhstWkC3bpYjMBOHI02oX78+EydOTJCXO3fuBNcDBgxg8ODB3Lp1Czc3N/r168dLL70EOD77a926tUMRALN68KFY5syZQ79+/Zg5c2aK9iv2RpH33XcfCxcujAvE5Ig78cRkS+kzkNpgSvZ5aVUnJ+Ko0kjuSXljecA12NH5vaIMnDuVD3c/zrwGb9Jz0ztEv3sCiQK3iOsAtFqwAKdx48ybMQ3x8vJKMThQnz596NKlC15eXgQEBCR4/vfffz/79u1LE1myevChWObMmUPHjh2ZNm0ajz/+eIrjSkypBAYGxsWnSA5/f3/A+kUff3ntn3/+SdYK35FnmVSd2Pvaz8YSq5Na+XIKSS5PiUhlEeksIp1tWS1jr+OlHsCHQI4z7nMEJyd4eUFLxru9RqvjX7D55W9x8fJIUEddPZBk1pcN6UO+fPkoVaoUhQsXvk1hP/vssyxfvpzEPCTHxMRw5coVh/vJ6sGHAGbNmkWHDh2YOnUqTz75pMNjSy3BwcH4+/uzbNmyuLzw8HDWrVuXbKAqR55lnTp1Etw3tk7sfd3c3KhWrVqydVIrX44hqR1yrLgZMbYUHe9v+3QOeDyp+2RmyqzTU/b89H24bqOafuncXSNd3TV+/I0IXPTwhqwRfyO7nJ5K7MjtP//8E1cnsdNT8QkPD9f69etr7ty5dezYsbpjxw49cuSIzpkzR+vUqRP3nBILGmRPVg8+NGPGDHVxcdExY8YkeUQ5Mf75559kAzLFf96JMWLECPX19dU5c+bo7t27tV27drcdae3YsaN27Njxjp7lhg0b1NnZWYcNG6b79u3TYcOGqYuLy21Hbl1dXXXSpEm6d+9e7dWrl3p7e2tYWNgdyRefxN6LmU1mHLn1AwKBIJtyeMJ2HT/5gxWTIyumrKI0VFW7PHNNQbVHrs+sQE25/DQSZ1XQ5/P8qP/8k/lHcbOL0iCRAEFFihSJq5OS0lC1FMeIESO0YsWK6uHhoblz59ZatWrphAkT4gIfJRY0KDGycvChBg0aJPq8GjRokOyYAgMDkw3IFBgYmGz7mJgYHTRokPr7+6u7u7vWr19fd+/enaBOgwYNbpPDkUBJP/30k5YuXVpdXV21TJkyOmfOnNvqjBs3TgMDA9XNzU2rVq2qa9asuWP54pOTlIajQZgCgdOqGpFi5SxEegdhuhOuXIGSJa9S5PxhRlf7gUbdSnH1vkqENe1G0cjjvFDpd2ZsCibe6cgMxwRhMhhSR1Z8L2ZqECZVPXavKYyshq+v8s47e/iYPjwSOoqoXq/j1aw+R+sUx0mUgbuepP3/womOzmxJDQaDIWkc9j0lIt1EZIeI3BCRaPt0J52KyCsiclREwkUkVETqpVC/rYjstPV9TET63Ul/WQER4bWnA6nnvBEBXG7dwDkighabl3L1g08pKOf5c9Fxune3NjsMBoMhK+KwcR/wObANyyX6FOB74ApwGHjf0Q5FpB2WS/VhQBVgI7DIFtApsfrNgR+AiUAF4BXgdRHp6WifWQUJC8PV2z1BnpOHB0UfLsPfy/Zz3P1+Jk2CDz7IJAENBoMhBRydabwGDAe6267Hq+rzQAngJokb/SVFH2Cqqk5S1X2q+ipWUKfuSdTvCPyqquNV9YiqLrDJ0l/uMeMGDQoiJt7xR4DoqzfQoCAefMSTGdMiGcFbzBi0j0mTMklIg8FgSAZHlcZ9wFr+O2brBqCqF7HsNHo7chMRcQOqAUvtipYCSR2AdgfC7fJuAkWxTnDdE6gmDNoU4eXHLdx4Ub/is1lHUVVa1zvPqz7fMJsn6fPSdRLx6GwwGAyZiqMW4TcBJ1VVETmDNcPYbCu7BhR28D75AWfgrF3+WaBREm2WAGNFpAmwHCgFvGErCwDC4lcWkW5AN7CsXlPr1+natWtp7hMqKioKl06d2NKhAx6nTzNuYR2mLqxK+Q+PUj3iSyJrlCPP4P480Lcf4/Vl2rWdyqjRf1Cp0uU0lSMp0mPMBkNOIat9dtLt85zUWdz4CVgBvGz7ewbwJ5YvqhrAViDUwfsUxjrHXc8ufxCwP4k2AozEUlxRwL+2+grUTK6/rGSnEUv8M/Lh4aqVK8fofB7Ta65+GrP/gFVn8BBV0Bf5Sn19VXfuTBdRbuNes9NIyVbAJJMyKqVkl5IZZLhrdDsmArG+kgcCPsB6rNnG/fz3yz8lzmNZl/vb5Rfk9tkHQKzFTH9bn4G2tlttxWEO9ptliL8N4+4OP/0kvOX1OTciXbnU4HG4fBkZ+C7auAmfuPaHq1do3BgOHcpEobMoYWFhd20AumrVqkw3Qs3oZMac9ik5V/PZDUftNH5U1eG2vw8B5YGmQGuglKqudvA+EUAo0NiuqDHWKark2kar6knbPZ4BNqnqP470m5UpVQoGTg7iSWbjc/Ywl1s8C6rI9O9x27CaGg/n4tw5eOQROHnSaqOqmSu0wWDIsaQqRriqXlfV5ao6X1UTDwqQNJ8AnUSkq4iUFZGxWMtWEwBEZLiIrIitLCL5RaS7rW5lW/2nsE50ZQuefhrKvdyAV/kcvw0Lufn+KChQALcalejbdx3PF53HreOn6Vt3Exf2mBgcBoMh80hyIzwpu4mkUNXjDtb70RbQ6V2sjew/gUdV9ZitSgBQ0q7Zc8AorP2NTUCIqm4lG/Hpp1Bn88v03BnFxS3P8L0CKPl3rGPqiQFE4sL1Y164PxDB5Ucfg55dUDX+/Q0GQ8aS3OmpMKxNHkdxdrSiqo4HxidR1snu+jzWpnu2xsMDZs+GKlV6cnUJVB0RwRtPHad6p07owIG4xkSRmyug0Grhb8R89plRGAaDIcNJTml05s6UhuEuKVkSvv0W2rSBgAEvcOuT1bh/MwH18bE8HtoIV3feaxfGqPUBuLsnc0ODwWBIY5JUGqo6NQPlMNho3Rr69oWRo9/kifO/EDFgCM43byaYxnkQzqztwRxra81OHIisaTAYDGlCqjbCDenL8OGQu34lOjINt92h/O0fQLSLC+rlRYyzMz80fJornnmYPx/at8d4xjUYDBmGQxbhIvJNClVUVbukgTwGwMUFfvwRqlRpw+Azgxj89xBinJ1BBHV2JnfuK3z52FBcfz5Op58m0dnTjSlTrPCyBoPBkJ446kbkYW7f38gL+AKXbMmQhvj7w+zZwtP1X2RAzIe4RkfB9es4A60WLMCpfwUkahqFnE7RetocPDxyMWECmL1xg8GQnjhq3BekqsF2yQ8IAc4A/0tPIXMqdevCp68d5zpeCfKdPD2R5s1hyhQasoo1EsK8iWfo2ROM3Z/BYEhP7mpBQ1XXAp9ixdowpANt3gjCwylh0MSYG5Y7dTp1wum3X3nA7QCbeJAfx5/ntdeM4jAYDOlHWqyCH8EKpmRIY1SVpX/s4tcWLbgpHlzBFwXCnV1YvXy55U6keXNc1q4i+n9tueKSj88+g759NYHiMG5HDAZDWnFXSkNEXIBOwIk0kcaQABHBw8MD31e6cn7LEdr5LaExS5EoZ2oOHIhcvGhVrFmTUrNH8NNsoZzTXv78ZCmDXz6NbtyEnjZuRwwGQ9rh6OmplYlku2F5uM0HvJyWQhn+IyQkJM5dyDu/BvDww9Aiaj7LTzazjDpWr47b/X78ceX++17hvgNriZ7oQvhUT9wI50aLFtCtm3E7YjAY7hpHZxpOWH6f4qerwFzgEVU1wUnTkdgv+nr14PPPYTUNeZYZHGjZN8FxKRGhzPyJOAm4E4lnxBWcIyJotWABTStVMgrDYDDcNQ7NNFQ1JJ3lMDjIyy/Dzp3w1VdtWDMaQp+BIqe3Q7VqIIJcuAC+vgncjkSoBx5HwyAgINPkNhgM2QNjDnYP8tln1qzj7Fno+/DvaK1a0L8/ABoUREx4wpDqGhlJv/FBxMRkhrQGgyE74bDSEJH7RORbEflLRK7bXqeKSKn0FNBwO25u8PPPEBQEM/+qwpLAl2DUKHT0aJbs2sW8Fi2IdnND/fyIdHGnu4xnynQ3vmj8i3E5YjAY7gqHlIaIhAC7gMewQryOt722BHaLSIN0ks+QBPnywYIF4OsrtDj6OX+WeRLp14/ANWvwfPFFnMLCkEWLcDl+lNJDy/Gu04f0WtmahRXeJCo8KrPFNxgM9yiOzjQ+BnYAgar6nKr2U9XngCBgp63ckMGUK2f5qMLJmWr7v+d0uYcp89FH+B47lqBe1ar/4jKyHZNcutNy/yj2FmtKxMlzmSO0wWC4p3FUaZQDRqrqtfiZqnoVGIkVM9yQCTRvbkX9i8CdBw7OZWmbN/lnyRJigoLQZs2ICQrixsSJ3FfhIg+sG0cPz2+4//wGLpWqRvj2PzNbfIPBcI/hqNI4gWWXkRhuwMm0EceQGnr1gh494EKkH72X9eLxBYtwjohAriQ8clu7ttB1wws8mmsDB8IDaduzIFeu3G4xbizIDQZDUjiqNEYCQ0SkSPxM2/UgYFhaC2a4M8aMgWbNIM/lMG5EJQzn5+TpiYSFAVClCnyxqRptC63l1y0FqVP9Ars6vYKGhZFrzx5jQW4wGJLFUaXRAMsN+mERWS0iP4rIauAw4AOEiMg0W/o2nWQ1JIOLC8yaBe6lg3DRyARlMTdvWg4ObZQrBxs3CUFBStDBzVSeNgFKlKBiv35xy1nh4eFmxmEwGG7DUaXxEBANnAYCgZq219NADFDPLhkyAR8fpcuA/fR2H8MNPIl0slYUt1WuzJJduxIogeBg2LBBuFyyKrdwQ1RxuXnTWJAbDIZkcdQiPDi9BTHcPSJCsWJC1U/L8UDfwxS+cZCZ+V+l6r59hF69epsSKFwYFnwRRkQLD9xj/nO/7uTqai1nGQtyg8Fgh6OR+wz3CCEhITRooJQsKbRoEUDD8z/xY70vqNOiRaL1c1UKItolHOKF7Ii+FYFzUBBmnmEwGOy5E4twLxHpKSI/icgKEZklIq+IiFfKrQ0ZiYjQpAlMmgQHuZ9q6z/jpwVe2PsRUVWW7NrFfJsF+TVnX27gSZfoibw3/jS6d28mjcBgMGRVHLUI9wd+Bz4DqgNeQA3gCyBURAqlm4SGVNOpE3z4oRXJ7/32B7he8gHYujWuPDZeh1e3bjiFhbHv0+EM6nCYadqJsA/3ohUegG/NuQaDwfAfjs40PgLyAPVs8cHr2PY5HgJyYx3JNWRB3n4bXnkFTkQW5MKxa9x68lm4ejWuPCQkhKZNmyIBAVx/oDwfTQvgvfeUn/UJlusj0KkT+umYzBuAwWDIUjiqNJoDb6vqhviZqroReBdIfMHckOmIWF5xG7bOQ3v9Hpe/j3L1hVft6kiC+kOGCKO/9KGV/Mps/of0eZ2Yd98zwccNBoPDSsMHOJVE2QlbuSGL4uwMP/wAzg3qMZR38Z3zLZcnzEi2zcsvw8yf3enkPpPJdIYPh3Jj7fYMkthgMGRVHFUaB4COSZR1APanjTiG9MLDA+bPh/kVB7KROvzx5ndcvpT8zKFVK1i2yoV+ub+mHut46PUanDmTQQIbDIYsiaNKYzTwjIgsF5HOItJcRF4QkSXAs8Co9BPRkFbkygULl7rQK3A+Da/O59EWwo0bybepUwc2bxFOBdVlxw54o+IyrjR9Cm7ezBihDQZDlsIhpaGq3wMvAxWAr4EFwGSgIvCyqv6QbhIa0pRChWD26vwUDHDhr43nGFPnRyIikm9z//2wZQtUrw5e58LwWTqHi3WaJwgpazAYcgYO22mo6kSgMJYb9Hq21yKqOimdZDOkE0FBsGIFDPUYSv8/nuX9pusTRPRLzOdUwYKwZg388/iLtGc6Prs2cL7iw3DOxOUwGHISySoNEekkIjtF5JqInMBahjqsqhtUdZ+qmqjT9yhly0LNJUM5JkG8uLo97/xvPz67k/dy6+UFc+dCod7P8AS/4H1sD/+UrU/MuQsZPwCDwZApJKk0ROQZ4BssQ74FWM4JX8e4Qc82VK7nw8aeAyjC33w4rwJl+7ydopdbZ2fLDfujX7SgudNSZl5oTNuX86a4N2IwGLIHyc00XgN+BsqqajtVrQEMAXqIiPPddGpzP3JURMJFJFREkvWMKyJNRWSTiFwVkfMiMk9E7r8bGQyWfUb7t5sjTk64EI131FWHvdz26AH9f6vHAJ/PmDNXeKbWEc4t3ZGB0hsMhswgOaVxPzBJVeOtdjMecAeKp7ZDEWkHjMWasVQBNgKLRCTRe4pIMDAPWGer3wjwBBamVgbDf0hYGE4+3gnyIuW/oE3J0bw5bN4MxYpB7z+74t4shL8mr0snSQ0GQ1YgOaXhB/xrlxd7necu+uwDTFXVSbZ9kVexlr66J1G/GuCKZZF+SFV3AsOBkiKS/y7kMAAaFERMeHjCzFvhjJ4T5FD78uUhNBTGVvmWk1qYYl2bsO5to88NhuxKSqennEQkLgHOieXbylJERNywlMBSu6KlwINJNNsORAJdRcRZRHyB54FtqnrekX4NiRPr5XaezcttlLc30eKMC5Gs/XgbH37omNuQAgVg1qZijGu7lj2Up/aIVvz0vxn2TnUNBkM2IKUv+w1YX9ixKdaia4tdfgon/ePIj6V4ztrlnwX8E2ugqmFAY6z9lFvAZeAB4DEH+zQkgb2X2z9GjsRp3x5O+ZdjNk+y5d1fGT7csXu5u8PnMwuw4f2VbKAuReZ+Ttv/RXPtWvqOwWAwZCySVBxoERl0JzdS1SEpdiZSGDgJ1FfVdfHyBwHPqGqZRNr4A2uBX4AZWLHK37cVP2x/7FdEugHdAAoVKlRt5syZdzKMOK5du4aPT85yqRU7Zudr1wjsNgD/0/t4ktn4v1iBZ5897vB9Qtd78enwYE7eKMD9Qf8yaOgBChe5lY6Sp56c/H/OSZgx3xkNGzYMVdXqiRaqaoYlwA2IAp6yyx8HrEmizQfADru8ooACDyXXX7Vq1TS1rFq1KtVt71USjPnSJT0bXFNPUFg9uKFDh97ZvfbvVy1X6pYupZF+5t5Xly6JSVNZ04oc/3/OIZgx3xnAdk3ie9Vhi/C0QFUjgFCs5ab4NMY6RZUYXkC0XV7sdYbKn6Pw86PgjqVs+WAZt8STd9+F999PuVkspUvDxq0u3Awsy6u3RnO86Yt8NDzaeFc3GO5xMuNL9xOgk4h0FZGyIjIWyz3JBAARGS4iK+LVXwBUFZFBInKfiFQFpgB/YykgQ3rh50ebd8vxzWRlMIMJHTSPAQMcD6vhl8eJxw6PZcVDg+jCZEq8045n/3fL7HMYDPcwGa40VPVHLMPBd4GdWNH/HlXVY7YqAUDJePVXYnnSbQXsAJZgbb43U9XrGSZ4DqbTM7foUXIRs3mSP4fN4803HVccTs7CI+sG80fnMTzJHB79uSs1a8KBA+krs8FgSB8yZXlHVcerapCquqtqNVVdG6+sk6oG2dWfqapVVdVHVQuoaktV3ZvhgudUPDzIH7qUq6WqMpsn+Wv0PHr25I6O1Fac3JtTI79jVuCb7NtnecydOzf9RDYYDOmD2RMwOIafH3m3L+XafZbiODF+Hi+8QALvuClR+M0O/LD7Adq0Vl689gk9/3eKN95QIiP/q6Nm08NgyNIYpWFwHD8/8mxbyvVyNcnjep1p06BdO1KMxxEfX1+Y/fExRrgPYh31+PmTo7R58DTn5m9K1sOuwWDIGjisNESkiIh8IiLbReSIiFSw5b8mIrXST0RDlsLPj9y71/Hiqmfx9YXVc87z+OPckZdbCQ7Cdc1yCnudJ5Qq/LQ9GPcnmhFVPDhZD7sGgyHzcdT9R3lgN1ac8FNAIJbNBba/e6eLdIasiZMTdevCtlGrCSMI9yXzaNwYLl1y/BZSqxYeC+eTmyt4cItcegXXqFu0mL+IRhWS97BrMBgyD0dnGh8D+4BgoA0Q/xO9EaidxnIZ7gFKP10F54oVmM2T5N84j/r14cwZx9uLm5u1XhWPm9Hu9H487I7uYzAYMg5HlcZDwAhVvYZliR2fJP1GGbI5fn54rl1CTJVqzOZJgnfP48EH4fBhx5prUBAxtxK6F/HiBpd3HKZiRVi2LB1kNhgMd4WjSiO5w5X5+c+RoSGn4eeH+6olUNVSHLmO7qROHdiRQjwmtfOwq35+RLu5cdXXm+/pyIhznWnb5BJvvUWC01UGgyFzcVRpbAVeSKKsLZY3XENOxc8P15VLiB72EXlDKnHuHNSvDytWJN3E3sOuLFqEU1gY27//lqPPtqeTfMtuKrBz5GLq1wcHYkIZDIYMwFGl8QHQUkSWYm2GK9BIRL4FWgMfppN8hnsFPz883n6dxUuEns0P0eDabzRvDtOnJ90kJCSEpk2bIgEBUKcOEhBAk5YtCZ7+PU5bNpMnyI/feIzTm8OoWBFS6bDYYDCkIQ4pDVVdAzyBtRH+DdZG+AigHvCEqm5JLwEN9xZubvCZ19vMc2pNi8if6dABRoxI2u2I/SmpuOsaNfDeF8qNWQt44LEgrl6FQc8coFMnuHo1fcdgMBiSxmE7DVVdoKr3YcUOfwgoq6olVHVRuklnuCeRbybjXLM6s53a0pqfeftt6NEDoqLu8EYeHuR6qinz58PPvVezj7LU/vZl6la8yubN6SK6wWBIAUftNN6zBVBCrTjdG1X1gK0sQETeS08hDfcYuXLBkiWW4nBuy1MuP/Pll/D446TKw60IPDG8Fv++8AbdmMj8sAcYWHclgwenQhEZDIa7wtGZxiCswEeJUdhWbjD8h01xONWozqTyY8jtpyxaBA89BKdOpeJ+np7k/2YUUavW45XbjWUxj+A5pD9168Jff6W59AaDIQkcVRrJmefmwYrdbTAkxKY4/Nb+ytZtQnCQsmsX1KgBO3em7pZuIQ9S8OROjv3vdf7NFczWrVClCowf77i7doPBkHpckioQkRDg4XhZL4nIY3bVPIEWwJ40l8yQPciVC4D7nK+zP6gN77m9xMi/2lC3LsyYYS1Z3TFeXgTO/oS3L8Gxl8H9x2+J6hFKqznD+XKaN0WKpOkIDAZDPJJUGkADrEBJYB2xTcxOIwLYC/RKY7kM2Y3oaNzCrzD8SDsKPfQjfda34YknYORI6NvX2re4U3Lnto7h7rl1kPK/fM6jKxfStfQU2k+oR/v2qbunwWBIniSXp1R1iKo6qaoT1vJU7djreMnDFhxpU8aJbLgnsS1VSfXqvLa5HbOenosqvPkmdOoEt+5igbP8z0O5MGc1Xp7KgusNONfxddq1vMHZs2kmvcFgsOGonYaTqm5Nb2EM2Zx4iuOp2e1Y13ceHh7KtGkQEgJnz6Y+CFO+Ng0IOLuLfSGv8DpjuLJgLWXLWktgZq/DYEg77jgIk4gUFJHi9ik9hDNkQ2yKg8cew638RUaN2kilQqdh8yaaVT7N559vSnUQJvH1ofyqLzi1bA+3Qppx8SJMfnY5T7e6yenTaTsMgyGn4qidhpOIDBORC8Bp4GgiyWBwjFy50Llz+dffn8IrRhP6byDLnJux4UxJQl87wMKFee4qCFPhRuVYuRKmDjvFAlow5NcqdLxvM99+a2YdBsPd4uhM4zWgB1ZcDQGGAUOxlMVh4MX0EM6QfRERmlaqxBO//opzZCQ+0Vfw4iZfag+mjSpEnz5yV4Z7IvD824W5PH0BeTxusuR6Xc506k/LxuGEhd2+DGYiBRoMjuGo0ngBeB8Yabv+WVUHAWWBk4BZnjLcMRIWhnh7J8hzcXPiPuejjBkDjzwC//xzd30UfLYRBc/s5lD9LvTnI4atqEnFMjfp3n0/USdOk2vPHhOb3GC4AxxVGiWA7aoaDURh2WegqpHAGKBzukhnyNZoUBAx4eEJ8lwjrvNdxyUULAhr11qGe1vu0h2m+OWi9JqJ/PvDYkIrPM/VW55c/2o7UcVLUKFff2KCgkxscoPBQRxVGpcBD9vfp4DS8cpcgLxpKZQh+5NUEKawwEDOlvVixw6lVk3l1CmoVw/Gjbv7/Yi8zzTlhd1v8NvXp/marnhoOG43r+McEcHjCxbQtJKJTW4wpISjSmMHUM729xJgiIg8IyJPAcOB39NDOEP2JakgTAcmTOBmzZoULixseLAf68t0xSvyEj17Qvv2cP363ffdolwYbl4J7VrDI93ZMjPs7m9uMGRzkrMIj88YrCUqsJwTVgViw+scA3qmrViGnEBISAiqav26DwhAgKb+/ta1Ks6ebtQ9OJXTfgvofHMcM2a0YedOmDMHypZNfb8aFERMVBTO8fK89Srr+8zmk011GDMGChe+u7EZDNkVR437lqnqV7a/zwA1seJqVAbuV9U/0k1CQ7YmySBMIjBsGGzdimewPzMi/sdin//x774zVK8O06alrj/7ZbEob2+iXd3YkacSR11K8dNPUKF0JONGXDVu1w2GRHDUTuM5EckXe60Wh2zKwldEnks3CQ05m6pVYetWGD6cJs4rePbRy9y4Ac8/D5073/lylf2y2B8jR+J0LIx/fhjBQ9Mq07w5PHXtG556uySjin/OpjUR6TMug+EexdE9jSlAySTKgm3lBkP64OoKb72F/P03H/9WmokTYaDzMNZOOUT16rB7953dLn5s8ivlyyMBATRt2pRnnqnDwoXw9OjqHHSvwNune1EopAxf1vuBs6dj0mdsBsM9RlrE0/DGOoZrMKQvvr6IwIvN/maQ50h2S0Va7B9NnRpRfPnlnZ2uSnJZDGj4RjWqXFjB5KcWc0X86L6+PWuKd2TsWIiMTKvBGAz3JkkqDRGpLCKdRSTWBqNl7HW81AP4EDiYIdIaDADFiuG8fy+ujzZmNP1Yfas2E17ZRevWcOFC2nTh5S10mdUU732hDK8wnS+juvLaa/BQhUtsHbctbToxGO5BkptptAK+tiUFBsS7jk2fA2WAd9JXTIPBjiJFcPn1F/jxR8rn+psl0oxF827xwAOwcmXadXNfaSfe+uNZev/ckOLFodlfY6nZsyYbirbl+ArzW8mQ80hOaYzB2q8ogbU81cZ2HT8VBgqq6vz0FdNgSAQRaNsWzyN7iflxNpVrunP2dDT9H9lOv353F6PDvpsnnoADB8DrndcZ5vIelU4uJKBROTZVfYVrh86kTUcGwz1AckGYLqvqMVUNw1IQC2zX8dMZNX4XDJlNvnwUfqouGzbA3Ecns40aBI9+hYbVrvDnn2nXjYcH9P8wF88dHcIbrQ7zFS9RfcckVlfowddfQ3R02vVlMGRVHLXTOGbzM4WIuIvIKyLyhYi8IyLGDMqQJXBxgVaz2nOq3eu8xFf8uKc8A6v8xscfQ0waHn4qWhS++qUQ1TZ+wVPl9/HarZG8+CK0LHeYA6+MSbspjsGQBUluI/x9Edljl+cObMHay3gFyz36DhEJvpNObUrnqIiEi0ioiNRLpu5gEdEkUsE76deQA/D2pvDMT7i1ahOSOzc/R7Uksu9bNGwIYWFp21WdOvDz7lIM/q4URYpApb9mUfrL1zmbuzQnhn9nph6GbElyM41GwEK7vB5ARWAU4AfUBiKBdx3tUETaAWOxYnJUATYCi5KJ/jcaCLBLa4DVqnqXjrMN2RWvkJoUPRvKvmfeZ5Nfc9auhcrlI5k0UVFNu3gaItChAxw8CF7vv01Lj2X8HZ6fou88x4kCVbjw/aK0GI7BkGVITmmUBOzjgj+BFbnvbVW9aosbPgp45A767ANMVdVJqrpPVV+13bN7YpVV9Zpt7+SMzYWJK1APmHQHfRpyIm5ulP1hIJMPNeCJJ+C9G/0p9lJzHq+0i+++W4OeTrt4Gp6eMHAgTD7WiEldt/KMzCT84g1+6rSAAQPgypX/6poAUIZ7meSUhh9wNvZCRNywfE6tstv83oX16z9FbPeoBiy1K1oKPOjIPYAuwCVgjoP1DTmc/Plh7lyo16kUddnAjN11ce40kchiwVTsn7bxNAoWhK8mOTFobzv6PbqXftHDGTYMni22joOV/sf6r6exZMkS9PRp2LTJBIAy3HNIUh8SETkMDFHVabbreljLQj1VdXy8eo2BH1U1xZgatk3zk0ADVV0bL/89oL2qlk6ysVXPCQgD5qjq60nU6QZ0AyhUqFC1mTNnpiRWoly7dg0fH59Utb1XyQljvnngXwr0/5Tql9cnyI92c2PLjBlE5E3b0DB79uRiwoQSVPlzPuPogSc3ORRQifvO70bd3JDISLZ26cK5Zs3IlStXmvadFDnh/2yPGfOd0bBhw1BVrZ5ooaommoCpWHEycmPZafyE5S4k0K7em8AfSd3Hrm5hLEPBenb5g4D9DrR/zNa+vCP9VatWTVPLqlWrUt32XiWnjDlm/QaNdPO0tjZsKdzDT6PWbUyf/mJUf/lFtU7JszqRLhoTr18FjXJz05hTp9Kl78TIKf/n+Jgx3xlYkVoT/V5NbnlqCBCEtUR1CfgfMFFVj9nV6wSsxzHOA9GAv11+QeIthSXDi8BGVd2TYk2DISlKBCMkPNnkHH6NcU+vZtfvaX/iSQRatYJ1BwoS8E4XrkvCX38xLh4w6WuIMB51DVmf5Iz7jmLFyxgJTAOeV9VX4texLTetwEEvt6oaAYQCje2KGmOdokoSW18tMBvghrtAE4mnEeniziEpQa+T70C1qnz1zGpu3Ej7vp2doUXPIDxdEyqHmBvhyKD3uFWoGPruQDhxIu07NxjSiGSN+1T1uKq+p6qvqup3iZSfspXdiQe3T4BOItJVRMqKyFisZasJACIyXERWJNKuM3AdmHUHfRkMCUgsnobL8aPs/ekLRtaYgB+XeWlmQ1YXeIoV09PWPYi9wtJcfkQ4u9PD9XOasYill2qiH35ITGAQ/O9/cPlymvZvMKQFjrpGTzNU9UfgNSzbjp3AQ8Cj8Za9ArCL3SGW3+ouwHRVTYffgIacRGLxNFq3aUz/rS9xdtU+Pi/wPuVvbOXJDu489RScPJk2/d4WF33xIlz/PsqjM4Mp/ML9dPT7lZIcZlTMG+xafo5Vobksd+/r1iU8s2swZCZJbXZkh2Q2wu8MM2aLiAjVkR/cUk9PVSeidKHzY7qg/XSNuBWTJn3GxMQken35surAgaq5cqlCjILqw3VuaIS3n8b4+Kh2767655933b/5P+cMMmMj3GDIkbi6wpvvurFvH7Rv9A8Fo0/x6PT27M5Tj9CJoXd9/6QCQOXKBe+/D8ePw7vvCn5+sHKTJ3WuL2eBx5NEf/0NVKgAISFWCFyDIRMwSsNgSILAQJi2LICTc7fSL8/XFLlxkCov1WBlya6c2p9+y0V+fvDBB5byGDwYjuSpTsvzUygUeYIxASO5vvcY0Wr76B4/DqdOpZssBoM9RmkYDCnweGtn3j/Zhclv/sVY5z7kO7KNctU8GTYMwsPTr99cuWDQIEsvDB8OTgXy8/rpN/E9d5hyz1VnyhSIfm+wpd3atoU1a+4s5q3BkAqM0jAYHMDTE94Z6ccTB0cz6NHtXL7hyvABV9mZJ4RNAxem63e1jw+89ZalPMaMgSJFnfjrL+jcGRosfofQur3QZcusZauKFeHbb9NPGEOOJznX6CvvICV2RNZgyHYEB8MvC1xZsgTqBf5NnvDT1Bnagi35W7DvlwPp2reHB/TuDUeOwOTJcN99sOFsKaqv+ZiinOTnFl8TKa7w++9WA1X466+49mocJRrSgORmGk5Y7kNiUxkgBMtK3NP2GgKUtpUbDDmGJk1g/qFyLP5oNwM8Pqbsv+sp1boCSx7oy+njkenat6urNcvYvx/mzIHq1eHUJS/aLOiC74FQXrn2kaUrVq+G0qXhkUf48/33WbpwYZp69jXkTJKzCA9R1Yaq2hAr/kUkUFtVS6hqHVUtAdSx5Y/NGHENhqyDiwv07ufGGyf7MOKFv5gmndA//6RUGRcGD4Zr19K3fycnaNMGtm2z9EPTpnArQvjyG3fKlIH2Ix7g6IvD0EOHqDBoEPWefBItVoyKb76Zpp59DTkLR/c0PgAGqhU/Iw5V3QIMxorgZzDkSPLmheHfFOKhfZMY3/w3btwUvhlynD/z1mNev/VEpu/EA4AGDWDxYtizB557zpqN/LA0PyUmvU3NfEfY+OI3eN66hVN0NC43buAcEUGrBQtoWqnSbUeADYbkcFRp3AecS6LsH6BU2ohjMNy7lC4N8xe6sHo1NLrvOEUiw2g1uh6L8z7Lool/x22Wp+feQrly1j748ePW5nnevLB9hzNvTCrDVXwT1HWKjESaN7fWuKKi0kwGQ/bGUaVxFHgpibKXsGJcGAwGrF/9kw88xMbJ+xnrN5BG136m/ktlmFDsQz75eEeGBGEqVMg6pnviBIwbB04lgnDRhFOeGCcn9MIFePJJKFUKRo+GS5fSVA5D9sNRpTEEaCkif4rIYBHpbnv9E8vz7OB0k9BguAcRgXadven+z/vMGLiP5W4tcDp5nDf6VmHZc38SFRiMNmuW7nsLnp7Qvbvy7he7+LZOR8LFg0v4cQNPOkVPoVKu39nwxhzLSWK/frBhg9XQ7HMYksAhpaGqM4GmwGXgbWCc7fUS0FQtJ4QGg8EONzfo/H4Qj1yYxdmB4yjhdZoPz72La+Qt5MqVDNlbEBE8PT0Ifq817iePsPm90bzX/jBzPJ9h994CPPRxG4odWs2E7rs4Xbm51ejdd+HRR2HJEqNADAlw2LhPVZeral2s47b+gKeqPqSqxkbDYEgBHx94730XdswNw8nFOUFZdJQTx379I137j+/Z16NhKUZ/H8CZM8588gmULGl5Iun+ZUWKBznRrh0cvFwQ3bEDmjWD8uVhwgS4fj1dZTTcG9yxRbiqxqjqP6oakx4CGQzZGd+KQTg7Jdx0dosJx+mlrjzTLoa9e9Ovb/uZTK5cwuuvw8GD1smrRx+1JhWzZsH943pT0TeMxc9OI8rNC7p3h5490084wz2Dw0pDREqIyFsiMl5EvrFLk9NTSIMhO6D2QZj8/Ihyc+fLwt143WksM2c5UbF8NJuKPcXfI6bDrVsZIpeIZeOxYAEcPQr9+0PBgvDnQXea/9AR3/3b+KDpen5/uK+1UrVnD7RrBxs3mqWrHIhDSkNEWgH7sewxngAaJpIMBkMy3BaEadEinMOOEjy5NW1/CKBLFwh2PUG+E7so9nYHLuUqxukX3rHOz2YQxYrBiBHWqauZM6F+fQi/Jby3pC7VnitP+fIwf9QBYpYshbp1oWZNmD7dxDfPQTg60xgKrAYCVLWwqgbbpRLpJ6LBkH2Iv7dAnTpIQABNmzalXbs6fP01rDoSyJiX9vOoy1JWRdSl4NSRRAcGs2PClgz9Ue/qak0m1qyxlq/69IH8+WHfPmj1bRvyXj/BpKrjuXbmKnToYO17GFuPHIGjSqMEMFpVkzLwMxgMDpJUECaAokVh/AQnJh9vzJreP1Pe8yjvMpTq3atTty7sfukLYj75FC5ezDB5S5WCjz+2NstnzYJHHoGrMd50+707uU7spWP+Rcwv+TqHwlys5arBg2HXrgT3MM4Ssw+OKo39QL70FMRgMPxHQIDlBn3jieLI22+TK7czmzbBoYkrcHqjD1H+RYju3BV27MgwmVxd4amnYPlya+/j3XehWHEnvj/fjFZLXuG++6BtzTAih4+CypWhYUOYN4/VK1ZkiEGjIWNwVGm8CbwjImYZymDIQPLmhWHD4O+/4aOPoEfAz1RmB5MjOnJr6gyoWpXwNwZkuFzFi1vRBY8ehWXLLGXi6Qk/bQ+mYMQJ3nH5iPNbj8ATT1CjQwdcRo0iJigoQwwaDemLo0pjMNZMY5/NKnytXVqTfiIaDAYfH8tgOywMek2uzKelv6KwnqQXY2n+5eO89hqcWL4f3n7bqpRBODlBo0bWstWZMzB+PJSulYfhUf3wv3GYJ/mJ7f9WoN7qDThHRGSYQaMh/XBUaUQDB4CNWI4Lo+2SsdkwGDIANzcrlsa+ffD9r7nZ8VAvVt+sxdix8EGTdUSP+AgtUQJ9/HHL+CLmv49meu8r5MplmXNs3gyHDsGbb7sQGvQkb0W8z80Y9wR1nSIjkb594Y/0NWo0pD2OuhGJi62RVEpvQQ0Gw3+IwGOPwbp1VqC+tm1hisuLBHOUD/Ud/l20BZo3J6ZyFYiJYfXq1XH7ChkRhKlkSWtZ7cgRGPNLEJ5OCY/kRqsQ9cOPUKkSN7sYo8F7CRMj3GC4x6lSBX78EY4dg2f7F2dMvqEUjjrOM/zAkMMdeedd4fhxKP7668QEBlKxf/8M3FdQLrrvYkGr5kS7uRHh5Ue4kwdd5Gv8OUN3xtNqamuaNoVZo48TWa+hFcvWeNvNsrg4UklE6qdUR1XX3r04BoMhtQQEWIZ5gwfD99+7M2bMM8zcAwyH6nizlf1WXGZbVKhWCxbgNG5cuu4rxBo00q0bTuPG4RwWhgYF0WrTnwRs/Ydt27qzdi1ELYVrS/+mCie5b31Xorv3IObRlri+0AGaN7fW5QxZAoeUBpZhX0o/R5xTKDcYDBmAhwd07QpdusDatTB2LJybH8WV6Fz4cSWunlNEBDJnTrr7lAoJCUFVLeUUEIAArVv706aNpazOn7esz2fMqEuZTQeoptvoEPk9T8+bSb75PzP381M0bl+Q3NEXrONkZvM8U3F0eaoh8LBdegr4FisA02PpIZzBYEg9IlZAqLlz4aftQXg4J9xXiMKF15Y2Z9060AlfWbvYmzaliz+p5Awa8+e39NaGDfD3CeGZT2oyveZnFOUkdXQjbXsWpGBB2FX6Ka4UKsWNvu/BgQNpLqPBMRzdCF+TSJqrqp2B+UDL9BXTYDCkFlVl55ldLHjc2lcId/chXDzozGTG/lqS+vXhy3dPEjn5W3jwQbj//v+MMDKYwoXh9ddhyxY4esKVpz+uSa1aEB0Noy50ZvO5krh//CGUKcPZwBr8O2FWhsuY00mLjfAFQNs0uI/BYEgH7B0l7v14BO4nj/DwN4E8/fQxChSAHhfeJ2/kWbo4T2H35WLooEHEdIsX4fnmzQyXu0gRy+fV5s2WA8Van3Xgw/pLCXb+mz58zMnj0bzf/RS1asHo965w4qPp6LWEMT+M+5K0x9E9jeQojbHTMBiyNPH3Fa6UL48EBNCpkz8vvCBERsK8efDVV75MXdmJb851ohjHuW/XZWq+DV2an6LUo/dbZ3w7doQmTSyfIhlIQAC8+qqV/v23MHPn9mHAnD6sXRXNja1QYusC+tKB6295s69Ma9w7d+BcRRciYiJpWqmSdcy4dGmW7NqFh4cHISEhGSp/dsJR1+jPJZK6isgYYASwKF2lNBgMd01S+wqurvDkk5Y7kGPHYNAgcA4qzspzDzBiBIQ0iOEnn06E/7bMUhxFi1prSCdPZsYwyJvX2uhftAj+ueDMrFmgT7XjUZ+1fK/tKbnvNx7o14zyzdoT2nktkcWDeeDNjDxmnL1xdHlqaiJpIvASMAfoldaCGQyGjKdoUevI7pEjsGoVPPssXPYpStuzX5Dr+mmecvmZTS4PET1+wn8xovbutVzgZgLe3pbfq5mznPj1Uj3uW/EVQ14+w0sF5vKztuL102Nwi7qF643rOEdE0HL+AoI9KwHmBFZqcVRpBCeSAlTVU1U7qerl9BLQYDBkPCIQEmLFVzp7Fr75BmrXc2NuzBM8eGoOfhHnCKhehG7d4N9OfdBixazwf9OnJxpLPCP2Fpyd4eGHYcyX7nz1T2uaTu+Ek3vCZTTX6AgkpD4f5R3Be8+FsWCBCX1+pzh6eupYIulsegtnMBgyHy8veOEFy+bj2DEYOhSKlfHh4kWYNAlqb/uMsd7v8O/mA9ChA+rvD0OGxLWP78IkI12jBzUMwlXDE+RFigvXnfzof+ltvL/7ksceg4J5o3im/kk++cSKZGtWrpLnjk5PichjIjJKRCaLyEci0iK9BDMYDFmPokVhwADLYeLOndbWxo0i9/P61Q/If+UIDVjNT9KO+Rvzs2sXxFy7TtGJE3EePTpDXaPbx2OP8vYm2s2NX59oyan5Q9k26wgRL/WiYkV4KGIl09cVo+obIXxeYQKVi5yja1fLc++FC+ki3r2NqqaYAF9gDdYpqQjgtO01Gsta3MeR+8S73yvAUSAcCAXqpVBfgNewgkHdsvU/IqV+qlWrpqll1apVqW57r2LGnDNI6zHHxKiuWaPatatq/vyq1m91Kz1XdIVG4ZQwEzTKzU1jTp1KUznsWbVqlS5atEhjTp3S0C++0JhTp3TRokW3jf+f0OMa2mqI/u1bRhU0EmddTBMtwFkVUa1eXfXtt1VXrlQND09XkdOUu/k/A9s1ie9VR4/cDgOqAh2BmaoaLSLOwNPAl7ZyhzbDRaQdMNamONbbXheJSDlVPZ5Es4+xrM77AbsBPyDAQdkNBkM6IgL161tpwgRYscLa2vjtN5h24mHOM4/ZPIUn/y0VObm5IWFh1kaEnx+4uyfdQSpJ7JhxU3//206RFahajAK/vAc6kJhdu/ln7I+UXbGa+4vk41IoVNg+hbDt7rQc/jgxnj7Uq2eFvH3kEStAoXMOc6DkqNL4H/Cuqk6PzVDVaGC6iOTHiuzn6AmqPsBUVZ1ku35VRJoB3YG37SuLSGngVaCiqu6LV5RxcS4NBoNDODtbZhxNmkBUlBUaduE31dCfEn5Rh1+L5v2JQfQ69Sb+G+cgTZpAy5bw6KNQsGCayZOc+5JEKuNUuSKFp1QErF+0165BRLWvyPvXFm6KJ/NvtmTm0qd5b2lz+uNBnjzWgYGHH7ai25Yrl/1dYzm6p5EP2JtE2V4cjB8uIm5ANWCpXdFS4MEkmrUCjgDNROSIiISJyLciknbvLIPBkOa4uEDTpsqjnXexsFUzIl3cueHqx0086cZXjJgaQIelHZka0Z5/F2+BF16wNtHbtcts0ePw8YG8+zbC2rV4dn+BJ/Ot4mfasLBET4oUgYsXlV9/juTVV6FCBYgV/8svYc8eTbCprtlkh10cGYiI7AeWqGrvRMo+BZqpalkH7lMYOAk00Hiu1EXkPaC9qpZOpM0EoBOwC2t5SoHRtuI6qhpjV78b0A2gUKFC1WbOnJni+BLj2rVr+Pj4pKrtvYoZc84go8d87do1YmJiyB8Vhcfp09woFMDmsPxs3lyYLVsKc+KEF6BUZidPOM3Hr4gTYW3aUbf2PzQd3JWrZctyvk4dLlWujKbSRXpajVmio8m9YwcRuXNzvVQpbm45Qf3BPVmVpyVfX2vPr1cbExPP4ff9uY7zSIk/yVM1NwUrRhAcfI1cuTLm2d/NmBs2bBiqqtUTLUxqsyN+Al7H2gSfjOXhtiyW59uvsDbDezt4n8JYX/r17PIHAfuTaDPR1ub+eHn32/JqJdef2Qi/M8yYcwaZMeaYmJgkr/fsUR0yRLVqVVWR//bL83FOV/o+rrdcPFVBY7y9VVu3Vt206Y77T7cx79mj2r69qo+PKmhEvkK6o15PbVFjl3Zx/0qv46kXyaXX8dQOTFNv7wht0SJGR4xQ3bAhfTfWM3UjXFU/FZECNuXRyZYtWCeZRqjqWAcV2HmbkvG3yy8IJGX3cRqIUtW/4uUdBKKA4sAWB/s2GAyZRHJ7C+XKwXvvWenMGcsP1rx5sHZtfh6+Og8PbvIwK/lf5G+0Wvwrv5e6ygNB4H92F/z6q7UXUrFiopsJGhvHI4nru6ZcOfj+e7hxAxYuxHXmTCqv+oFft/QmpvyrOBOBF5azx4l0Y9n1RixYEMCCBVZzd3eoWRPq1rXSgw9ablKyMo76nvID3seaKTwGPAe0AAqr6gBHO1PVCKwjto3tihoDG5NotgFwEZGS8fJKYG3iH3O0b4PBkPXx94eXXoKFCy0biQUL4PmXPNkX3IIuEV+S/+bfNB31CAEBMLLFWhg4ECpXRoOCoEcPyyFVVBRAxsZF9/KyHHjNng2nTyPnzuHk4ZGgiifhnPIsRVjlVnToYHmgD7m1GKd1q/l+xN883jKGfPksPdS1qxX1du9eiLlDd7Caztb3Kc40RMQFuAC0VtVfuXvnhJ8A34nIViyF8DKWMppg6284UFNVH7HVXw78DnwjIq/Z8sZgzTC236UsBoMhi+Lubh2mevRR6/rAAZg/X1i4UNi8Gd46+Sqf8hQtWMATJ3+l8VdTcZo8jcObz1O6ojNuu3dzfeFCYlaupKKrKzF9+nCjRQvo1i3tZxzxcXNDg4KICQ9PEM40xtkZebodgYUL891QKy+6xKs4Hz0EwC1x5wglmLuvNe/u+5DJk+Eh1nHdN4CA2oHUeNCV2rWtmUlSs5HVq1cTHh6erp59U1QaqholImexlpXuGlX9UUTyAe9i2Vr8CTyqqrGzhgCgZLz6MSLyGPAZsBa4CSwD+qjdJrjBYMi+lC4N/fpZ6cYNWLkSfvvNn9Wru/DNgS64E0656L3sqOJO4cKw9do4Hrxii/AXYUUtzIi46GqzRr/RooXVn6cnMTdvMq9FC7zatqVp06Zx7hKdVy6DQ4fg8GHcDx3i/oOH6ZjPDadSsHF9DLMWNsHzajhRy5w5tiyQQ5TidZ5l033PU6um0jxwL/c3DeaBWl64uSnh4eHcmDiRmAUL0k1ROmqn8T3QFVh41z0CqjoeGJ9EWadE8k5jhZc1GAwGvLwsL+2P2QJN//03LFzowdKlVTmxznK6+xbvMIlueHDrv4Yxwr+hYeR7LP1sg2ODXtGtm6WgwsJwCgrCy/aLP8EXd1CQlRo1AsAZa6P2bYBohY1LOb/lMKfWHebW3kMUPXWYgFvnOHgQLh48z3dUgGFwgiL841uSfIUCaHT0N5yjI9NNUTqqNMKAZ0VkGzAPa3M6wUKZqn6TJhIZDAbDHVKsmLUX8tJL1tmrXbtg3U+NYbgk+KaKilLKtwymbKkTvOv1CZEdO1OpfQUC0liHxLdGJyAAgUSt0ZPF2Rnq1SN/vXrk7/tf9pBb8MQO2LrSk0/n/QCHDpPn30OUvHqYsleXEYELLkTG1Xfy9LSs79NokI4qjXG21yJYxnn2KGCUhsFgyHREoFIl5cyZXSzc15xWCxYQ5eQGkVG8k/cDLl3JT75Dv1CPL3Dr9ylb+9XgywKd+bfJ01RvlJt69aBEibu37L4ja/Q7wN0dateG2rV94J1nALh0yQqLO2XZaV79tEQCRRlz8yZOQUFpFkHEUaURnEb9GQwGQ7pjv0S0Z+5cqrRpQ+Ndu2jqtAF39yf5eF4DfOZNp+HRb3j/XHeuTu9H4emnuIYvAQHw0EP/HYWtVCnDI9zeEblzW9b3sIuFRy1FGePiilNUpLWXsmvXnc90ksBROw1ztNVgMNxTpOSwsEGDAvDJa0RF9uaP70I5+cs26kb4sm0bDD3dmbCfgvj4p068RnE8Pa1TSw8+CHXqWL/0CxTI5AHaYa8od9kUZaJ7KXeBozON+ILZ23bEWhAaDAZDlsKRJSIXV6Fi5+pU7Fyd5oDeiuB6wxP4bJrCYAazwaMR42525pc1T7BmzX+2F6VKQa1algKpVcuajaTSy0ma4ahn37shSaUhIv5YbkN+VNVptjxnrDga8bkmIverieRnMBiyAeLuhs/GpRAWhtO331JvyhTqHXuGHc+PYUqu3mzbEsOuP4RDh4RDhyw38GApjCpVoEYNa1ZSo4ZlwOd0R6Hu0kD+dNpLiSW5mcYrWDE0nrSXCZgEnLL93Q7LQG8IBoPBkF0ICoJBgyyr81WrqFKpElXyA1OnEfPpGP5u1JmFedqzclc+duyAw4dhyxYrxeLrC9WqQfXq1mu1alCyZMYrkrQkOaXRDJikqjft8hX4SlV/BxCRc1huRYzSMBgM2Q8nJyviUix58+Lk5krgJ73p7taP7q1awReduVizKZu3CBs3wrZtsGuXcuaMsHo1xPdckiuXNSOpUgWqVrVey5SxXMnfCyQnZmngvUTy7ec6f9nqGgwGQ/bn8cet9McfMGUKfPcdHDpEnt+b0bw5NK95gdW7dxMeHs4DBSrx15Iw1p0IYt4WZ44e9ePiRXfWrIE1a/67pbs7PPCAFQmwcmVrf6RiRUvBZDWSUxoewLX4GWqFeQ3A8lYbS7itrsFgMOQcKlaETz+FESPg5Ekr78oVNDCQiiVKcNjVFf8//6Swhwf1w8Op0KIFXh92o3z5pmzbJmzbBjt3wp9/wokTsH27leITHGx1E5seeMDagM/MELPJKY1/sLzJro+fmciGdzBwLo3lMhgMhnsDd3fLGhBAFRkwgDwTJ1IjLMzKi4jAGZs7j4EDkZ2/Uey+INo0DrI2PYB//4XQUOIUyd69cPAgHD1qpXnz/uvOwwPKlrUiBVaoAOXLW6l48YzZK0lOaawHOgLTUrjHc1jeag0GgyFn4+cHb7+NNGiANmmCXL8eV+Tk6YnMnw+DB/9XP18+CAoi77RpNG5cjsZBB6HiAQgKIqJwEHuP+/D775Yi2bMH9u+3/Grt2GGl+Pj4WMqkXDmlXDkhOjoftWqBh0faevRNTml8BqwXkdHAW6oaFb/Q5jL9IyAEqJdmEhkMBsM9jgYHExMZmdA1+s2bOD3zDNK8OYSFWVOIsDAr+flZlebOhbfeAsANqJwvH5WDgqw4IQUKwI4dXP3rFPtuBLH1nyBC93tz4AD89ZcVf2TbNti2TSjEGYK5xkutTrPmeAa5RlfVTSLyJpZi6CAiy4DjtuLiWIGT8gNvq+qmNJHGYDAY7nGSdY1+5IjlGr1mzcQbd+sGISG3K5Xcua3yyZPxHTeOmkBNsBRJiRJwdgNnzjmxfMhEvJYu5rGwhUSIO55VbmWsa3RV/VhEfgf6A//jvw3vcKzYFh+p6sq7lsJgMBiyCXfkGt2ePHks8/JatRIvHzwYOnRIqFCuXAFnZ/z9of2RuciRpQC4EQHRmeAaXVVXAats1uD5sI7cnlfVNAnKZDAYDNmNNHGNnhj581updu1Ei6VXL3TDhtv3UjLBNTo2JfFPmvRqMBgM2Zz0dueRGFq1auJ7KWnoGv0eNmY3GAwGQyyxeynzWrQg2s2NKG9vot3cmNeiBUt27SKt/MoapWEwGAzZgNi9FK9u3XAKC+OPkSNxCgvDq1u3zHWNbjAYDIasSUa4RjczDYPBYMhGpPdeilEaBoPBYHAYozQMBoPB4DBGaRgMBoPBYYzSMBgMBoPDSFqd3c2K2KIKHktl8/wkjBuSEzBjzhmYMecM7mbMgapaILGCbK007gYR2a6q1TNbjozEjDlnYMacM0ivMZvlKYPBYDA4jFEaBoPBYHAYozSSZmJmC5AJmDHnDMyYcwbpMmazp2EwGAwGhzEzDYPBYDA4jFEaBoPBYHAYozTsEJFXROSoiISLSKiI1MtsmVKLiNQXkfkiclJEVEQ62ZWLiAwWkVMiclNEVotIebs67iLyuYicF5HrtvsVzdCB3AEi8raIbBORKyJyTkR+FZEKdnWy1bhFpIeI/GEb8xUR2SQiLeKVZ6vx2iMi79je31/Ey8t2Y7aNR+3SmXjlGTJmozTiISLtgLHAMKAKsBFYJCLFM1Ww1OMD/An0Bm4mUv4m8AbwKlADKzLjMhHxjVdnDFZ8+GeAekAu4Ddb+N+sSAgwHngQeBiIApaLSN54dbLbuE8A/YGqQHVgJfCLiFS0lWe38cYhIrWBF4E/7Iqy65gPAAHx0gPxyjJmzKpqki0BW4BJdnkHgeGZLVsajO0a0CnetQCngQHx8jyBq8BLtms/IAJoH69OMSAGaJrZY3Jw3D5ANNAyh437X+Cl7Dxem9yHsX4crAa+yM7/Y2Aw8GcSZRk2ZjPTsCEibkA1YKld0VKsX63ZjWDAn3jjVdWbwFr+G281wNWuzt/APu6dZ+KLNaO+aLvO1uMWEWcReRpLWW4ke493IjBbVVfa5WfnMZewLTcfFZGZIlLClp9hYzZK4z/yA87AWbv8s1j/jOxG7JiSG68/1q90e/8199IzGQvsBDbZrrPluEXkARG5BtwCJgCtVXU32Xe8LwKlgIGJFGfLMWOthHQCmmMtyfkDG0UkHxk4ZhPu9XbsDVckkbzsRGrGe088ExH5BHgIeEhVo+2Ks9u4DwCVgdxYa9bfikhIvPJsM14RKY2171hPVSOSqZptxgygqoviX4vIZuAI8DywObaaXbM0H7OZafzHeSwtbK9xC3K79s4OxJ66SG68Z7BmX/mTqZMlEZFPsTb7HlbVI/GKsuW4VTVCVQ+p6nZVfRtrdvU62XO8dbBk/VNEokQkCmgAvGL7+4KtXnYa822o6jVgD3AfGfh/NkrDhu0XSyjQ2K6oMdbacHbjKNabKG68IuKBdaIidryhQKRdnaJAWbLwMxGRscCzWApjv11xth23HU6AO9lzvL9gnRqqHC9tB2ba/v6L7Dfm27CNqQzWBnjG/Z8z+0RAVkpAO6zTBV1tD3Is1qmjwMyWLZXj8eG/D9UN4D3b38Vt5f2BK0AboALWh+4U4BvvHl8CJ4FGWMeQV2H9inXO7PElMeZxtjE9jPWrKzb5xKuTrcYNjLB9OQRhfZkOxzoR0zw7jjeJZ7Aa2+mp7DpmYDTWjCoYqAX8ZhtjYEaOOdMfRFZLwCtAGNaGYihQP7NluouxhGCtVdqnqbZywTrGdxoIB9YAFezu4QF8jjXlvwH8ChTL7LElM+bExqvA4Hh1stW4galYwcZuYZ3NX068I5TZbbxJPAN7pZHtxhxPCUTYvvjnAOUyeszGYaHBYDAYHMbsaRgMBoPBYYzSMBgMBoPDGKVhMBgMBocxSsNgMBgMDmOUhsFgMBgcxigNg8FgMDiMURqGLIuI1BGRWbagMhEickFElonI87H+/0Wkky0YTVC8dmEiMtXuXi1FZLdYwbVURHKLiJOIjBGR0yISIyK/pONYgiSRQFiJ1IsdT6n0kiW1iMgTItInkfwQm8yNMkMuQ8ZiHBYasiQi8hrwCVZAof5Yxmt5gCZYVq2XgHlJNG+NZRkbey8XYDqWq4QeWMZRV4EnsQJUvYHlBffCbXcyxOcJLEviTzJZDkMmYpSGIcshIvWxvpi+UNVedsXzbN5rvZNqr6o77LKKYMXVmKWqa+P1U9b25xhVjUkDud1V9dbd3sdgyMqY5SlDVuQtrMhzbyZWqKqHVdU+vGcc8ZenRGQwllsYgMm2ZZTVIhKG5XIBIDr+0pGIBIjINFsc5Vtixd/uYNdH7DJSfRH5SUQuYcU7QES8RGS8bTntmojMB9I09rSIvCgiu2zLbedFZLJdSFts8g0VkV62oD1XRWSN3B432tlW77SI3BCRlSJSxtZ+sK3OVCwX3EXkv/jUYXZieYnIFzZ5zonI9yKSOy3Hbch8zEzDkKWw7VWEAL+oanga3PJrrDjpPwFDgQVYS1fuQC+soDZ1bHUPi4g3ls+ePMA7wN9AB+A7EfFS1Yl2958OzMBa6or9PH2F5fxyCLANy6voD2kwFgBEZATWktpnQD+smdRQoIKIPKgJY4d0wIq10RtwA0ZhzdbKqGqUrc4Q21hHYfmtqgrMt+v2A6AAVuzpx2159rOqsVhO9J4FSgMfYYUbeP5uxmvIWhilYchq5MeKbXwsLW6mqidEZKft8rCqxgarQURO2urEz+uJFZ+goaqutmUvEpFCwFARmWz3pTxbVd+M17401pfmAFUdYcteKiI+wMt3Ox7bhn8/YIiqvh8v/y9gPdASy3V4LJHAY6oaaasHlgKtiRX1LQ/wGjBBVfvb2iwTkUjg49ibqOphETkHRMR/XnasVdVXbX8vtT2LriLSSY2Tu2yDWZ4yGBJSHzgZT2HE8j3WL+1ydvk/213XwvpczbLLn5lG8jW23X+6iLjEJqylsStY8sdnWazCsLHb9lrc9voA1v7QT3btZqdCtgV217uxZnSFUnEvQxbFzDQMWY0LwE0gMJP6z4vlWtqeM/HK42NfN8D2mlis5rSgoO31UBLl+eyu/7W7jl1S8rC9xsr7j1291MibUl+GbIBRGoYshapGichqoHEmnUb6F2s93p7YMJr2x3Ltl11ilUghrPjNxLtOC2L7bwJcTKbcUWLlLYgVOjQWMzswJIpZnjJkRUZg/WIelVihiASLSMV06nsNUFRE6trlP4v1a3xfCu23YEXNa2uX/3TaiMcy2/2LqxUP3D4dvcP77QauA0/Z5dtfgzVz8LxzkQ3ZCTPTMGQ5VHWtzfL4E5stxVTgONaJpkewwvE+CyR57PYumIp10miuiAwATgDtsfYSXrLbBE9M9gMi8gPwvog48d/pqUfvUI5mInLGLu+yqi4TkZHAF7aN5jVYUdqK2fr5WlVXOdqJql4UkTHAOyJylf9OT3WxVYlvv7IXyCsi3bFicoer6m4MOQqjNAxZElUdIyJbgdexYiPnx7Li3g68hBWmMj36vS4iDbCOi47AMgo8AHRU1e8dvM1LWLHl+2Idc12JpeTW34EonyeStwcrfOc7IrIPy7q9B9YS2d/ACuDgHfQRyyCsUKFdsI4hb8E6irwBuByv3tdAbWAYkBvrhFtQKvoz3MOYcK8Gg+E2ROQprBNg9VV1XWbLY8g6GKVhMORwRKQW0AJrhhEOVMOyyj8APGhsLAzxMctTBoPhGpZ9Rw8gF9aG/yzgbaMwDPaYmYbBYDAYHMYcuTUYDAaDwxilYTAYDAaHMUrDYDAYDA5jlIbBYDAYHMYoDYPBYDA4jFEaBoPBYHCY/wP1C1Ohtt+/XwAAAABJRU5ErkJggg==\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -227,7 +219,7 @@ "int_expdata1 = int_exp1.run(backend)\n", "\n", "# View result data\n", - "int_expdata1" + "print(int_expdata1)" ] }, { @@ -239,45 +231,41 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "---------------------------------------------------\n", - "Experiment: InterleavedRBExperiment\n", - "Experiment ID: 2bf05234-9ce5-411a-b3c5-e14575da2d24\n", - "Status: COMPLETE\n", - "Circuits: 280\n", - "Analysis Results: 1\n", - "---------------------------------------------------\n", - "Last Analysis Result\n", - "- popt: [0.47626118 0.99623266 0.99535996 0.51013026]\n", - "- popt_keys: None\n", - "- popt_err: [1.77332947e-04 2.66140415e-06 2.83096706e-06 1.79438603e-04]\n", - "- pcov: [[ 3.14469741e-08 4.28977668e-10 4.58298066e-10 -3.17868436e-08]\n", - " [ 4.28977668e-10 7.08307204e-12 6.39967732e-12 -4.36846717e-10]\n", - " [ 4.58298066e-10 6.39967732e-12 8.01437450e-12 -4.66354152e-10]\n", - " [-3.17868436e-08 -4.36846717e-10 -4.66354152e-10 3.21982123e-08]]\n", - "- reduced_chisq: 800.0272045289706\n", - "- dof: 24\n", - "- xrange: [1.0, 500.0]\n", - "- EPC: 0.00021900082002077048\n", - "- EPC_err: 1.9493171174852214e-06\n", - "- systematic_err: 0.0016646691935545965\n", - "- systematic_err_L: -0.001445668373533826\n", - "- systematic_err_R: 0.001883670013575367\n", - "- plabels: ['A', 'alpha', 'alpha_c', 'B']" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "---------------------------------------------------\n", + "Experiment: InterleavedRBExperiment\n", + "Experiment ID: f7136452-6d21-4f01-863d-080f0b82c29c\n", + "Status: DONE\n", + "Circuits: 200\n", + "Analysis Results: 1\n", + "---------------------------------------------------\n", + "Last Analysis Result\n", + "- popt: [0.69722312 0.96960905 0.98331924 0.25887581]\n", + "- popt_keys: ['a', 'alpha', 'alpha_c', 'b']\n", + "- popt_err: [0.01166472 0.00182016 0.00341327 0.00568909]\n", + "- pcov: [[ 1.36065800e-04 -1.68564398e-06 -1.45989199e-06 -2.48308105e-05]\n", + " [-1.68564398e-06 3.31297578e-06 -1.71071499e-06 -4.66622029e-06]\n", + " [-1.45989199e-06 -1.71071499e-06 1.16503800e-05 -3.69833061e-06]\n", + " [-2.48308105e-05 -4.66622029e-06 -3.69833061e-06 3.23657136e-05]]\n", + "- reduced_chisq: 0.09217768064462115\n", + "- dof: 16\n", + "- xrange: [1.0, 200.0]\n", + "- EPC: 0.012510569516598985\n", + "- EPC_err: 0.002559948969898621\n", + "- EPC_systematic_err: 0.03307585556038997\n", + "- EPC_systematic_bounds: [0, 0.04558642507698896]\n", + "- success: True\n" + ] }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -298,7 +286,7 @@ "int_expdata2 = int_exp2.run(backend)\n", "\n", "# View result data\n", - "int_expdata1" + "print(int_expdata2)" ] }, { @@ -310,33 +298,31 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "---------------------------------------------------\n", - "Experiment: ParallelExperiment\n", - "Experiment ID: 140d68a4-74af-48f9-8cc1-7d3b61a1f331\n", - "Status: COMPLETE\n", - "Component Experiments: 5\n", - "Circuits: 140\n", - "Analysis Results: 1\n", - "---------------------------------------------------\n", - "Last Analysis Result\n", - "- experiment_types: ['RBExperiment', 'RBExperiment', 'RBExperiment', 'RBExperiment', 'RBExperiment']\n", - "- experiment_ids: ['ba4e4b75-3802-424e-8d5c-3e7928fa9c95', '93c7220c-dbd1-43a8-bb60-e8bcd8b6fbfa', 'adfd1b3c-4435-4c95-aa43-b67a8d230e16', '842fcff6-662c-47c2-a024-e871ac1b9c5b', '11b1662b-3f18-43cb-8e35-8df96ec6003c']\n", - "- experiment_qubits: [(0,), (1,), (2,), (3,), (4,)]" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "---------------------------------------------------\n", + "Experiment: ParallelExperiment\n", + "Experiment ID: 2aa4c1cf-7575-46f4-b80c-c8dc4658eb00\n", + "Status: DONE\n", + "Component Experiments: 5\n", + "Circuits: 140\n", + "Analysis Results: 1\n", + "---------------------------------------------------\n", + "Last Analysis Result\n", + "- experiment_types: ['RBExperiment', 'RBExperiment', 'RBExperiment', 'RBExperiment', 'RBExperiment']\n", + "- experiment_ids: ['6ca546fc-b1c2-42b6-904b-b8b1bf465d4b', 'd3414e4d-90f1-4a19-8b44-4da6972e10fb', '8c888438-1d75-483c-9d8f-fdd2b2a34560', 'b686a25a-bfc2-497a-8ce2-afc32648d2f0', '09597176-8112-422b-894f-c95a20dd15e9']\n", + "- experiment_qubits: [(0,), (1,), (2,), (3,), (4,)]\n", + "- success: True\n" + ] }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -348,7 +334,7 @@ }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -360,7 +346,7 @@ }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -372,7 +358,7 @@ }, { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY0AAAETCAYAAADKy1riAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy86wFpkAAAACXBIWXMAAAsTAAALEwEAmpwYAABODElEQVR4nO3dd3gU1frA8e+bHhJ6S0BJUBApVlBABaLSFBBRr13Bhg0b92dvqFwbNlS4Cvcidix4FQWkKFGRiICIKKC0gEgAQZCWnvf3x2zCZskmk7DJJpv38zzzJHvmzMx7lrBnZ04TVcUYY4xxIyzYARhjjKk5rNIwxhjjmlUaxhhjXLNKwxhjjGtWaRhjjHHNKg1jjDGuWaVhjDHGtSqvNESkp4hME5E/RERFZJiLY44Rka9EJNNz3EMiIlUQrjHGGC/BuNOIB34GbgMyy8osIvWAOcBW4CTgVuBOYGQlxmiMMaYEEswR4SKyFxihqpNLyXMj8BTQXFUzPWkPADcCh2kpBWjSpIkmJydXKLZ9+/YRFxdXoWNrKitz7WBlrh0OpcxLlizZrqpNS9oXcUhRVY3uwDeFFYbHLOAxIBlY7+/A5ORkFi9eXKGLpqamkpKSUqFjayorc+1gZa4dDqXMIrLB774acKcxG9ikqld7pbUCNgCnqGqaT/7hwHCA5s2bd54yZUqFYtu7dy/x8fEVOramsjLXDlbm2uFQynz66acvUdUuJe2rCXcaAL41m/hJR1UnABMAunTpohWtae2bSfWXnJzMhg1+vxAZU2WSkpJIT08PdhjFVNb/55pQaWwBEnzSmnl+bq3iWEw1smHDBmyWZlMd1KbOnDVhnEYa0ENEYrzS+gCbgfSgRGSMMbVUMMZpxIvI8SJyvOf6rTyvW3n2PyEiX3gd8g6wH5gsIp1E5DzgHuC50npOHQrf09q3WWOMcQTj8VQXYJ7X60c82+vAMCAROLJwp6r+LSJ9gHHAYmAn8CzwXGUEl5qaSlZWFvX274e0NDQ5mVnLlhETE1OjnvcbY0xlqPI7DVVNVVUpYRvm2T9MVZN9jlmuqj1VNUZVE1X1kcq4y1BVsrKy2D9hAl0vuQTt35+C5GT2T5hAVlaW3XGYIuPHj6d169bExMTQuXNnvvnmmzKPef/99zn++OOpU6cOSUlJjBkz5qA877zzTlGehIQELr/8crZs2VLi+d59911EhIEDBxZLf+KJJzjppJOoV68eTZs2ZdCgQfz8888VK2g57Ny5kyuuuIL69etTv359rrjiCnbt2lXmcWW9l6rKqFGjaNGiBbGxsaSkpPDLL78Uy5Odnc0tt9xCkyZNiIuL45xzzmHTpk0Bic/4UNWQ3Tp37qzlVbB5s+ZERqtC0ZYXFaUFmzeX+1w1zbx584IdQrk4f75Vb8qUKRoREaETJkzQFStW6IgRIzQuLk43bNjg95gZM2ZoeHi4jhs3TteuXaufffaZJiYm6ksvvVSUZ/78+RoWFqbPPfecrlu3TtPS0vSEE07QM84446DzrV27Vlu2bKk9evTQAQMGFNvXt29fnTRpki5fvlx/+uknPffcc7V58+a6Y8cO12WcN2+eJiUluc6vqtq/f3/t0KGDfvvtt7pgwQLt0KGDDhw4sNRj3LyXTz75pMbHx+uHH36oy5cv13/84x+amJiou3fvLspzww03aGJios6ePVuXLFmivXr10uOOO07z8vIOKT63gvW3WJpD+f8MLFY/n6tB/2CvzK0ilYYuWKA5sfWKVRoF9eurLlhQ/nPVMLWt0ti3b59ed911Wq9ePW3cuLHed999umfPHo2NjdX09HS/x5188sl67bXXFktr06aN3nPPPX6PueSSS/Tcc88tlvbiiy/qYYcdpgUFBaqqOmbMGG3VqlWxPJMmTdK4uLhiaTk5OXryySfr5MmTdejQoQdVGr727NmjYWFhOm3atFLzeStvpbFixQoFdP78+UVp33zzjQK6atUqv8eV9V4WFBRoQkKCjh49umj//v37NT4+Xl955RVVVd21a5dGRkbqW2+9VZRn48aNKiL6+eefH1J8btWmSqMm9J6qUpqcjORlF0sryMxEKzgdiam+rrrqKr788kvmzp3Lu+++y9ixYxkxYgTt27cnKSkJgPT0dESEyZMnA5CTk8OSJUvo27dvsXP17duXBQsW+L1WdnY2MTExxdJiY2PZtGlT0ViTU089lYyMDD799FNUle3btzNlyhTOPvvsYsfdf//9JCcnM3ToUFfl3LNnDwUFBTRs2NBV/opIS0sjPj6eU045pSjt1FNPJS4uzu/74ua9XL9+PVu2bCmWJzY2lp49exblWbJkCbm5ucXyHH744bRv374oT0XiMyWzSsOLqjJr2TKmDTibPMIpAPIio/lkwABmLVvm3JqZkLB9+3Y++OADHn74YU466ST69OnDhRdeyOuvv86QIUOK8kVGRtKuXTvq169fdFx+fj7Nmzcvdr7mzZv7bXsA6NevHx9//DGzZ8+moKCA3377jWeffRaAjIwMALp37867777LZZddRlRUFE2bNkVVef3114vOM3v2bN577z1eeeUV12W97bbbOP744+nevbvfPBs3biQ+Pr5oO+ussw5Ku+GGG/wev2XLFpo2bVpsvIKI0KxZM7/vi5v3svBnWXnCw8Np0qRJqXnKG58pWU0Y3FdlRMT5Nnj9cMYuPYl/briPWXfMpM7pzrfE2jSAJ9StWbMGVS32Qdq1a1dee+01zjvvvKK0li1bsmrVqoOO9/1bUNVS/z6uu+461q5dy+DBg8nNzaVevXrcdtttjBo1ivDwcABWrFjBrbfeyoMPPki/fv3IyMjgzjvv5Prrr+eNN95g+/btDBs2jHfeecf1XcPIkSOZP38+8+fPL7pOSVq0aMGPP/5Y9HrhwoXcfffdpKamFqXVq1ev1GuVVP6y3peSjivpmPK+3yXlqWh8pjjXlYaIDAUuAVoBMT67VVWPPPiomiclJQVV5YvOdWAD7PruVy5+8nr7wwox0dHRAERFRRWlNW/enIYNG9KhQwe/xzVp0oTw8PCDvp1u27btoG/D3kSEp556iscff7zoW+8XXzjDkQpnYn7iiSc4+eSTufPOOwE49thjiYuLo0ePHvzrX/9i7dq1ZGRk0Lt376LzFhQUABAREcEvv/xCu3btivbdcccdTJkyhXnz5nHEEUeU+n5ERETQpk2botebNm06KK00CQkJbNu2rdiHsKry559/+n1f3LyXCQnOZBBbtmzh8MMP95snPz+f7du307Rp02J5evbsWeH4TMlcPZ4SkQeB14AWwI/AVz7b15UUX1CICI0612c+p7Jxk1iFEYJat25NWFgYq1evLkqbNm0aO3fu5O+///Z7XFRUFJ07d2bOnDnF0ufMmVPsebk/4eHhtGzZkqioKN599126d+9Os2bOrDj79+8/6G6g8LWqctJJJ7F8+XJ+/PHHou2cc86hR48e/Pjjj7Ru3brouNtuu4133nmHL7/8kqOPPrrsN+QQde/enb1795KWdmD+0LS0NPbt2+f3fXHzXrZu3ZqEhIRiebKysvjmm2+K8nTu3JnIyMhieTZt2sTKlSuL8lQkPuOHvxZy7w1nuo7n3eStTluFek95TJ36rYJqfLxqfn6FT1Oj1LbeUxdccIGeccYZum/fPl21apXWrVtXW7RooW+++WZRnk2bNmm7du30o48+KkqbMmWKRkZG6sSJE3XFihV66623alxcXLEeV/fcc0+xrrJ//vmnjh8/XlesWKFLly7VW2+9VWNiYnThwoVFeV577TWNiIjQ8ePH69q1a3X+/PnapUsXPfHEE/2WoaTeUzfddJPWrVtXv/jiC83IyCja9uzZ4/c8eXl5xfKWtO3atavU97N///7aqVMnTUtL0wULFminTp0O6tLarl27Yt2M3byXTz75pNatW1enTp2qy5cv14suuqjELrctWrTQOXPm6A8//KApKSkldrktK76KOtS/xcoQ1C63wB7gDDd5q9N2KJXGvHnz9LDDVKFAf16WV/YBIaC2VRpbt27Vc889Vxs1aqSNGjXSZ599VmfMmKEtWrTQhx9+WFVV169fr4C+9tprxY4dN26cJiUlaVRUlJ544on61VdfFds/dOjQYl1W//zzT+3WrZvGxcVpnTp19Mwzz9TvvvvuoJhefPFF7dChg8bGxmpCQoJecskl+vvvv/stQ0mVBs7szwdthWUqSWE5S9uGDh3q93hV1R07duhll12mdevW1bp16+pll12mO3fuPCg23zjKei8LCgr04Ycf1oSEBI2OjtaePXvq8uXLi+XJzMzUESNGaKNGjTQ2NlYHDhyoGzduLHd8FVWbKg1X62mIyGfAF6r6fEBub6pIly5d9FAWYXr3iWQend2VX659gTMmXhLg6KqfmjY1uojg5u/XmMpWHf8WD3ERJr/rabjtcns7cJWIXCkiTUQkzHerUGTVXPJph1GH/ci384MdijHGVAtue0/95vn5mp/9Wo5z1Ridu0aQRneS063SMMYYcP9B/yglrJIX6rp0gRc4jd6Zo8jbvouIJg2CHZIxxgSVq0pDVUdVchzVUqNGsC7xNMIylPVT0jhyxFnBDskYY4Kq3G0RnkWUDheRuMoIqLoJ696V57mdRVsOLzuzMcaEONeVhoj0E5HFwC6ccRt/i8j3ngWSQtax3eMYyfN8vqlTsEMxxpigczsivB8wHYgHHgNuAkYDdYEZoVxxdOkCEeSS9+1CyMkJdjimDMOGDUNEDtq6detWlCc5ObkovU6dOnTq1IlXX3212HlycnIYM2YMJ5xwAnFxcTRq1Ihu3brx6quvkp2d7XvZUn311Vd07tyZmJgYjjjiCFeTDbpZMGjjxo0MGjSIuLg4mjRpwq233kqO19/oqFGjSnwvRIRt27YV5Rs3bhzt27cnNjaWdu3a8cYbb5SrfBWhWvbCSiVx815OnTqVDh06EB0dTYcOHfjf//53UJ5ALPxUa/kbwOG9AWnADCDMJz3Mk77AzXmqejvUwX2qqnv2qJ4nH6mCZn4Z2mtqhMLgvqFDh2rv3r0PGs3svQBRUlKSPvTQQ5qRkaGrV6/W+++/XwGdMmWKqqpmZ2drSkqK1q9fX8eOHas//PCDrlu3Tt977z3t2rVrud6ndevWaZ06dXTEiBG6YsUKnTBhgkZEROiHH35Y6nFlLRiUl5ennTp10l69eumSJUt09uzZmpiYqCNGjCjKs2fPnoPeh169emlKSkpRnvHjx2tcXJy+8847unbtWn333Xc1Pj6+XGtvqDr/FuvXr3ed383CSr7cvJcLFizQ8PBwHT16tK5YsUJHjx6t4eHhxQZSBmrhJ9/yVzfBHhG+HxjgZ99AYL+b81T1FohKQ1X19I5bVUFXD3+6wuerCUKl0ihrUaKkpCQdM2ZMsbS2bdvqxRdfrKqqTz31lIqILlq06KBj8/Pz9e+//3Yd41133aVt2rQplnbNNddot27d/B7jZsGgGTNmqIgUG/X85ptvanR0tN/4Nm7cqGFhYfr2228XpXXv3l1vv/32YvlGjhypp556qusyqpav0nCzsFJJ3LyXF154ofbu3btYnjPPPLPo31Y1MAs/+apNlYbbNo1swN+8yHU9+0NWux7N+JWjyPvKxmuEqpiYGHJzcwF4++236d27N126HDwgNiwsrGiK8MmTJyMipKen+z1vWlraQYsM9evXj8WLFxddr6RjylowKC0tjfbt2xeb+bVfv35kZ2ezZMmSEs/73//+lwYNGnD++ecXpflbHOr777/3G9+hcrOwUkncvJf+8hSeN1ALP9VmbiuNVOAxEWntnSgirYBRwLzAhlW9dO8O8zmNxHXfgmcqalN9ff7558UWD4qPj+fuu+8uMW9eXh6TJ09m+fLlnHnmmQCsXr2a9u3bl3md+vXr065dOyIjI/3m2bJlS4kLCOXl5bF9+3a/x5S1YFBJ5/U31Tg4U6hPmjSJK6+8smhaeHA+UCdNmsSiRYtQVRYvXsx//vMfcnNz/cYH0LFjx2Lvr29ax44dS31PCt8H3/eltAWR3LyX/vIUnjdQCz/VZm4H990NfAv8KiLfARlAAtANpzdVyf8jQ0T37vA4p3FN7iR05Sqko//1Fkzw9ezZkwkTJhRLa9CgQbHX999/P6NGjSI7O5uoqKiixY4A13MIDRkypNgqf/6UtIBQSemlHVN4XFmLCvlLnzFjBr///jvXXnttsfQHH3yQLVu2cMopp6CqNG/enKFDh/L000+XumjTjBkzit2JtG3blhkzZtCyZUuAUitSf3H6ls/tMb7pgVrUqSLx1QZuB/f9JiLHAv8EegAnAn8BY3GmTM+ovBCDr00bSGs4gNN3fsmksCNoXfYhJojq1KlT5uJBI0eO5JprrqFOnTokJiYW+zA46qijWLlyZUBiSUhIKHGRoYiICBo3buz3mLIWDEpISODbb78tdpy/b9EAEydO5JRTTjnoDiA2NpZJkybx6quvsnXrVhITE5kwYQJ169Y9aPlUb4VrqPumFS4oVRo3Cyv5O66s99JfnsLzBmrhp9rM9TgNVc1Q1f9T1a6q2tbz865QrzAARKDNKc1I5XRSv/NdtNDURI0bN6ZNmza0aNHioG+Pl156KXPnzqWkGZILCgrYvXu36+t0796duXPnFkubM2cOXbp08ftt3M2CQd27d2flypVs2rSp2Hmjo6Pp3LlzsfNt3ryZ6dOnc9111/mNMzIyksMOO4zw8HCmTJnCwIEDCQurnHlI3SysVBI372X37t1LXdQpUAs/1Wr+WshDYQtU7ylV1dGjVU9giX5w0lMVPmd1Fyq9p0rqcrtt27aiPCX1nvKWlZWlPXv21AYNGujYsWN16dKlum7dOp06dap279696H366KOPtF27drpp0ya/5yrsJnrbbbfpihUrdOLEiRoZGVmsm2hJ5ylrwaDCLrenn366/vDDDzpnzhxt0aJFsS63hR577DGtV6+e7tu376B9v/76q77xxhv622+/6cKFC/Wiiy7SRo0aldkTatu2baUu2OT9fpfEzcJKV1xxhV5xxRXlei+//fZbDQ8P18cff1xXrlypjz/+uEZERBzU5TYQCz95K+lvMdiqvMst8CVwtNfvpW1f+DtPMLdAVhpffKF6O885b9kff1T4vNVZqFQalLCAUMuWLYvylFVpqDoVx5NPPqnHHnusxsTEaIMGDbRr1676yiuvaHZ2tqo6K+3hoqtpamqqnnDCCRoVFaXJycn673//u9j+ks7jZsGgDRs26IABAzQ2NlYbNWqkI0aM0KysrGJ5CgoKNDk5WW+88cYSY1uxYoUef/zxGhsbq/Xq1dPBgwcXdestTVJSUqkLNnkvQFUSNwsr9erVS3v16lUsraz3UlX1gw8+0Hbt2mlkZKQeffTROnXq1IPyBGLhJ2+1qdLwuwiTiMwDblTVVSKSShmz3Krq6eW+zalkh7oIk/cCJnv2QO/6i1ioJ5Pz5ntEXX5hgKKsPmwRJmMqpjr+LVbWIkx+G8K9KwFVrdiVQ0jdupDX6Xj2La/Dzo/mc1gIVhrGGFMWt3NPXSkiJXb1EJFGInJlYMOqnk46JZLv6EZ4mg3yM8bUTm67R7wGHOlnX2v8r+gXUrp1cwb5xe9Ih6ysYIdjjDFVzm2lUdqIljggLwCxVHunnAJPcxdt6v2JRlvXW2NM7eO3TUNEjscZxFdokIj4LioRC1wMrC7PRUXkJuBOIBH4BbhdVb8pJf+FwH3AUcCfwMuqOqY81wyEtm0hvlkc27bBr7/C0UdXdQTGGBNcpY0IHww87Pldgfv95NsBXOP2giJyEc5I8puA+Z6fM0Wkg6puLCH/WcA7wK3A50B7YKKIZKrqy26vGwgi0KMHtJ46hvxrVsO3E8o+yFSapKQkm9bBVAsljZAPVaU9nnoBp73iCJzHU+d5XntvLYBmqjqtHNccCUxW1YmqulJVb8GZy+pGP/mvAD5V1fGquk5VpwNPAHdLED4xevaEw9hEm4VvQSXNAmrcSU9PP+SxPPPmzQv6eKKq3qzMgd9Km+k41PitNFT1b1XdoKrpOBXEDM9r722LqrrunCwiUUBnYLbPrtmAv/H50YBvq3MmcBhQ5dV7jx5OY3h0fiYsXVrVlzfGmKByO2HhhgBdrwkQDmz1Sd8K9PZzzCxgrIj0BeYCbXAmTgSnTSTdO7OIDAeGgzOVcWpqaoUC3bt3b4nH5ufD0tiukAk/jnuLXfv3V+j81ZG/MocyK3PtYGUOILe3XzgfxEtxVvHL991cnqMFTvtID5/0h4FVfo4R4Cmcu4s8nNl1H/ac5+TSrhfIaUS8nXWW6hqO0PQTh1T4/NVRTZtGJBCszLWDlbl8ONSV+zyD914CFgExOOMy3gJ2A2uBR13WUds9lUyCT3ozDr77AIomdLkbiMd5HJUAfO/Zne7yugHVowdM5Xx+29cyGJc3xpigcTtO43acxufCxurxqjoUp5E8E6cHVZlUNQdYAvTx2dUHKHUdRVXNV9U/POe4BEhT1W0u4w+onj3hbp7mFl4KxuWNMSZo3FYabYGvgQLPFgWgqjuBfwG3leOazwHDRORaEWkvImNxHlu9AiAiT4jIF4WZRaSJiNzoyXu8J/8/cCqyoOjSBaKj4ddflR2bMoMVhjHGVDm3lUYmEOZ51rUF5w6j0F6cD31XVPU9nA/8B4AfgdOAs/VAY3siB09ZciXOo7FvgY5Aiqp+T5BERztTivzAiey+/KZghWGMMVXO7Rrhy3F6Lc0FvgHuE5H1OA3To4BV5bmoqo4HxvvZN8zn9Xage3nOXxV69IANXyXR4sdvy85sjDEhwu2dxgSgoef3B3EapecD3+FM7fFPP8eFrJ49nfEazf9eDVtLbMM3xpiQ46rSUNX3VPUJz+9rcB4R9QOGAG1UNbXSIqymuneH78JPA2DfbLvbMMbUDhVaOV5V96nqXFWd5nl8VOvEx0PEySeSSQyb37f1NYwxtUNps9y2Ks+JtITJBkNdz95R3Jf2OK3yO3FHsIMxxpgqUFpDeDplrAvuI/zQQql5zjwTUh67g6PXY5WGMaZWKK3SuJryVRq1TrduEBeTR8yq5fy5pDFNO7dCVW26bmNMyPJbaajq5CqMo0ZKS0vlxLaHk7r8RNaMvJYmUx5l1rJlxMTEkJKSEuzwjDEm4CrUEG6ciR6zsrI4O38CIBz5zWsUJCezf8IEsrKyCidbNMaYkOJqcJ+ITCoji6qq69X7QoGI0O+44zhz9bmEoaD5kJPP4OnTCRs3zh5RGWNCktsR4WdwcPtGI6AusMuz1TqSnk5EbDTkZhelhcXGIunpkJgYvMCMMaaSuF2EKbmkdBHpiTPR4GUBjKnG0ORkCrKyinUbK8jMJCw5GbvPMMaEokNq01DVr4HnofbNEa6qzFq2jE8GDCAnPJq9xJEVFsMnAwYwa9kya9MwxoSkQDSErwNOCMB5ahQRISYmhjrDh7Nu7nr6MIcuDdcRe91wYmJirE3DGBOS3LZplEhEIoBhwKaARFPDpKSkeO4ohI0Jzblmy2jaL2lN8gNXBDs0Y4ypFG57T31ZQnIUzgy3jYEbAhlUTVJ4R9HvrDDOfe1jwibUAas0jDEhyu3jqTBAfLY9wEfAmao6sXLCqzn694dPGUTL39Nge62cw9EYUwu47T2VUslx1Hh9+sAzMoiH9VEyp84g9vorgx2SMcYEnI0ID5CGDSGq24lsJpEdr00LdjjGGFMpXFcaItJWRF4Xkd9EZJ/n52QRaVOZAdYk/c4K430u5PdtUcEOxRhjKoXbhvAUYAaQCUwHtgLNgUHARSLSX1W/qqQYa4z+/eHkh17g8DzYoGC9bo0xocZtl9tngaVAP1XdW5goInWB2Z79XQIfXs3SuTM0aQK//w6//rCPozvHBTskY4wJKLePpzoAT3lXGACqugd4CmfN8FovLAz69YNnGUlC32PARoUbY0KM20pjE864jJJEAX8EJpyar39/WEl7Gvy1Hn7+OdjhGGNMQLmtNJ4CHhGRlt6JntcPA48HOrCaqm9f+IyBAOR89GmQozHGmMByW2n0wpkGfa2IpIrIeyKSCqwF4oEUEXnDs71eSbHWCM2aQauTE1lEF/a8Y5WGMSa0uK00TgPygQwgCTjZ8zMDKAB6+Gy12jnnOKPDG/62ELZtC3Y4xhgTMG5HhLeu7EBCyTnnwPkPXMy+uOaMiYqxEZTGmJBhn2eVoFMnyE0+iuf2Xc+Cn+sFOxxjjAmY8owIryMiI0TkAxH5QkTeF5GbRKROZQZYE4k4dxtN+JNNoyZCVlawQzLGmIBwVWmISALwA/AiziC+OsBJwMvAEhFpXmkR1lCDB8NJLOLiL4bDvHnBDscYYwLC7Z3G00BDoIeqtlbV7p52jtOABjhdcl3z3KGsF5EsEVkiIqU2notIPxFJE5E9IrJdRD4RkaPKc82q1qMHLKl3Bvuow663rBeVMSY0uK00zgLuVdVvvRNVdQHwADDA7QVF5CJgLM7YjhOABcBMEWnlJ39r4BPgG0/+3kAszlxY1VZkJJw5IIY59EGmf2ajw40xIcFtpREPbPazb5Nnv1sjgcmqOlFVV6rqLThdd2/0k78zEIlTaa1R1R+BJ4AjRaRJOa5b5Qq73tb/+3f46adgh2OMMYfMbaXxK+BvDdPLgVVuTiIiUTiVwGyfXbOBU/wcthjIBa4VkXDPJIlDgUWqWq2XyOvfH2aFD6AAYe/c74IdjjHGHDK3s9w+A7zhafB+B+fOIAG4GOdxkdtFsZsA4ThTq3vb6jnPQVQ1XUT6AB8A43AquqU4j8wOIiLDgeEAzZs3JzU11WVoxe3du7fCx3pLPOFYmi/eyrAtOxgQgPNVpkCVuSaxMtcOVuYAUlVXG84H8RacEeCFWwZwXTnO0QJQnAZ17/SHgVV+jkkAfsNpjD8B6Amkeraw0q7XuXNnrah58+ZV+FhvEyaogmpKSkBOV6kCVeaaxMpcO1iZywdYrH4+V12P01DVCZ4P/Y44U4V0BFqq6sRy1FHbcaYjSfBJb8bBdx+Fbgb2qepdqrpUVb/GeSTWC/+PtKqNIUOgQdhuRn51DntfeSvY4RhjzCEptdIQkWEi8qOI7BWRTcAYYK2qfqtOI3ZBeS6mqjnAEqCPz64+OL2oSlIHp6LxVvi62o9ob9IEOqfU5VhdxvZ/fxDscIwx5pD4/dAVkUuASTgf2tNxHkXdwaFPg/4cMExErhWR9iIyFucO5hXPdZ8QkS+88k8HThSRhz3rlJ8IvAb8jlMBVXv/uFD4lEEk/DwHMjODHY4xxlRYad/Ubwf+B7RX1YtU9STgEeBmEQmv6AVV9T3PuR8AfsQZIHi2qm7wZEkEjvTK/yVwKTAYpwF8Fk5vqv6quq+icVSlIUNgugwipiCTvdO+DHY4xhhTYaVVGkcBE1XV+9HQeCAaKHEgnluqOl5Vk1U1WlU7e9opCvcNU9Vkn/xTVPVEVY1X1aaqOkhVVxxKDFWpWTPQXinsIZ7fx9vocGNMzVVapVEf+MsnrfB1w8oJJ3QNvjCalxnBl1s6BDsUY4ypsLLGaYSJiHfFEu4nnfI2itc2550HLUY8QUQ6XLYLGjQIckDGGFMBZfU++han/aBwK2zFXeiTnlNZAYaK5s2hVy8Iy8lk5ktrgh2OMcZUSGl3Go9UWRS1xGWXwYPzzibxqT3w4OJgh2OMMeXmt9JQVas0Auz88+HZ6/vw2L772bzoD1qc1BJVRUSCHZoxxrhS7QfHhZIff0xlw7HOIPb0kS+iGRnMmjWr1s2JY4ypuazSqCKqSlZWFl0j36cA4eT5z1GQnMz+CRPIysoqnGfLGGOqNbez3JpDJCL0O+44Cn4cTBhKGHmQA4OnTyds3Dh7RGWMqRHsTqMKSXo6YTExxdLCYmOR9PTgBGSMMeVklUYV0uRkCrKyiqUVZGaiycnBCcgYY8rJKo0qoqrMWraMTwYMID8qij1SlzzC+bRdP2YtW2ZtGsaYGsF1pSEiLUXkORFZLCLrRKSTJ/12EelaeSGGBhEhJiaGOsOHE5aeztQrPmEP8TTYnE9MTIy1aRhjagRXDeEi0hH4BmcdizScFfSiPLuTgJNxZqI1pUhJSSkal3H6o4lMeOMG/m/HGPY2Gh/s0IwxxhW3dxrPAiuB1sB5gPfX4gVAtwDHFbIK7yiSkmB5z5tRhNW3vRzkqIwxxh23lcZpwJOquhdnjW9vWzl4+Vbjwjk3H85Uzqft1/9B9+wNdjjGGFMmt+M0SpvBtgkHJjI05TB4MAyqN5LNu1vQa2EOJ/YOdkTGGFM6t3ca3wNX+dl3Ic5suKacoqOh49VdGcnzvPR2o2CHY4wxZXJbaTwGDBKR2cAVOI+oeovI68AQ4F+VFF/Iu+YaAGXHe3PJ+mZRsMMxxphSuao0VPUr4FychvBJOA3hTwI9gHNVdWFlBRjqOnWCU0/K5ZXMK/nzhgeDHY4xxpTK9TgNVZ2uqm1x1g4/DWivqkeo6sxKi66WuPamKMZzE4evmIWuWBnscIwxxi9XlYaIPCQiLQBUdY2qLlDVXz37EkXkocoMMtRddBF80PB6sohm6/0vBjscY4zxy+2dxsPAYX72tfDsNxUUGwtDhjflLS6n4aevw19/BTskY4wpkdtKo7Q5LhoC2QGIpVa74QZ4SW5jR34D/lqwKtjhGGNMifyO0xCRFOAMr6TrRWSgT7ZYYADwS8Ajq2WSkyF50DG0mraR+xdF8IjvO22MMdVAaYP7egEPeH5XSh6nkQOsAG4NcFy10ogRMG1aBP99NY8Hr9pCRLK/J4LGGBMcfh9PqeojqhqmqmE4j6e6Fb722mJU9URVTau6kEPXmWdCu3bw5tY+7Ox3UbDDMcaYg7gdpxGmqt9XdjC1XVgY3HILfMy5NP1tAbpocbBDMsaYYsq9CJOINBORVr5bZQRXGw0bBp80vIrd1GXb/WODHY4xxhTjdpxGmIg8LiI7gAxgfQmbCYC4OLhyRD0mcTWN5r4HGRnBDskYY4q4vdO4HbgZZ10NAR4HRuNUFmuB6yojuNpqxAiYEHUL4ZrH1ufeDnY4xhhTxG2lcRXwKPCU5/X/VPVhoD3wB1Cux1MicpOIrBeRLBFZIiI9Ssk7SkTUz9asPNetKZo1g55XHcnJfM+dGSODHY4xxhRxW2kcASxW1XwgD2d8BqqaC7wAXO32giJyETAW527lBJyV/2aW0i7yDJDos30FpKrqNrfXrWlGjoQfpAvvfRBGxmbfda+MMSY43FYafwMxnt83A+289kUA5VkMYiQwWVUnqupKVb0Fp53kxpIyq+peVd1SuAGROLPrTizHNWuco45yFmm6Ludl9nQ9E/RAxaFqlYgxJjjcVhpLgQ6e32cBj4jIJSLyD+AJ4Ac3JxGRKKAzMNtn12zgFJexXAPsAqa6zF9j3XsvZBLLUZvm8ffkjyAtDc3IYNasWaSmpgY7PGNMLSRuvrWKSB/gCFV9VUQSgE9xPvwBNgCDVfUnF+dpgdMG0ktVv/ZKfwi4TFXb+T3YyRcGpANTVfUOP3mGA8MBmjdv3nnKlCllhVWivXv3Eh8fX6FjA+m+O5KZ9WMn6pCJxsUiubl8f801/Nm/P/Xq1QvotapLmauSlbl2sDKXz+mnn75EVbuUuFNVy73h9KBqAxwLRJbjuBY4U5L08El/GFjl4viBnuM7urle586dtaLmzZtX4WMDadGnmzWHCFXnAZUqaF5UlBZs3hzwa1WXMlclK3PtYGUuH5w27BI/V92O07hSRBp7VTSqzroaPwF1ReRKlxXYdiAfSPBJbwZsdXH8dcACVa01EyR2aZxOTnhssbSw2FgkPT04ARljajW3bRqvAUf62dfas79MqpoDLAH6+Ozqg9OLyi/Po60BhHgDuC9NTiYqLKdYWkFmJpqcHJyAjDG1WiDW04jD6Ybr1nPAMBG5VkTai8hYnMdWrwCIyBMi8kUJx10N7APeL8e1ajRVZdayZXw68GyyJIZd1Cc7PIZPBgxg1rJl1ovKGFPlSltP43jgRK+kQSLSySdbLHAxsNrtBVX1Pc+jrgdwxlz8DJytqhs8WRLxuasREcHpNfW2qu53e62aTkSIiYmB4cNZNnQc95/7CzfrKzQ7vAN5MTE4b4sxxlSd0tbTGMyBZVwVuN9Pvh04H+iuqep4YLyffcNKSFOcx2C1TkpKCqqKiBA/sBmtP/s/Dpu0mCZP3hvs0IwxtVBpj6dewPmgPgLn8dR5ntfeWwugmapOq9wwa7fCO4rRT4RzBy/QZO8GdjzwfJCjMsbURqUtwvS3qm5Q1XScCmK657X3tkXtwXqV6dQJkoamMJXzqDP2cZsB1xhT5dwuwrRBnXmmEJFoz4SDL4vIfZ5eTaaKPPooPBj1NGH5ufw5YlSwwzHG1DKlNYQ/Cpyvqh290qKBhcAxHOhRdZuIdFNVW1OjCrRqBWeNOJLLnnubvE2n8j8Faw83xlSV0u40egMzfNJuxhkFPgaoD3QDcnF6Qpkqcv/9MK/RBXzyfSJTP9RikxkaY0xlKq3SOBLwXRf8XJwZae9V1T3qrBs+BjizcsIzJWnUCB57DJqxlaTLe5D9bsjP3WiMqSZKqzTq4zW1h2eG2pOBeT6N38twxlaYKjR8OCR0bEJUzh7233wnZGUFOyRjTC1QWqXxB5Ds9borEMXB031E4ozUNlUoIgJeeCmcO3iehrvS+fuRF4IdkjGmFiit0vgGuF1EGnhGZN8KFADTffKdAGyqpPhMKU4/HRqedwYfM5ioZ/4FW7YEOyRjTIgrrdJ4BOdOYyvOokfnAxO8pvsoNAyYXwmxGReeew4ejB5DeF426256JtjhGGNCnN8ut6q63jP/1LVAQ+B7VX3TO49njMYXwBuVGaTxLykJho5uy9l3zmDD96fy4z6Iiwt2VMaYUFXq4D5V3aiqD6nqLb4Vhmf/Zs++RZUXoinL7bfD9uN6s+aPWB6+J9u64BpjKo3bqdFNNRYRARMnwpGyjptebs/65/4X7JCMMSHKKo0QcdJJMPCmVuwjjsj7/o/cvdlF+2x6MGNMoFilEUIeeyKCxxo8yWE565nf/zFIS0MzMpg1axapqanBDs8YEwKs0ggh8fFKl3ubsoTjSfn2X+T37kdBcjL7J0wgKyvL7jiMMYfMKo0QIiLcecXhHBO2EgHC9+8hPCeHwdOn0++442ylP2PMIbNKI8RIejqR8dHF0sJiY5H09OAEZIwJKaVNjf5lOc6jqmqTFlYDmpxMQVYW4V5pefsyiUhOxu4zjDGHqrQ7jTCcNTMKt6OBFJxR4rGenylAO7DPo+pAVZm1bBmfDBhAflQUWdH1KED4Pa8FU2YvtzYNY8whK2251xRVPV1VTwfG4qyb0U1Vj1DV7qp6BNDdkz62asI1pRERYmJiqDN8OGHp6UR98TnPtXuVI0gn884ZFBRY3W6MOTR+H0/5eAx40LN+RhFVXSgio4DRwCcBjs1UQEpKCqqKiCCJiVye2p03Wn/PsD9f5I1hgxj2pj1FNMZUnNuG8LbAn372bQPaBCYcEwjevaQSEuCw91/gV9rR960rmPuuv39GY4wpm9tKYz1wvZ991wPpAYnGVIozBsXx1Q1T+Jz+XHVDNGvWBDsiY0xN5fbx1CPA2yLyM/AhznTpzYELcBrIL6uc8EygDB93HOdlTGLTJzBooPL9IqFu3WBHZYypaVzdaajqFKAf8DdwLzDO83MX0E9V36usAE1ghIXBG29An9ZreP3Xrjxwzk8UFAQ7KmNMTeN6cJ+qzlXVU3G62yYAsap6mqp+UWnRmYCqVw/+/XY9WsnvXJ96MfffsT/YIRljaphyjwhX1QJV3aaq9j21BjqyezM2P/EGHVhJ0osjGTcu2BEZY2oSt20aiMgRwIVAKyDGZ7eq6jWBDMxUnhPv7sPy1Du54fMxXHBLX5KSzmPgwGBHZYypCVxVGiIyGPgA585kG5Dtk8WGGtcwx3wymk3J87ghYzzn/GMIX3xpA/+MMWVz+3hqNJAKJKpqC1Vt7bMdUXkhmkoRFUXLxdN459LpZGYJZ50Fa9Y4i4vbdCPGGH/cVhpHAM+oakBGhonITSKyXkSyRGSJiPQoI7+IyO0iskpEskUkQ0SeDEQstZm0SOSyq7+jd5ffGPL3JN6/I5M1823RJmOMf27bNFYBjQNxQRG5CGeuqpuA+Z6fM0Wkg6pu9HPYs8BA4E5gOVAfSAxEPLWZqpKbm8m4vwbQljXs2xtHeI8C0vqcR9ORlxdNR2KMMYXc3mncBdznaQw/VCOByao6UVVXquotQAZwY0mZRaQdcAswWFU/UdV1qrpUVWcEIJZaTUTod9xxHLlpIwLEs49YMrlgzkccVc8WbTLGHMxtpTEK505jpYj8LCJf+2xfuTmJiEQBnYHZPrtmA6f4OWwwsA7oLyLrRCRdRF4XkWYuYzelkPR0wmKKd4YLI587B67i11+DFJQxptpy+3gqHwjER0gTIBxnGhJvW4Hefo45AkgCLgaG4fTUegb4VES6+44XEZHhwHCA5s2bV/jZ/N69e2vFc/2ov/6iq8+iTZHksnRnEqecks3TT//EkUfuC1p8la22/Dt7szLXDpVWZlWtsg1ogfOh38Mn/WFglZ9jJniOOcor7ShPWtfSrte5c2etqHnz5lX42JqioKBAZ86cqVOHDNG8qCjNjYvTvKgo/fDsIXrccX9qLPv0hpjXNHVeQbBDrTS14d/Zl5W5djiUMgOL1c/nalWvEb4d564lwSe9GQfffRTKAPJU9TevtNVAHs5AQ1NBvos2/fTUU4SlpxN3y3CefvoXxh47iX9nXcWeMwfz2eTtwQ7XGFMNuB3c17OsPKr6tYs8OSKyBOiDM1iwUB9gqp/DvgUiRORIVV3rSTsCJ/YNZV3TlM570abdHTsiiYn0S0hARMg/oycfpORzzrd3seOqY/nguze54N9nYu3jxtRebu80UoF5ZWxuPQcME5FrRaS9iIzFeWz1CoCIPCEi3pMgzgV+ACaJyAkicgIwCVgILC7HdY0fvr2kCl+HRwgXfHMbr9+4kL+pz/mv9uHN014lJycYURpjqgO3DeGnl5DWGGfsRC9ghNsLqup7ItIYeABnrMXPwNmqWnjXkAgc6ZW/QEQGAi8CXwOZwBxgpNqkiZVOBIaPP54Puy5m3tX38OyC3kw8Ez76CJo2DXZ0xpiq5qrSUFV/XWo/EpHngUHATLcXVdXxwHg/+4aVkJYB/MPt+U3gXTA0jkUdXmLfIJg/X0lNGsbxd/Wl7Shbf8uY2iQQDeHTcWa/NSHupJNgyRI4vfMeEjLX0faRy1l58pXo7j3BDs0YU0UCUWm0A+wxUS3RsiXMSqvHRyPm8TCjOGrR22xpcQK75iwKdmjGmCrgtvfUlSUkRwGdgGuAjwIZlKneIiPh+ZcieL/Hw5x91ZlM3HcpBf3PY9Ena+gzMDrY4RljKpHbhvDJftKzgfeA2wISjalRLrwQunY9jev+sYwdi9ayZFA0N9+Qz1N37SCutc3yYkwocvt4qnUJW6KqxqrqMFX9u7ICNNVbUhLMSGvIOY90ISIC6r3yFFltOrL0X9ODHZoxphK4qjRUdUMJm78R3KaWCQ+Hhx6CxYvhl7ZD+L2gJSc8MJBZHW5n60bfRR6NMTVZuRrCRWSgiIwRkf+KyNMiMqCyAjM1z3HHwYe/tGfmQ9/xcvit9Fs5lm2tu/L2g6vIzw92dMaYQHBVaYhIXc/059Nw2i/OBm4HpolIqojEV16IpiaJjIR7H4mh/6qx3H/cpzQu2MZLo3dxwgkwZ06wozPGHCq3dxqPAycCVwCxqpoIxAJXetIfr5zwTE3Vpg2MXjqQ795ex6aW3Vi+HN7q+zoX9tnJzz8HOzpjTEW5rTTOBx5Q1bdVNR9AVfNV9W3gQc9+Y4oRgfMujWH1ahh7RzoTuY4xc4/nxmPmc9ll8NtvZZ/DGFO9uK00GgMr/OxbQYDWDzehKTYWbn0umb0z5xNbL5JUetHmnUc4pn0eV16prFx5IK8zlb8xprpyW2msx5mcsCRne/YbU6pG/U+m2aalZJ53GY8wimkFA3nzTTijQwb3pqSxdGYGs2bNqnUrrBlTk7gd3Pcq8KynwfttnIWREnCWYL0WGFk54ZmQU7cu8VPfQN/qS2zqCh784lHuSX+KnK8iifoql8mN/kX8DSmceqoSGWkLdxhT3bid5fZ5EWkK3IGzTjeA4IwIf1JVx1ZOeCZUyeWX0+PMDE59M5lwcqhDJgBP/XUvyY+v5803heuug2uugRYtghysMaaI2y639YFHcRZLGojTa2oA0EJV76+88Ewok/R0wmJiiqXFkM2y8BO55fc7eeghpVUrGDIEPvkEcnODFKgxpkiZlYaIRAA7gD6qulNVZ3p6Uc1U1Z2VH6IJVZqcTEFWVrG0gogImvXqyNXH/8CAAYIItPz4Zcae+yWtWuRx223w3Xdg7eXGBEeZlYaq5gFbARvTawJGVZm1bBmfDBhAflQUWr8++VFRfDxoELPu/D8aLZ7NZ5/Bxt+yeCbmAb7kTH7ansgxL17Lo91ncHTrbO65BxYtsgrEmKrktvfUWzgN3sYEhIgQExNDneHDCUtPR2bOJCw9nTrDhxMTE4OEhwOQ2DqGmO1/oB98SFjfPlwW+T4zGMC5G17gqafgtJOzOerwTG65BWbNAp8bF2NMgLntPZUOXCoii4BPcHpPFft+p6qTAhuaCXUpKSmoKiICiYkI0C8hwXntLS4OueB8Gl9wPmRnUzB7LufsO5Ydc0GmTuOFP4Yx4+Wzee3l87kydgDd+tSlb1/o29cZme57OmNMxbmtNMZ5frYEOpewXwGrNEy5+VYQB1UYvqKjCRs0gFOBUy+GgpuPYttjQ+k3+3/8Y9+HZGVGM3taXy6b9jYjqEtSEpx+OqSkOFtSUmWVxJjawW2l0bpSozCmgsJOOI6Ej8ZD/kuQlkbu5Kmc8M1PnH5UPPO/hfM2PMf+yXW4a/IQttGcww6DU0+FU05RuncXcnOdSqrojscYUyq34zQ2VHYgxhyS8HA47TTqnnYadXGmYy7IV/Yf9z7xvyxkPDeRFn4a7286n4/eO4/33juc5myhbXgEzx+bQXjLHNq3z+TSS4+mQweIigp2gYypntzeaRQREd/Gc1WbMMhUQ2HhQvzyNPj5Z8KmTuWUqVM59efbOfeYRczddTQP/P4v8vIjCF+az/VLX2XMZ1cwZgxERED79s76IJ06QceOzpaUBGHlWoHGFPK9k7M7u5rLb6UhIgnAf4H3VPUNT1o4kOOTda+IHGUr+ZlqSQSOOQaOOQYZNQp++42UXbvo2aMH4V5/ym9wJf9sPJklucfxzO7hLF9+ND8vL0C9OhjGxsJRR0G7ds7Ptm2dhvY2baBpU2tw9yc1NZWsrCz6HXcckp6OJicza9kyYmJiSElJCXZ4ppxKu9O4CWetjAt80gWYCGz2/H4RcAPwSGUEaExAHXUUkpbmjETP8fr+ExnJ8Y1+5/hNaVwy6zyWxMLu/3xIz/duYn300fyU056lmUezcll7PlvWi/3EFTttXBy0bu1sSUnO1qoVHH64syUmOk/QIHjfuoNxXVUlKyuL/a+8QsHMmYTFxFCQlcX+AQNg+PCQvuMI1bur0iqN/sBEVc30SVfgVVX9AUBE/sSZVsQqDVMjFI5ED/dKKxAh7KuvkObNqaNKj3AgshXEnM+xK1dy7KpPuDzrPwC89dh6fvgrjsNS36LDuk9ZlnU0S/e1Z9XPRzPn53ZkEXvQNcPCICEB6tbdTcOGmZzWpoB2UelEHpXMmr0ZNG8O/fufSNOmUK9e4O9avL/t1/vlF7Rdu/J921eF/fudwOrUgT174KuvYOdO2LXL2XbuhAsvhG7dYNkyGDYM2bWLfn/9Bbt3IwA5OYQDgz/7jLD69ZFffoHk5ANbo0YBLXywPrhD+e6qtEqjHfBQCem+7/hvnrzGVHuFI9H3DxjA4OnT0chIJDeXTwYMoM6yZfTr1w8pbLjo1s3ZCu3YAatWcXn3VlweBrz4F7y4hP7rPwQKACgIj+DJ+/eydlM0hy/9hLBtW1m0tz3f/X00mzc3BepxOZ/wyHfXk0MkUeRyPa8ymiuKLhMVBU2aQOPGztakifNZ2rDhgZ8NGjhb/frFt5gYr89cVcjLQ7OzyfvzT/Jfe42CL77g2MhICkaOpG7nzsT17ImuXIn8/bfzod+zJwwYANu3w9lnH6gQdu1yJv965hn45z9hyxYYNKj4mxsfD8ce67xncXHOLdYxxyBZWei0aZCdXZQ1LDISef99mDy5+DlSU6FXL/jmG3jvveIVSlKS82a4/NA/5IqygoruriZMoGD69JC7uyqt0ogB9nonqGq+iCQC272Sszx5jan2CkeiM3w4YePGsfSjjzjhvPOo4/kwKfU/c+PGTn/dQrfe6mxZWbB6NaxaRdjmzdx3W7Sz/5z/wtJPi7LnN2zMzjYnUW/pPKLysotm9n2Nqzm7yffszo7m1+zWPJ9zM5s3wz83j6QlfxBJLlHkEEUOC+nKcB4DYB4p1GUrEeSQQy5/k8MbMoT7Gv6bunVh5cY4YjUTAXp7l8PzWO7UtDRISzsQX0QU6ZtjyKg/gDoaS9voxtDuSKRRQ8IaNSCicQPCTunpfGi0agXff3+gBqtf31kgvlCbNjBtGgCakUHBJ58Uv7PLy3NmAoiNhfT0A1vHjk6GNWvg7bedysrbmjVw5JHwv//B3LnFK5Xk5KJKxfeDu7CiDPgHd0GB8++f6Xkg07gxIkK/8HAKPvuM8NzcA3dX06cTNm5cja4woPRKYxtwBDDfO7GEBu/WwJ8BjsuYSuM9En13x45IYmLJI9Hdiokpamwv5uOP4fffYeVKWLWK8FWraLJzJ/prNOw+8K07gjwu2flv5zy9ezH6g5vZuhUaXfgT4RmbyBFPlVEQiTbN4eL2sHs37F18JNnZzcjMi2JfXhT7c6P4ruBk/voL/voLRuNMQJ1LJC3YzHBepQ4H5lnZSxzXMZFUUthJQ7LzYpwJg94CiANmHlzWB522mejoaKKjTyIqCqKjKfYzKsqpP5yfys6d4fRq/AiPbn2EgvBowvKz+Vfivfw4PIfWrROIijqeiIjjiYiAiJed3msREVcRcf9V1MnZRcPdG6i/M536uzbw69xWhH0Dx874lY4z3iV6X/E5U2d8lIXERNPq84kcs+JbEr6cRnhBflFFOeizGfx0zThWP/8Zsb+vJjw3i7DsTMJzs8hv0Ii/ht9LWBg0fe4eYn79ibCcLCQ7E8nOIq9Ne3a89A4i0GxwN6J+WYp4tYvlpPRl9wezEIEGl1/pVBhewlTJXJGO1ksk4o1JTkNX27ZoUjISGYEIRRv4/z3YxF9vWRF5F2iiqn1KPYHIXGC7ql5cCfEdki5duujixYsrdGxqamqNf/ZYXlbmyqcZGRQkJxPu9WGTHxXlfOtOTDzk82dlwd9/O1/Qd+92ft+zB3I2ZjDkn62Jyj9QWWWHxXDn+evYQiKZmc6x2dnOT+8tO/vAlpPjfLmuiOZsoTXrWU9rtpJwyGUFqMffJLGBZNJJYAsTGQ7As4zkWv5DPfYUy7+L+pzFTB5gNAOYAUABQiax/EJHuvI9AJO4ik78TBYxZBJLFjGsoAP38iQAI3mWJmwv2pdJLOkk8xnOI7shTOUdLiXGq4deNpEksZGdNGQ/dQj3PNLMJYJ0knmRW3mZWwgjnz7MYQ1tSCeZ/BK+23tXIAdXLM4XItUCdu8OIyam/HdVIrJEVbuUtK+0O40Xgfki8gxwj2e2W++TRgBPAylAj3IGdBNwJ5AI/ALcrqrf+MmbTMnLyZ6lqp+X57rGBJNve0pYbCwFmZkH2lMO5W7HIybG2Zo397nurGV8ds7Zxdpxpg84i7OvXka/fuW7bm5u8UolM7N4Wk7OgQrG83SG7GwlLy+B3NwET7qSlyfk5kJe3oGtsFLKy3OOLSiA/HzndeHv3ltBQX0KCo4lJ/9Y1ufDGQVOvo/znyM1607eX3wEMXrg7ipasonrkMwTvM3oAqeyyNFIClRQhbbqnPcxfQ1V51yqFP2e6Pn9bf1nUbp3vgbqvN+z887hxqx/My5/BLlEEUkON4WNY1dkE8IIo7Vupo2u5khdw5EFq2mjq9ktDQkXSNaNfF5wlvNeE8F6WrOatozlNubQlxgyaaGb2UAS+USUMMuzFFXQbElm1q+BbcfxW2moapqI3IVTMVwuInOAjZ7drYA+QBPgXlVN83Oag4jIRcBYnC698z0/Z4pIB1XdWMqh/YFlXq//cntNY6oD3/YUSU8nLDnZXXtKAK9brnacEkRGOlvduuWKoozXgVVYUc6YcFaxinLmgLP4v+GeDg+V9rxHSE39lqysFsQet5Y6nt5TFy9bxrCY+Z4P7+ae7bSioy4AXgfISkQXfQOrVxOxejVt16ym7Zo1nP3QfnQw6NeLCT+9JxoZCa1bo0e2Qdu0Jf/a65m9cSM5r/6bwbNnUhARSdjRuQFvx/H7eKoog8jpwN1ATw40eGcBXwNPq+qX5bqgyELgJ1W9zittNfChqt5bQv5knDuNk1S1XM+a7PFU+ViZq0awx2kUlrmm9+Ipi3fvqcKKsiq7vVbav/OWLTBjhtMpYPXqAz9nz0Zbt0ZbtSIs78CDoYo8/qzo4ykAVHUeMM8zGrwxzleE7apa7kWZRCQKZ5bcZ3x2zQZOKePwj0QkBlgNPK+qH5b3+sZUB+We2beGXzdYAt7hoZwq7f1OSICrry6e5nlOJgsXOj0SvCqNsNhYJD3daXgPANdzT3kqiW2HeL0mQDjOSoDetuLTK9DLXuD/gG+BPOAc4D0RGaqqb/lmFpHh4LSGNW/enNTU1AoFunfv3gofW1NZmWsHK3PoivrrL7rm5hbv3pyZycKMDHICVX5VrbINaIEzoryHT/rDwKpynGc8ziOuUvN17txZK2revHkVPramsjLXDlbm0FRQUKAzZ87UqUOGaF5UlObGxWleVJROHTJEZ86cqQUFBa7PBSxWP5+rVT1n53actcZ9+9s14+C7j9IsBNoGKihjjKnpfJdQ/umpp4ovoRygx2Plnhr9UKhqjogswel59YHXrj7A1HKc6nicJWeNMcZ4VEU7TpVWGh7PAW+KyPc47RQ34Dy2egVARJ4ATlbVMz2vhwK5wFKcCX4GATfj9OgyxhjjpbI7PFR5paGq74lIY+ABnMF9PwNn64HVAROBI30OewBIwnm09RtwtZbQCG6MMaZyBeNOA1Udj9OYXdK+YT6vX8cz5sUYY0xw2eKVxhhjXLNKwxhjjGtlTiNSk3lWFdxQZsaSNaH4uiG1gZW5drAy1w6HUuYkVW1a0o6QrjQOhYgsVj9zr4QqK3PtYGWuHSqrzPZ4yhhjjGtWaRhjjHHNKg3/JgQ7gCCwMtcOVubaoVLKbG0axhhjXLM7DWOMMa5ZpWGMMcY1qzR8iMhNIrJeRLJEZImI9Ah2TBUlIj1FZJqI/CEiKiLDfPaLiIwSkc0ikikiqSLS0SdPtIi8JCLbRWSf53yHVWlBykFE7hWRRSKyW0T+FJFPRaSTT56QKreI3CwiP3nKvFtE0kRkgNf+kCqvLxG5z/P3/bJXWsiV2VMe9dm2eO2vkjJbpeFFRC4CxgKPAycAC4CZItIqqIFVXDzOhJC3AZkl7L8L+CdwC3ASzsqMc0SkrleeF4DzgUuAHkA94DNxlv+tjlJw5jU7BTgDZ7XHuSLSyCtPqJV7E86szycCXYAvgY9F5FjP/lArbxER6QZcB/zksytUy/wrzqSuhdsxXvuqpsz+VmeqjRvO4k4TfdJWA08EO7YAlG0vMMzrteCsSXK/V1ossAe43vO6PpADXOaV53CcKer7BbtMLssdjzM78qBaVu6/gOtDubyeuNfifDlIBV4O5X9jYBTws599VVZmu9PwEJEooDMw22fXbJxvraGmNc4KikXlVdVM4GsOlLczEOmT53dgJTXnPamLc0e90/M6pMstIuEicjFOZbmA0C7vBOBDVf3SJz2Uy3yE53HzehGZIiJHeNKrrMxWaRzQBAjn4GVnt3Lw8rShoLBMpZU3Aedbuu/8NTXpPRkL/AikeV6HZLlF5BgR2Qtk4yxoNkRVlxO65b0OaAM8WMLukCwzzpOQYcBZOI/kEoAF4qxPVGVlDsp6GtWc78AVKSEtlFSkvDXiPRGR54DTgNNUNd9nd6iV+1ecZZAb4Dyzfl1EUrz2h0x5RaQdTrtjD1XNKSVryJQZQFVner8Wke+AdcBQ4LvCbD6HBbzMdqdxwHacWti3xm3GwbV3KCjsdVFaebfg3H01KSVPtSQiz+M09p2hquu8doVkuVU1R1XXqOpiVb0X5+7qDkKzvN1xYv1ZRPJEJA/oBdzk+X2HJ18olfkgqroX+AVoSxX+O1ul4eH5xrIE6OOzqw/Os+FQsx7nj6iovCISg9OjorC8S3DWZ/fOcxjQnmr8nojIWOBSnApjlc/ukC23jzAgmtAs78c4vYaO99oWA1M8v/9G6JX5IJ4yHY3TAF51/87B7hFQnTbgIpzeBdd63sixOL2OkoIdWwXLE8+B/1T7gYc8v7fy7L8b2A2cB3TC+U+3GajrdY5/A38AvXG6Ic/D+RYbHuzy+SnzOE+ZzsD51lW4xXvlCalyA096PhyScT5Mn8DpEXNWKJbXz3uQiqf3VKiWGXgG546qNdAV+MxTxqSqLHPQ34jqtgE3Aek4DYpLgJ7BjukQypKC86zSd5vs2S843fgygCzgK6CTzzligJdwbvn3A58Chwe7bKWUuaTyKjDKK09IlRuYjLPYWDZO3/y5eHWhDLXy+nkPfCuNkCuzVyWQ4/ngnwp0qOoy24SFxhhjXLM2DWOMMa5ZpWGMMcY1qzSMMca4ZpWGMcYY16zSMMYY45pVGsYYY1yzSsNUWyLSXUTe9ywqkyMiO0RkjogMLZz/X0SGeRajSfY6Ll1EJvuca5CILBdncS0VkQYiEiYiL4hIhogUiMjHlViWZClhIawS8hWWp01lxVJRInKuiIwsIT3FE3PvYMRlqpZNWGiqJRG5HXgOZ0Ghu3EGrzUE+uKMat0FfOLn8CE4I2MLzxUBvI0zVcLNOIOj9gAX4CxQ9U+cWXB3HHQm4+1cnJHEzwU5DhNEVmmYakdEeuJ8ML2sqrf67P7EM3ttnL/jVXWpT1JLnHU13lfVr72u097z6wuqWhCAuKNVNftQz2NMdWaPp0x1dA/OynN3lbRTVdeqqu/ynkW8H0+JyCicaWEA/ut5jJIqIuk4Uy4A5Hs/OhKRRBF5w7OOcrY4629f7nONwsdIPUXkAxHZhbPeASJSR0TGex6n7RWRaUBA154WketEZJnncdt2Efmvz5K2eOIbLSK3ehbt2SMiX8nB60aHe/JliMh+EflSRI72HD/Kk2cyzhTcLeXA+tTpPmHVEZGXPfH8KSJviUiDQJbbBJ/daZhqxdNWkQJ8rKpZATjlf3DWSf8AGA1Mx3l0FQ3cirOoTXdP3rUiEoczZ09D4D7gd+By4E0RqaOqE3zO/zbwLs6jrsL/T6/iTH75CLAIZ1bRdwJQFgBE5EmcR2ovAnfi3EmNBjqJyClafO2Qy3HW2rgNiALG4NytHa2qeZ48j3jKOgZn3qoTgWk+l30MaIqz9vQ5njTfu6qxOJPoXQq0A57GWW5g6KGU11QvVmmY6qYJztrGGwJxMlXdJCI/el6uVdXCxWoQkT88ebzTRuCsT3C6qqZ6kmeKSHNgtIj81+dD+UNVvcvr+HY4H5r3q+qTnuTZIhIP3HCo5fE0+N8JPKKqj3ql/wbMBwbhTB1eKBcYqKq5nnzgVKAn46z61hC4HXhFVe/2HDNHRHKBZwtPoqprReRPIMf7/fLxtare4vl9tue9uFZEhqlNchcy7PGUMcX1BP7wqjAKvYXzTbuDT/r/fF53xfl/9b5P+pQAxdfHc/63RSSicMN5NLYbJ35vcworDI/lnp+tPD+PwWkf+sDnuA8rENt0n9fLce7omlfgXKaasjsNU93sADKBpCBdvxHO1NK+tnjt9+abN9Hzs6S1mgOhmefnGj/7G/u8/svndeEjpRjPz8J4t/nkq0i8ZV3LhACrNEy1oqp5IpIK9AlSb6S/cJ7H+ypcRtO3W67vY5fCSqQ5zvrNeL0OhMLr9wV2lrLfrcJ4m+EsHVrI7g5MiezxlKmOnsT5xjympJ0i0lpEjq2ka38FHCYip/qkX4rzbXxlGccvxFk170Kf9IsDEx5zPOdvpc564L7b+nKebzmwD/iHT7rva3DuHGLLH7IJJXanYaodVf3aM/L4Oc9YisnARpweTWfiLMd7KeC32+0hmIzT0+gjEbkf2ARchtOWcL1PI3hJsf8qIu8Aj4pIGAd6T51dzjj6i8gWn7S/VXWOiDwFvOxpaP4KZ5W2wz3X+Y+qznN7EVXdKSIvAPeJyB4O9J66xpPFe/zKCqCRiNyIsyZ3lqoux9QqVmmYaklVXxCR74E7cNZGboIzinsxcD3OMpWVcd19ItILp7vokziDAn8FrlDVt1ye5nqcteX/D6eb65c4ldz8coTyUglpv+As33mfiKzEGd1+M84jst+BL4DV5bhGoYdxlgq9Bqcb8kKcrsjfAn975fsP0A14HGiA08MtuQLXMzWYLfdqjDmIiPwDpwdYT1X9JtjxmOrDKg1jajkR6QoMwLnDyAI644zK/xU4xcZYGG/2eMoYsxdnfMfNQD2cBv/3gXutwjC+7E7DGGOMa9bl1hhjjGtWaRhjjHHNKg1jjDGuWaVhjDHGNas0jDHGuGaVhjHGGNf+H1RoZbCHpRFqAAAAAElFTkSuQmCC\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -384,7 +370,7 @@ }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -407,7 +393,7 @@ "par_expdata = par_exp.run(backend)\n", "\n", "# View result\n", - "par_expdata" + "print(par_expdata)" ] }, { @@ -421,7 +407,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -430,108 +416,108 @@ "text": [ "---------------------------------------------------\n", "Experiment: RBExperiment\n", - "Experiment ID: ba4e4b75-3802-424e-8d5c-3e7928fa9c95\n", - "Status: COMPLETE\n", + "Experiment ID: 6ca546fc-b1c2-42b6-904b-b8b1bf465d4b\n", + "Status: DONE\n", "Circuits: 140\n", "Analysis Results: 1\n", "---------------------------------------------------\n", "Last Analysis Result\n", - "- popt: [0.45163202 0.99592734 0.53346007]\n", - "- popt_keys: None\n", - "- popt_err: [2.76362095e-04 4.69836111e-06 2.81882215e-04]\n", - "- pcov: [[ 7.63760076e-08 1.21900813e-09 -7.74978656e-08]\n", - " [ 1.21900813e-09 2.20745971e-11 -1.26382118e-09]\n", - " [-7.74978656e-08 -1.26382118e-09 7.94575830e-08]]\n", - "- reduced_chisq: 121.7024893164993\n", + "- popt: [0.46693859 0.99874811 0.51804455]\n", + "- popt_keys: ['a', 'alpha', 'b']\n", + "- popt_err: [0.13640766 0.00047292 0.13743453]\n", + "- pcov: [[ 1.86070504e-02 6.41705166e-05 -1.87442876e-02]\n", + " [ 6.41705166e-05 2.23655594e-07 -6.47148737e-05]\n", + " [-1.87442876e-02 -6.47148737e-05 1.88882507e-02]]\n", + "- reduced_chisq: 0.11803581788941118\n", "- dof: 11\n", "- xrange: [1.0, 500.0]\n", - "- EPC: 0.002036328730827597\n", - "- EPC_err: 2.358787086606496e-06\n", - "- plabels: ['A', 'alpha', 'B'] \n", + "- EPC: 0.0006259460054571786\n", + "- EPC_err: 0.00023675759358046116\n", + "- success: True \n", "\n", "---------------------------------------------------\n", "Experiment: RBExperiment\n", - "Experiment ID: 93c7220c-dbd1-43a8-bb60-e8bcd8b6fbfa\n", - "Status: COMPLETE\n", + "Experiment ID: d3414e4d-90f1-4a19-8b44-4da6972e10fb\n", + "Status: DONE\n", "Circuits: 140\n", "Analysis Results: 1\n", "---------------------------------------------------\n", "Last Analysis Result\n", - "- popt: [0.47169909 0.99688744 0.51927115]\n", - "- popt_keys: None\n", - "- popt_err: [3.22156128e-04 3.36318925e-06 3.23899863e-04]\n", - "- pcov: [[ 1.03784571e-07 1.05691293e-09 -1.04281971e-07]\n", - " [ 1.05691293e-09 1.13110419e-11 -1.06516581e-09]\n", - " [-1.04281971e-07 -1.06516581e-09 1.04911121e-07]]\n", - "- reduced_chisq: 698.3818845120279\n", + "- popt: [0.5002357 0.99904187 0.49014021]\n", + "- popt_keys: ['a', 'alpha', 'b']\n", + "- popt_err: [0.1822092 0.00043693 0.18345169]\n", + "- pcov: [[ 3.32001915e-02 7.93933245e-05 -3.34245179e-02]\n", + " [ 7.93933245e-05 1.90904465e-07 -7.99713221e-05]\n", + " [-3.34245179e-02 -7.99713221e-05 3.36545220e-02]]\n", + "- reduced_chisq: 0.1070511551604703\n", "- dof: 11\n", "- xrange: [1.0, 500.0]\n", - "- EPC: 0.0015562786871028411\n", - "- EPC_err: 1.686845026470112e-06\n", - "- plabels: ['A', 'alpha', 'B'] \n", + "- EPC: 0.00047906273060688287\n", + "- EPC_err: 0.00021867259321739114\n", + "- success: True \n", "\n", "---------------------------------------------------\n", "Experiment: RBExperiment\n", - "Experiment ID: adfd1b3c-4435-4c95-aa43-b67a8d230e16\n", - "Status: COMPLETE\n", + "Experiment ID: 8c888438-1d75-483c-9d8f-fdd2b2a34560\n", + "Status: DONE\n", "Circuits: 140\n", "Analysis Results: 1\n", "---------------------------------------------------\n", "Last Analysis Result\n", - "- popt: [0.48489571 0.99685652 0.50226562]\n", - "- popt_keys: None\n", - "- popt_err: [4.67226100e-04 4.51247210e-06 4.74594366e-04]\n", - "- pcov: [[ 2.18300229e-07 2.00381319e-09 -2.21544151e-07]\n", - " [ 2.00381319e-09 2.03624045e-11 -2.05160852e-09]\n", - " [-2.21544151e-07 -2.05160852e-09 2.25239812e-07]]\n", - "- reduced_chisq: 554.2470051270237\n", + "- popt: [0.55973516 0.99912126 0.42700616]\n", + "- popt_keys: ['a', 'alpha', 'b']\n", + "- popt_err: [0.27152143 0.00050462 0.27233487]\n", + "- pcov: [[ 7.37238867e-02 1.36774520e-04 -7.39434311e-02]\n", + " [ 1.36774520e-04 2.54645379e-07 -1.37208230e-04]\n", + " [-7.39434311e-02 -1.37208230e-04 7.41662805e-02]]\n", + "- reduced_chisq: 0.12636835483455555\n", "- dof: 11\n", "- xrange: [1.0, 500.0]\n", - "- EPC: 0.0015717396315151344\n", - "- EPC_err: 2.263350847455984e-06\n", - "- plabels: ['A', 'alpha', 'B'] \n", + "- EPC: 0.00043937121868359297\n", + "- EPC_err: 0.00025253391107906575\n", + "- success: True \n", "\n", "---------------------------------------------------\n", "Experiment: RBExperiment\n", - "Experiment ID: 842fcff6-662c-47c2-a024-e871ac1b9c5b\n", - "Status: COMPLETE\n", + "Experiment ID: b686a25a-bfc2-497a-8ce2-afc32648d2f0\n", + "Status: DONE\n", "Circuits: 140\n", "Analysis Results: 1\n", "---------------------------------------------------\n", "Last Analysis Result\n", - "- popt: [0.47796991 0.98417873 0.51016471]\n", - "- popt_keys: None\n", - "- popt_err: [8.68826944e-05 1.05557314e-05 7.66890971e-05]\n", - "- pcov: [[ 7.54860259e-09 3.02649695e-10 -5.67513974e-09]\n", - " [ 3.02649695e-10 1.11423466e-10 -4.31459400e-10]\n", - " [-5.67513974e-09 -4.31459400e-10 5.88121761e-09]]\n", - "- reduced_chisq: 422.49065309169913\n", + "- popt: [0.48003187 0.99469758 0.51095063]\n", + "- popt_keys: ['a', 'alpha', 'b']\n", + "- popt_err: [0.01282506 0.0003712 0.01354442]\n", + "- pcov: [[ 1.64482244e-04 3.76084285e-06 -1.64591939e-04]\n", + " [ 3.76084285e-06 1.37790112e-07 -4.53772965e-06]\n", + " [-1.64591939e-04 -4.53772965e-06 1.83451314e-04]]\n", + "- reduced_chisq: 0.03699962727664218\n", "- dof: 11\n", "- xrange: [1.0, 500.0]\n", - "- EPC: 0.007910633750468743\n", - "- EPC_err: 5.362710602598715e-06\n", - "- plabels: ['A', 'alpha', 'B'] \n", + "- EPC: 0.0026512122080561418\n", + "- EPC_err: 0.00018658983090838707\n", + "- success: True \n", "\n", "---------------------------------------------------\n", "Experiment: RBExperiment\n", - "Experiment ID: 11b1662b-3f18-43cb-8e35-8df96ec6003c\n", - "Status: COMPLETE\n", + "Experiment ID: 09597176-8112-422b-894f-c95a20dd15e9\n", + "Status: DONE\n", "Circuits: 140\n", "Analysis Results: 1\n", "---------------------------------------------------\n", "Last Analysis Result\n", - "- popt: [0.46057898 0.99499266 0.52740683]\n", - "- popt_keys: None\n", - "- popt_err: [9.96213286e-05 2.52493125e-06 1.00644325e-04]\n", - "- pcov: [[ 9.92440911e-09 2.10349103e-10 -9.98184864e-09]\n", - " [ 2.10349103e-10 6.37527782e-12 -2.16645073e-10]\n", - " [-9.98184864e-09 -2.16645073e-10 1.01292802e-08]]\n", - "- reduced_chisq: 3646.3294248027814\n", + "- popt: [0.46814847 0.99839983 0.52017324]\n", + "- popt_keys: ['a', 'alpha', 'b']\n", + "- popt_err: [0.10497103 0.00047757 0.10608133]\n", + "- pcov: [[ 1.10189166e-02 4.97107495e-05 -1.11326896e-02]\n", + " [ 4.97107495e-05 2.28073402e-07 -5.03167306e-05]\n", + " [-1.11326896e-02 -5.03167306e-05 1.12532482e-02]]\n", + "- reduced_chisq: 0.043914308686492876\n", "- dof: 11\n", "- xrange: [1.0, 500.0]\n", - "- EPC: 0.0025036719357949266\n", - "- EPC_err: 1.2688190380616948e-06\n", - "- plabels: ['A', 'alpha', 'B'] \n", + "- EPC: 0.0008000826861901955\n", + "- EPC_err: 0.00023916786387579025\n", + "- success: True \n", "\n" ] } @@ -545,9 +531,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python [conda env:qiskit-dev]", "language": "python", - "name": "python3" + "name": "conda-env-qiskit-dev-py" }, "language_info": { "codemirror_mode": { @@ -559,7 +545,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.6" + "version": "3.7.7" } }, "nbformat": 4, diff --git a/qiskit_experiments/analysis/curve_fitting.py b/qiskit_experiments/analysis/curve_fitting.py index ead843e82b..a87ccf58b8 100644 --- a/qiskit_experiments/analysis/curve_fitting.py +++ b/qiskit_experiments/analysis/curve_fitting.py @@ -223,11 +223,11 @@ def multi_curve_fit( wsigma[idxs[i]] = sigma[idxs[i]] / np.sqrt(weights[i]) # Define multi-objective function - def f(x, *params): + def f(x, *args, **kwargs): y = np.zeros(x.size) for i in range(num_funcs): xi = x[idxs[i]] - yi = funcs[i](xi, *params) + yi = funcs[i](xi, *args, **kwargs) y[idxs[i]] = yi return y diff --git a/qiskit_experiments/analysis/plotting.py b/qiskit_experiments/analysis/plotting.py index a64256b712..fdaf0733ef 100644 --- a/qiskit_experiments/analysis/plotting.py +++ b/qiskit_experiments/analysis/plotting.py @@ -35,10 +35,10 @@ def plot_curve_fit( ): """Generate plot of a curve fit analysis result. - Wraps ``matplotlib.pyplot.plot``. + Wraps :func:`matplotlib.pyplot.plot`. Args: - func: the fit funcion for curve_fit. + func: the fit function for curve_fit. result: an AnalysisResult from curve_fit. confidence_interval: if True plot the confidence interval from popt_err. ax (matplotlib.axes.Axes): Optional, a matplotlib axes to add the plot to. diff --git a/qiskit_experiments/base_analysis.py b/qiskit_experiments/base_analysis.py index 61cb570296..6105ca7088 100644 --- a/qiskit_experiments/base_analysis.py +++ b/qiskit_experiments/base_analysis.py @@ -16,17 +16,38 @@ from abc import ABC, abstractmethod from typing import List, Tuple +from qiskit.providers.options import Options from qiskit.exceptions import QiskitError -from .experiment_data import ExperimentData, AnalysisResult +from qiskit_experiments.experiment_data import ExperimentData, AnalysisResult + +# pylint: disable = unused-import +from qiskit_experiments.matplotlib import pyplot class BaseAnalysis(ABC): - """Base Analysis class for analyzing Experiment data.""" + """Base Analysis class for analyzing Experiment data. + + The data produced by experiments (i.e. subclasses of BaseExperiment) + are analyzed with subclasses of BaseExperiment. The analysis is + typically run after the data has been gathered by the experiment. + For example, an analysis may perform some data processing of the + measured data and a fit to a function to extract a parameter. + + When designing Analysis subclasses default values for any kwarg + analysis options of the `run` method should be set by overriding + the `_default_options` class method. When calling `run` these + default values will be combined with all other option kwargs in the + run method and passed to the `_run_analysis` function. + """ # Expected experiment data container for analysis __experiment_data__ = ExperimentData + @classmethod + def _default_options(cls) -> Options: + return Options() + def run( self, experiment_data: ExperimentData, @@ -34,7 +55,7 @@ def run( return_figures: bool = False, **options, ): - """Run analysis and update stored ExperimentData with analysis result. + """Run analysis and update ExperimentData with analysis result. Args: experiment_data: the experiment data to analyze. @@ -43,14 +64,13 @@ def run( return_figures: if true return a pair of ``(analysis_results, figures)``, otherwise return only analysis_results. - options: kwarg options for analysis function. + options: additional analysis options. See class documentation for + supported options. Returns: - AnalysisResult: the output of the analysis that produces a - single result. List[AnalysisResult]: the output for analysis that produces multiple results. - tuple: If ``return_figures=True`` the output is a pair + Tuple: If ``return_figures=True`` the output is a pair ``(analysis_results, figures)`` where ``analysis_results`` may be a single or list of :class:`AnalysisResult` objects, and ``figures`` may be None, a single figure, or a list of figures. @@ -63,13 +83,18 @@ def run( f"Invalid experiment data type, expected {self.__experiment_data__.__name__}" f" but received {type(experiment_data).__name__}" ) + # Get analysis options + analysis_options = self._default_options() + analysis_options.update_options(**options) + analysis_options = analysis_options.__dict__ + # Run analysis # pylint: disable=broad-except try: - analysis_results, figures = self._run_analysis(experiment_data, **options) + analysis_results, figures = self._run_analysis(experiment_data, **analysis_options) analysis_results["success"] = True - except Exception: - analysis_results = AnalysisResult(success=False) + except Exception as ex: + analysis_results = AnalysisResult(success=False, error_message=ex) figures = None # Save to experiment data @@ -88,18 +113,19 @@ def run( @abstractmethod def _run_analysis( - self, data: ExperimentData, **options - ) -> Tuple[List[AnalysisResult], List["matplotlib.figure.Figure"]]: + self, experiment_data: ExperimentData, **options + ) -> Tuple[List[AnalysisResult], List["pyplot.Figure"]]: """Run analysis on circuit data. Args: experiment_data: the experiment data to analyze. - options: kwarg options for analysis function. + options: additional options for analysis. By default the fields and + values in :meth:`options` are used and any provided values + can override these. Returns: - tuple: A pair ``(analysis_results, figures)`` where - ``analysis_results`` may be a single or list of - AnalysisResult objects, and ``figures`` is a list of any - figures for the experiment. + A pair ``(analysis_results, figures)`` where ``analysis_results`` + may be a single or list of AnalysisResult objects, and ``figures`` + is a list of any figures for the experiment. """ pass diff --git a/qiskit_experiments/base_experiment.py b/qiskit_experiments/base_experiment.py index 3cafeacd19..016b48fde4 100644 --- a/qiskit_experiments/base_experiment.py +++ b/qiskit_experiments/base_experiment.py @@ -14,34 +14,18 @@ """ from abc import ABC, abstractmethod -from typing import Union, Iterable, Optional, Tuple, List +from typing import Iterable, Optional, Tuple, List +import copy from numbers import Integral from qiskit import transpile, assemble, QuantumCircuit -from qiskit.exceptions import QiskitError +from qiskit.providers.options import Options from qiskit.providers.backend import Backend from qiskit.providers.basebackend import BaseBackend as LegacyBackend +from qiskit.exceptions import QiskitError from .experiment_data import ExperimentData -_TRANSPILE_OPTIONS = { - "basis_gates", - "coupling_map", - "backend_properties", - "initial_layout", - "layout_method", - "routing_method", - "translation_method", - "scheduling_method", - "instruction_durations", - "dt", - "seed_transpiler", - "optimization_level", - "pass_manager", - "callback", - "output_name", -} - class BaseExperiment(ABC): """Base Experiment class @@ -61,26 +45,13 @@ class BaseExperiment(ABC): # ExperimentData class for experiment __experiment_data__ = ExperimentData - # Custom default transpiler options for experiment subclasses - __transpile_defaults__ = {"optimization_level": 0} - - # Custom default run (assemble) options for experiment subclasses - __run_defaults__ = {} - - def __init__( - self, - qubits: Union[int, Iterable[int]], - experiment_type: Optional[str] = None, - circuit_options: Optional[Iterable[str]] = None, - ): + def __init__(self, qubits: Iterable[int], experiment_type: Optional[str] = None): """Initialize the experiment object. Args: - qubits: the number of qubits or list of physical qubits - for the experiment. + qubits: the number of qubits or list of physical qubits for + the experiment. experiment_type: Optional, the experiment type string. - circuit_options: Optional, list of kwarg names for - the subclassed `circuit` method. Raises: QiskitError: if qubits is a list and contains duplicates. @@ -99,62 +70,88 @@ def __init__( print(self._num_qubits, self._physical_qubits) raise QiskitError("Duplicate qubits in physical qubits list.") - # Store options and values - self._circuit_options = set(circuit_options) if circuit_options else set() + # Experiment options + self._experiment_options = self._default_experiment_options() + self._transpile_options = self._default_transpile_options() + self._run_options = self._default_run_options() + self._analysis_options = self._default_analysis_options() + + # Set initial layout from qubits + self._transpile_options.initial_layout = self._physical_qubits def run( self, - backend: "Backend", + backend: Backend, analysis: bool = True, experiment_data: Optional[ExperimentData] = None, - **kwargs, + **run_options, ) -> ExperimentData: """Run an experiment and perform analysis. Args: backend: The backend to run the experiment on. - analysis: If True run analysis on experiment data. - experiment_data: Optional, add results to existing experiment data. - If None a new ExperimentData object will be returned. - kwargs: keyword arguments for self.circuit, qiskit.transpile, and backend.run. + analysis: If True run analysis on the experiment data. + experiment_data: Optional, add results to existing + experiment data. If None a new ExperimentData object will be + returned. + run_options: backend runtime options used for circuit execution. Returns: - ExperimentData: the experiment data object. + The experiment data object. """ - # NOTE: This method is intended to be overriden by subclasses if required. - # Create new experiment data if experiment_data is None: experiment_data = self.__experiment_data__(self, backend=backend) - # Filter kwargs - run_options = self.__run_defaults__.copy() - circuit_options = {} - for key, value in kwargs.items(): - if key in _TRANSPILE_OPTIONS or key in self._circuit_options: - circuit_options[key] = value - else: - run_options[key] = value - - # Generate and run circuits - circuits = self.transpiled_circuits(backend, **circuit_options) + # Generate and transpile circuits + circuits = transpile(self.circuits(backend), backend, **self.transpile_options.__dict__) + + # Run circuits on backend + run_opts = copy.copy(self.run_options) + run_opts.update_options(**run_options) + run_opts = run_opts.__dict__ + if isinstance(backend, LegacyBackend): - qobj = assemble(circuits, backend=backend, **run_options) + qobj = assemble(circuits, backend=backend, **run_opts) job = backend.run(qobj) else: - job = backend.run(circuits, **run_options) + job = backend.run(circuits, **run_opts) # Add Job to ExperimentData experiment_data.add_data(job) # Queue analysis of data for when job is finished if analysis and self.__analysis_class__ is not None: - # pylint: disable = not-callable - self.__analysis_class__().run(experiment_data, **kwargs) + self.run_analysis(experiment_data) # Return the ExperimentData future return experiment_data + def run_analysis(self, experiment_data, **options) -> ExperimentData: + """Run analysis and update ExperimentData with analysis result. + + Args: + experiment_data (ExperimentData): the experiment data to analyze. + options: additional analysis options. Any values set here will + override the value from :meth:`analysis_options` + for the current run. + + Returns: + The updated experiment data containing the analysis results and figures. + + Raises: + QiskitError: if experiment_data container is not valid for analysis. + """ + # Get analysis options + analysis_options = copy.copy(self.analysis_options) + analysis_options.update_options(**options) + analysis_options = analysis_options.__dict__ + + # Run analysis + analysis = self.analysis() + analysis.run(experiment_data, save=True, return_figures=False, **analysis_options) + return experiment_data + @property def num_qubits(self) -> int: """Return the number of qubits for this experiment.""" @@ -166,32 +163,19 @@ def physical_qubits(self) -> Tuple[int]: return self._physical_qubits @classmethod - def analysis(cls, **kwargs): - """Return the default Analysis class for the experiment. - - Returns: - BaseAnalysis: the analysis object. - - Raises: - QiskitError: if the experiment does not have a defaul - analysis class. - """ + def analysis(cls): + """Return the default Analysis class for the experiment.""" if cls.__analysis_class__ is None: - raise QiskitError( - f"Experiment {cls.__name__} does not define" " a default Analysis class" - ) + raise QiskitError(f"Experiment {cls.__name__} does not have a default Analysis class") # pylint: disable = not-callable - return cls.__analysis_class__(**kwargs) + return cls.__analysis_class__() @abstractmethod - def circuits( - self, backend: Optional[Backend] = None, **circuit_options - ) -> List[QuantumCircuit]: + def circuits(self, backend: Optional[Backend] = None) -> List[QuantumCircuit]: """Return a list of experiment circuits. Args: backend: Optional, a backend object. - circuit_options: kwarg options for the function. Returns: A list of :class:`QuantumCircuit`. @@ -201,59 +185,106 @@ def circuits( *N*-qubit experiment. The circuits mapped to physical qubits are obtained via the :meth:`transpiled_circuits` method. """ - # NOTE: Subclasses should override this method with explicit - # kwargs for any circuit options rather than use `**circuit_options`. - # This allows these options to have default values, and be - # documented in the methods docstring for the API docs. - - def transpiled_circuits( - self, backend: Optional[Backend] = None, **kwargs - ) -> List[QuantumCircuit]: - """Return a list of experiment circuits. + # NOTE: Subclasses should override this method using the `options` + # values for any explicit experiment options that effect circuit + # generation + + @classmethod + def _default_experiment_options(cls) -> Options: + """Default kwarg options for experiment""" + # Experiment subclasses should override this method to return + # an `Options` object containing all the supported options for + # that experiment and their default values. Only options listed + # here can be modified later by the `set_options` method. + return Options() + + @property + def experiment_options(self) -> Options: + """Return the options for the experiment.""" + return self._experiment_options + + def set_experiment_options(self, **fields): + """Set the experiment options. Args: - backend: Optional, a backend object to use as the - argument for the :func:`qiskit.transpile` - function. - kwargs: kwarg options for the :meth:`circuits` method, and - :func:`qiskit.transpile` function. + fields: The fields to update the options - Returns: - A list of :class:`QuantumCircuit`. + Raises: + AttributeError: If the field passed in is not a supported options + """ + for field in fields: + if not hasattr(self._experiment_options, field): + raise AttributeError( + f"Options field {field} is not valid for {type(self).__name__}" + ) + self._experiment_options.update_options(**fields) + + @classmethod + def _default_transpile_options(cls) -> Options: + """Default transpiler options for transpilation of circuits""" + # Experiment subclasses can override this method if they need + # to set specific default transpiler options to transpile the + # experiment circuits. + return Options(optimization_level=0) + + @property + def transpile_options(self) -> Options: + """Return the transpiler options for the :meth:`run` method.""" + return self._transpile_options + + def set_transpile_options(self, **fields): + """Set the transpiler options for :meth:`run` method. + + Args: + fields: The fields to update the options Raises: - QiskitError: if an initial layout is specified in the - kwarg options for transpilation. The initial - layout must be generated from the experiment. + QiskitError: if `initial_layout` is one of the fields. + """ + if "initial_layout" in fields: + raise QiskitError( + "Initial layout cannot be specified as a transpile option" + " as it is determined by the experiment physical qubits." + ) + self._transpile_options.update_options(**fields) - .. note:: - These circuits should be on qubits ``[0, .., N-1]`` for an - *N*-qubit experiment. The circuits mapped to physical qubits - are obtained via the :meth:`transpiled_circuits` method. + @classmethod + def _default_run_options(cls) -> Options: + """Default options values for the experiment :meth:`run` method.""" + return Options() + + @property + def run_options(self) -> Options: + """Return options values for the experiment :meth:`run` method.""" + return self._run_options + + def set_run_options(self, **fields): + """Set options values for the experiment :meth:`run` method. + + Args: + fields: The fields to update the options """ - # Filter kwargs to circuit and transpile options - circuit_options = {} - transpile_options = self.__transpile_defaults__.copy() - for key, value in kwargs.items(): - valid_key = False - if key in self._circuit_options: - circuit_options[key] = value - valid_key = True - if key in _TRANSPILE_OPTIONS: - transpile_options[key] = value - valid_key = True - if not valid_key: - raise QiskitError( - f"{key} is not a valid kwarg for" f" {self.circuits} or {transpile}" - ) + self._run_options.update_options(**fields) - # Generate circuits - circuits = self.circuits(backend=backend, **circuit_options) + @classmethod + def _default_analysis_options(cls) -> Options: + """Default options for analysis of experiment results.""" + # Experiment subclasses can override this method if they need + # to set specific analysis options defaults that are different + # from the Analysis subclass `_default_options` values. + if cls.__analysis_class__: + return cls.__analysis_class__._default_options() + return Options() - # Transpile circuits - if "initial_layout" in transpile_options: - raise QiskitError("Initial layout must be specified by the Experiement.") - transpile_options["initial_layout"] = self.physical_qubits - circuits = transpile(circuits, backend=backend, **transpile_options) + @property + def analysis_options(self) -> Options: + """Return the analysis options for :meth:`run` analysis.""" + return self._analysis_options - return circuits + def set_analysis_options(self, **fields): + """Set the analysis options for :meth:`run` method. + + Args: + fields: The fields to update the options + """ + self._analysis_options.update_options(**fields) diff --git a/qiskit_experiments/characterization/__init__.py b/qiskit_experiments/characterization/__init__.py index 3525ea9aa3..053799bacb 100644 --- a/qiskit_experiments/characterization/__init__.py +++ b/qiskit_experiments/characterization/__init__.py @@ -23,6 +23,7 @@ :toctree: ../stubs/ T1Experiment + T2StarExperiment Analysis @@ -32,5 +33,7 @@ :toctree: ../stubs/ T1Analysis + T2StarAnalysis """ from .t1_experiment import T1Experiment, T1Analysis +from .t2star_experiment import T2StarExperiment, T2StarAnalysis diff --git a/qiskit_experiments/characterization/t1_experiment.py b/qiskit_experiments/characterization/t1_experiment.py index e8dd1822cc..cfabf3018c 100644 --- a/qiskit_experiments/characterization/t1_experiment.py +++ b/qiskit_experiments/characterization/t1_experiment.py @@ -19,6 +19,7 @@ from qiskit.providers import Backend from qiskit.circuit import QuantumCircuit from qiskit.utils import apply_prefix +from qiskit.providers.options import Options from qiskit_experiments.base_experiment import BaseExperiment from qiskit_experiments.base_analysis import BaseAnalysis @@ -29,9 +30,34 @@ class T1Analysis(BaseAnalysis): - """T1 Experiment result analysis class.""" + """T1 Experiment result analysis class. + + Analysis Options: + + * t1_guess (float): Optional, an initial guess of T1. + * amplitude_guess (float): Optional, an initial guess of the + coefficient of the exponent. + * offset_guess (float): Optional, an initial guess of the offset. + * t1_bounds (list of two floats): Optional, lower bound and upper + bound to T1. + * amplitude_bounds (list of two floats): Optional, lower bound and upper + bound to the amplitude. + * offset_bounds (list of two floats): Optional, lower bound and + upper bound to the offset. + """ - # pylint: disable=arguments-differ, unused-argument + @classmethod + def _default_options(cls): + return Options( + t1_guess=None, + amplitude_guess=None, + offset_guess=None, + t1_bounds=None, + amplitude_bounds=None, + offset_bounds=None, + ) + + # pylint: disable=arguments-differ def _run_analysis( self, experiment_data, @@ -43,7 +69,6 @@ def _run_analysis( offset_bounds=None, plot=True, ax=None, - **kwargs, ) -> Tuple[AnalysisResult, List["matplotlib.figure.Figure"]]: """ Calculate T1 @@ -52,17 +77,15 @@ def _run_analysis( experiment_data (ExperimentData): the experiment data to analyze t1_guess (float): Optional, an initial guess of T1 amplitude_guess (float): Optional, an initial guess of the coefficient - of the exponent + of the exponent offset_guess (float): Optional, an initial guess of the offset - t1_bounds (list of two floats): Optional, lower bound and upper - bound to T1 - amplitude_bounds (list of two floats): Optional, lower bound and - upper bound to the amplitude + t1_bounds (list of two floats): Optional, lower bound and upper bound to T1 + amplitude_bounds (list of two floats): Optional, lower bound and upper + bound to the amplitude offset_bounds (list of two floats): Optional, lower bound and upper - bound to the offset - plot: If True generate a plot of fitted data. - ax: Optional, matplotlib axis to add plot to. - kwargs: Trailing unused function parameters + bound to the offset + plot (bool): Generator plot of exponential fit. + ax (AxesSubplot): Optional, axes to add figure to. Returns: The analysis result with the estimated T1 @@ -71,6 +94,7 @@ def _run_analysis( unit = data[0]["metadata"]["unit"] conversion_factor = data[0]["metadata"].get("dt_factor", None) qubit = data[0]["metadata"]["qubit"] + if conversion_factor is None: conversion_factor = 1 if unit == "s" else apply_prefix(1, unit) @@ -186,10 +210,20 @@ def _format_plot(cls, ax, analysis_result, qubit=None, add_label=True): class T1Experiment(BaseExperiment): - """T1 experiment class""" + """T1 experiment class. + + Experiment Options: + * delays: delay times of the experiments + * unit: Optional, unit of the delay times. Supported units are + 's', 'ms', 'us', 'ns', 'ps', 'dt'. + """ __analysis_class__ = T1Analysis + @classmethod + def _default_experiment_options(cls) -> Options: + return Options(delays=None, unit="s") + def __init__( self, qubit: int, @@ -211,11 +245,12 @@ def __init__( if len(delays) < 3: raise ValueError("T1 experiment: number of delays must be at least 3") - self._delays = delays - self._unit = unit + # Initialize base experiment super().__init__([qubit]) - # pylint: disable=arguments-differ + # Set experiment options + self.set_experiment_options(delays=delays, unit=unit) + def circuits(self, backend: Optional[Backend] = None) -> List[QuantumCircuit]: """ Return a list of experiment circuits @@ -229,8 +264,7 @@ def circuits(self, backend: Optional[Backend] = None) -> List[QuantumCircuit]: Raises: AttributeError: if unit is dt but dt parameter is missing in the backend configuration """ - - if self._unit == "dt": + if self.experiment_options.unit == "dt": try: dt_factor = getattr(backend.configuration(), "dt") except AttributeError as no_dt: @@ -238,11 +272,11 @@ def circuits(self, backend: Optional[Backend] = None) -> List[QuantumCircuit]: circuits = [] - for delay in self._delays: + for delay in self.experiment_options.delays: circ = QuantumCircuit(1, 1) circ.x(0) circ.barrier(0) - circ.delay(delay, 0, self._unit) + circ.delay(delay, 0, self.experiment_options.unit) circ.barrier(0) circ.measure(0, 0) @@ -250,10 +284,10 @@ def circuits(self, backend: Optional[Backend] = None) -> List[QuantumCircuit]: "experiment_type": self._type, "qubit": self.physical_qubits[0], "xval": delay, - "unit": self._unit, + "unit": self.experiment_options.unit, } - if self._unit == "dt": + if self.experiment_options.unit == "dt": circ.metadata["dt_factor"] = dt_factor circuits.append(circ) diff --git a/qiskit_experiments/characterization/t2star_experiment.py b/qiskit_experiments/characterization/t2star_experiment.py new file mode 100644 index 0000000000..cc580b8fde --- /dev/null +++ b/qiskit_experiments/characterization/t2star_experiment.py @@ -0,0 +1,263 @@ +# This code is part of Qiskit. +# +# (C) Copyright IBM 2021. +# +# This code is licensed under the Apache License, Version 2.0. You may +# obtain a copy of this license in the LICENSE.txt file in the root directory +# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0. +# +# Any modifications or derivative works of this code must retain this +# copyright notice, and modified files need to carry a notice indicating +# that they have been altered from the originals. +""" +T2Star Experiment class. +""" + +from typing import List, Optional, Union, Tuple, Dict +import numpy as np + +import qiskit +from qiskit.providers import Backend +from qiskit.circuit import QuantumCircuit +from qiskit.utils import apply_prefix +from qiskit.providers.options import Options +from qiskit_experiments.base_experiment import BaseExperiment +from qiskit_experiments.base_analysis import BaseAnalysis, AnalysisResult +from qiskit_experiments.analysis.curve_fitting import curve_fit, process_curve_data +from qiskit_experiments.analysis.data_processing import level2_probability +from qiskit_experiments.analysis import plotting +from ..experiment_data import ExperimentData + +# pylint: disable = invalid-name +class T2StarAnalysis(BaseAnalysis): + """T2Star Experiment result analysis class.""" + + @classmethod + def _default_options(cls): + return Options(user_p0=None, user_bounds=None) + + # pylint: disable=arguments-differ, unused-argument + def _run_analysis( + self, + experiment_data: ExperimentData, + user_p0: Optional[Dict[str, float]] = None, + user_bounds: Optional[Tuple[List[float], List[float]]] = None, + plot: bool = True, + ax: Optional["AxesSubplot"] = None, + **kwargs, + ) -> Tuple[AnalysisResult, List["matplotlib.figure.Figure"]]: + r"""Calculate T2Star experiment. + + The probability of measuring `+` is assumed to be of the form + :math:`f(t) = a\mathrm{e}^{-t / T_2^*}\cos(2\pi freq t + \phi) + b` + for unknown parameters :math:`a, b, freq, \phi, T_2^*`. + + Args: + experiment_data (ExperimentData): the experiment data to analyze + user_p0: contains initial values given by the user, for the + fit parameters :math:`(a, T_2^*, freq, \phi, b)` + User_bounds: lower and upper bounds on the parameters in p0, + given by the user. + The first tuple is the lower bounds, + The second tuple is the upper bounds. + For both params, the order is :math:`a, T_2^*, freq, \phi, b`. + plot: if True, create the plot, otherwise, do not create the plot. + ax: the plot object + **kwargs: additional parameters for curve fit. + + Returns: + The analysis result with the estimated :math:`T_2^*` and 'freq' (frequency) + The graph of the function. + """ + + def osc_fit_fun(x, a, t2star, freq, phi, c): + """Decay cosine fit function""" + return a * np.exp(-x / t2star) * np.cos(2 * np.pi * freq * x + phi) + c + + def _format_plot(ax, unit): + """Format curve fit plot""" + # Formatting + ax.tick_params(labelsize=10) + ax.set_xlabel("Delay (" + str(unit) + ")", fontsize=12) + ax.set_ylabel("Probability to measure |0>", fontsize=12) + + # implementation of _run_analysis + unit = experiment_data._data[0]["metadata"]["unit"] + conversion_factor = experiment_data._data[0]["metadata"].get("dt_factor", None) + if conversion_factor is None: + conversion_factor = 1 if unit == "s" else apply_prefix(1, unit) + xdata, ydata, sigma = process_curve_data( + experiment_data._data, lambda datum: level2_probability(datum, "0") + ) + + si_xdata = xdata * conversion_factor + t2star_estimate = np.mean(si_xdata) + + p0, bounds = self._t2star_default_params( + conversion_factor, user_p0, user_bounds, t2star_estimate + ) + fit_result = curve_fit( + osc_fit_fun, si_xdata, ydata, p0=list(p0.values()), sigma=sigma, bounds=bounds + ) + + if plot and plotting.HAS_MATPLOTLIB: + ax = plotting.plot_curve_fit(osc_fit_fun, fit_result, ax=ax) + ax = plotting.plot_scatter(si_xdata, ydata, ax=ax) + ax = plotting.plot_errorbar(si_xdata, ydata, sigma, ax=ax) + _format_plot(ax, unit) + figures = [ax.get_figure()] + else: + figures = None + + # Output unit is 'sec', regardless of the unit used in the input + analysis_result = AnalysisResult( + { + "t2star_value": fit_result["popt"][1], + "frequency_value": fit_result["popt"][2], + "stderr": fit_result["popt_err"][1], + "unit": "s", + "label": "T2*", + "fit": fit_result, + "quality": self._fit_quality( + fit_result["popt"], fit_result["popt_err"], fit_result["reduced_chisq"] + ), + } + ) + + analysis_result["fit"]["circuit_unit"] = unit + if unit == "dt": + analysis_result["fit"]["dt"] = conversion_factor + return analysis_result, figures + + def _t2star_default_params( + self, + conversion_factor, + user_p0=None, + user_bounds=None, + t2star_input=None, + ) -> Tuple[List[float], Tuple[List[float]]]: + """Default fit parameters for oscillation data. + + Note that :math:`T_2^*` and 'freq' units are converted to 'sec' and + will be output in 'sec'. + """ + if user_p0 is None: + a = 0.5 + t2star = t2star_input * conversion_factor + freq = 0.1 + phi = 0.0 + b = 0.5 + else: + a = user_p0["A"] + t2star = user_p0["t2star"] + t2star *= conversion_factor + freq = user_p0["f"] + phi = user_p0["phi"] + b = user_p0["B"] + freq /= conversion_factor + p0 = {"a_guess": a, "t2star": t2star, "f_guess": freq, "phi_guess": phi, "b_guess": b} + if user_bounds is None: + a_bounds = [-0.5, 1.5] + t2star_bounds = [0, np.inf] + f_bounds = [0.5 * freq, 1.5 * freq] + phi_bounds = [-np.pi, np.pi] + b_bounds = [-0.5, 1.5] + bounds = [ + [a_bounds[i], t2star_bounds[i], f_bounds[i], phi_bounds[i], b_bounds[i]] + for i in range(2) + ] + else: + bounds = user_bounds + return p0, bounds + + @staticmethod + def _fit_quality(fit_out, fit_err, reduced_chisq): + # pylint: disable = too-many-boolean-expressions + if ( + (reduced_chisq < 3) + and (fit_err[0] is None or fit_err[0] < 0.1 * fit_out[0]) + and (fit_err[1] is None or fit_err[1] < 0.1 * fit_out[1]) + and (fit_err[2] is None or fit_err[2] < 0.1 * fit_out[2]) + ): + return "computer_good" + else: + return "computer_bad" + + +class T2StarExperiment(BaseExperiment): + """T2Star experiment class""" + + __analysis_class__ = T2StarAnalysis + + def __init__( + self, + qubit: int, + delays: Union[List[float], np.array], + unit: str = "s", + osc_freq: float = 0.0, + experiment_type: Optional[str] = None, + ): + """Initialize the T2Star experiment class. + + Args: + qubit: the qubit under test + delays: delay times of the experiments + unit: Optional, time unit of `delays`. + Supported units: 's', 'ms', 'us', 'ns', 'ps', 'dt'. + The unit is used for both T2* and the frequency + osc_freq: the oscillation frequency induced using by the user + experiment_type: String indicating the experiment type. + Can be 'RamseyExperiment' or 'T2StarExperiment'. + """ + + self._qubit = qubit + self._delays = delays + self._unit = unit + self._osc_freq = osc_freq + super().__init__([qubit], experiment_type) + + def circuits(self, backend: Optional[Backend] = None) -> List[QuantumCircuit]: + """Return a list of experiment circuits. + + Each circuit consists of a Hadamard gate, followed by a fixed delay, + a phase gate (with a linear phase), and an additional Hadamard gate. + + Args: + backend: Optional, a backend object + + Returns: + The experiment circuits + + Raises: + AttributeError: if unit is dt but dt parameter is missing in the backend configuration + """ + if self._unit == "dt": + try: + dt_factor = getattr(backend._configuration, "dt") + except AttributeError as no_dt: + raise AttributeError("Dt parameter is missing in backend configuration") from no_dt + + circuits = [] + for delay in self._delays: + circ = qiskit.QuantumCircuit(1, 1) + circ.h(0) + circ.delay(delay, 0, self._unit) + circ.p(2 * np.pi * self._osc_freq, 0) + circ.barrier(0) + circ.h(0) + circ.barrier(0) + circ.measure(0, 0) + + circ.metadata = { + "experiment_type": self._type, + "qubit": self._qubit, + "osc_freq": self._osc_freq, + "xval": delay, + "unit": self._unit, + } + if self._unit == "dt": + circ.metadata["dt_factor"] = dt_factor + + circuits.append(circ) + + return circuits diff --git a/qiskit_experiments/composite/batch_experiment.py b/qiskit_experiments/composite/batch_experiment.py index b96c7dc474..c6d0a71ada 100644 --- a/qiskit_experiments/composite/batch_experiment.py +++ b/qiskit_experiments/composite/batch_experiment.py @@ -41,7 +41,7 @@ def __init__(self, experiments): qubits = tuple(self._qubit_map.keys()) super().__init__(experiments, qubits) - def circuits(self, backend=None, **circuit_options): + def circuits(self, backend=None): batch_circuits = [] @@ -51,7 +51,7 @@ def circuits(self, backend=None, **circuit_options): qubit_mapping = None else: qubit_mapping = [self._qubit_map[qubit] for qubit in expr.physical_qubits] - for circuit in expr.circuits(**circuit_options): + for circuit in expr.circuits(backend): # Update metadata circuit.metadata = { "experiment_type": self._type, diff --git a/qiskit_experiments/composite/composite_analysis.py b/qiskit_experiments/composite/composite_analysis.py index 265c3d9c03..33258c78bd 100644 --- a/qiskit_experiments/composite/composite_analysis.py +++ b/qiskit_experiments/composite/composite_analysis.py @@ -13,6 +13,7 @@ Composite Experiment Analysis class. """ +from qiskit.exceptions import QiskitError from qiskit_experiments.base_analysis import BaseAnalysis, AnalysisResult from .composite_experiment_data import CompositeExperimentData @@ -40,8 +41,16 @@ def _run_analysis(self, experiment_data: CompositeExperimentData, **options): QiskitError: if analysis is attempted on non-composite experiment data. """ - # Run analysis for sub-experiments and add sub-experiment metadata - # as result of batch experiment + if not isinstance(experiment_data, CompositeExperimentData): + raise QiskitError("CompositeAnalysis must be run on CompositeExperimentData.") + + # Run analysis for sub-experiments + for expr, expr_data in zip( + experiment_data._experiment._experiments, experiment_data._components + ): + expr.run_analysis(expr_data, **options) + + # Add sub-experiment metadata as result of batch experiment # Note: if Analysis results had ID's these should be included here # rather than just the sub-experiment IDs sub_types = [] diff --git a/qiskit_experiments/composite/composite_experiment.py b/qiskit_experiments/composite/composite_experiment.py index 0256e1f59c..70b1583b2d 100644 --- a/qiskit_experiments/composite/composite_experiment.py +++ b/qiskit_experiments/composite/composite_experiment.py @@ -26,7 +26,7 @@ class CompositeExperiment(BaseExperiment): __analysis_class__ = CompositeAnalysis __experiment_data__ = CompositeExperimentData - def __init__(self, experiments, qubits, experiment_type=None, circuit_options=None): + def __init__(self, experiments, qubits, experiment_type=None): """Initialize the composite experiment object. Args: @@ -34,16 +34,13 @@ def __init__(self, experiments, qubits, experiment_type=None, circuit_options=No qubits (int or Iterable[int]): the number of qubits or list of physical qubits for the experiment. experiment_type (str): Optional, composite experiment subclass name. - circuit_options (str): Optional, Optional, dictionary of allowed - kwargs and default values for the `circuit` - method. """ self._experiments = experiments self._num_experiments = len(experiments) - super().__init__(qubits, experiment_type=experiment_type, circuit_options=circuit_options) + super().__init__(qubits, experiment_type=experiment_type) @abstractmethod - def circuits(self, backend=None, **circuit_options): + def circuits(self, backend=None): pass @property @@ -55,6 +52,6 @@ def component_experiment(self, index): """Return the component Experiment object""" return self._experiments[index] - def component_analysis(self, index, **kwargs): + def component_analysis(self, index, **analysis_options): """Return the component experiment Analysis object""" - return self.component_experiment(index).analysis(**kwargs) + return self.component_experiment(index).analysis(**analysis_options) diff --git a/qiskit_experiments/composite/parallel_experiment.py b/qiskit_experiments/composite/parallel_experiment.py index 152709df27..f6a280350d 100644 --- a/qiskit_experiments/composite/parallel_experiment.py +++ b/qiskit_experiments/composite/parallel_experiment.py @@ -32,7 +32,7 @@ def __init__(self, experiments): qubits += exp.physical_qubits super().__init__(experiments, qubits) - def circuits(self, backend=None, **circuit_options): + def circuits(self, backend=None): sub_circuits = [] sub_qubits = [] @@ -42,7 +42,7 @@ def circuits(self, backend=None, **circuit_options): # Generate data for combination for expr in self._experiments: # Add subcircuits - circs = expr.circuits(**circuit_options) + circs = expr.circuits(backend) sub_circuits.append(circs) sub_size.append(len(circs)) diff --git a/qiskit_experiments/data_processing/__init__.py b/qiskit_experiments/data_processing/__init__.py index ad7871477a..3489f694c8 100644 --- a/qiskit_experiments/data_processing/__init__.py +++ b/qiskit_experiments/data_processing/__init__.py @@ -23,6 +23,7 @@ DataProcessor DataAction + TrainableDataAction Data Processing Nodes @@ -33,13 +34,17 @@ Probability ToImag ToReal + SVD + AverageData """ -from .data_action import DataAction +from .data_action import DataAction, TrainableDataAction from .nodes import ( Probability, ToImag, ToReal, + SVD, + AverageData, ) from .data_processor import DataProcessor diff --git a/qiskit_experiments/data_processing/data_action.py b/qiskit_experiments/data_processing/data_action.py index ff08ff9f2c..9427bd508a 100644 --- a/qiskit_experiments/data_processing/data_action.py +++ b/qiskit_experiments/data_processing/data_action.py @@ -13,7 +13,7 @@ """Defines the steps that can be used to analyse data.""" from abc import ABCMeta, abstractmethod -from typing import Any +from typing import Any, List, Optional, Tuple class DataAction(metaclass=ABCMeta): @@ -30,46 +30,75 @@ def __init__(self, validate: bool = True): self._validate = validate @abstractmethod - def _process(self, datum: Any) -> Any: + def _process(self, datum: Any, error: Optional[Any] = None) -> Tuple[Any, Any]: """ Applies the data processing step to the datum. Args: datum: A single item of data which will be processed. + error: An optional error estimation on the datum that can be further propagated. Returns: - processed data: The data that has been processed. + processed data: The data that has been processed along with the propagated error. """ @abstractmethod - def _format_data(self, datum: Any) -> Any: - """ - Check that the given data has the correct structure. This method may + def _format_data(self, datum: Any, error: Optional[Any] = None) -> Tuple[Any, Any]: + """Format and validate the input. + + Check that the given data and error has the correct structure. This method may additionally change the data type, e.g. converting a list to a numpy array. Args: datum: The data instance to check and format. + error: An optional error estimation on the datum to check and format. Returns: - datum: The data that was checked. + datum, error: The formatted datum and its optional error. Raises: - DataProcessorError: If the data does not have the proper format. + DataProcessorError: If either the data or the error do not have the proper format. """ - def __call__(self, data: Any) -> Any: - """ - Call the data action of this node on the data. + def __call__(self, data: Any, error: Optional[Any] = None) -> Tuple[Any, Any]: + """Call the data action of this node on the data and propagate the error. Args: data: The data to process. The action nodes in the data processor will raise errors if the data does not have the appropriate format. + error: An optional error estimation on the datum that can be further processed. Returns: - processed data: The data processed by self. + processed data: The data processed by self as a tuple of processed datum and + optionally the propagated error estimate. """ - return self._process(self._format_data(data)) + return self._process(*self._format_data(data, error)) def __repr__(self): """String representation of the node.""" return f"{self.__class__.__name__}(validate={self._validate})" + + +class TrainableDataAction(DataAction): + """A base class for data actions that need training.""" + + @property + @abstractmethod + def is_trained(self) -> bool: + """Return False if the DataAction needs to be trained. + + Subclasses must implement this property to communicate if they have been trained. + + Return: + True if the data action has been trained. + """ + + @abstractmethod + def train(self, data: List[Any]): + """Train a DataAction. + + Certain data processing nodes, such as a SVD, require data to first train. + + Args: + data: A list of datum. Each datum is a point used to train the node. + """ diff --git a/qiskit_experiments/data_processing/data_processor.py b/qiskit_experiments/data_processing/data_processor.py index 374751c36b..133813cc4e 100644 --- a/qiskit_experiments/data_processing/data_processor.py +++ b/qiskit_experiments/data_processing/data_processor.py @@ -14,7 +14,7 @@ from typing import Any, Dict, List, Set, Tuple, Union -from qiskit_experiments.data_processing.data_action import DataAction +from qiskit_experiments.data_processing.data_action import DataAction, TrainableDataAction from qiskit_experiments.data_processing.exceptions import DataProcessorError @@ -54,7 +54,17 @@ def append(self, node: DataAction): """ self._nodes.append(node) - def __call__(self, datum: Dict[str, Any]) -> Any: + @property + def is_trained(self) -> bool: + """Return True if all nodes of the data processor have been trained.""" + for node in self._nodes: + if isinstance(node, TrainableDataAction): + if not node.is_trained: + return False + + return True + + def __call__(self, datum: Dict[str, Any], **options) -> Tuple[Any, Any]: """ Call self on the given datum. This method sequentially calls the stored data actions on the datum. @@ -62,15 +72,16 @@ def __call__(self, datum: Dict[str, Any]) -> Any: Args: datum: A single item of data, typically from an ExperimentData instance, that needs to be processed. This dict also contains the metadata of each experiment. + options: Run-time options given as keyword arguments that will be passed to the nodes. Returns: processed data: The data processed by the data processor. """ - return self._call_internal(datum, False) + return self._call_internal(datum, **options) def call_with_history( self, datum: Dict[str, Any], history_nodes: Set = None - ) -> Tuple[Any, List]: + ) -> Tuple[Any, Any, List]: """ Call self on the given datum. This method sequentially calls the stored data actions on the datum and also returns the history of the processed data. @@ -89,10 +100,13 @@ def call_with_history( return self._call_internal(datum, True, history_nodes) def _call_internal( - self, datum: Dict[str, Any], with_history: bool, history_nodes: Set = None - ) -> Union[Any, Tuple[Any, List]]: - """ - Internal function to process the data with or with storing the history of the computation. + self, + datum: Dict[str, Any], + with_history: bool = False, + history_nodes: Set = None, + call_up_to_node: int = None, + ) -> Union[Tuple[Any, Any], Tuple[Any, Any, List]]: + """Process the data with or without storing the history of the computation. Args: datum: A single item of data, typically from an ExperimentData instance, that @@ -101,6 +115,9 @@ def _call_internal( history_nodes: The nodes, specified by index in the data processing chain, to include in the history. If None is given then all nodes will be included in the history. + call_up_to_node: The data processor will use each node in the processing chain + up to the node indexed by call_up_to_node. If this variable is not specified + then all nodes in the data processing chain will be called. Returns: datum_ and history if with_history is True or datum_ if with_history is False. @@ -108,6 +125,8 @@ def _call_internal( Raises: DataProcessorError: If the input key of the data processor is not contained in datum. """ + if call_up_to_node is None: + call_up_to_node = len(self._nodes) if self._input_key not in datum: raise DataProcessorError( @@ -115,17 +134,37 @@ def _call_internal( ) datum_ = datum[self._input_key] + error_ = None history = [] for index, node in enumerate(self._nodes): - datum_ = node(datum_) - if with_history and ( - history_nodes is None or (history_nodes and index in history_nodes) - ): - history.append((node.__class__.__name__, datum_, index)) + if index < call_up_to_node: + datum_, error_ = node(datum_, error_) + + if with_history and ( + history_nodes is None or (history_nodes and index in history_nodes) + ): + history.append((node.__class__.__name__, datum_, error_, index)) if with_history: - return datum_, history + return datum_, error_, history else: - return datum_ + return datum_, error_ + + def train(self, data: List[Dict[str, Any]]): + """Train the nodes of the data processor. + + Args: + data: The data to use to train the data processor. + """ + + for index, node in enumerate(self._nodes): + if isinstance(node, TrainableDataAction): + if not node.is_trained: + # Process the data up to the untrained node. + train_data = [] + for datum in data: + train_data.append(self._call_internal(datum, call_up_to_node=index)[0]) + + node.train(train_data) diff --git a/qiskit_experiments/data_processing/nodes.py b/qiskit_experiments/data_processing/nodes.py index 726770db80..307dc398f8 100644 --- a/qiskit_experiments/data_processing/nodes.py +++ b/qiskit_experiments/data_processing/nodes.py @@ -13,173 +13,349 @@ """Different data analysis steps.""" from abc import abstractmethod -from typing import Any, Dict, Optional, Tuple +from typing import Any, Dict, List, Optional, Tuple import numpy as np -from qiskit_experiments.data_processing.data_action import DataAction +from qiskit_experiments.data_processing.data_action import DataAction, TrainableDataAction from qiskit_experiments.data_processing.exceptions import DataProcessorError -class IQPart(DataAction): - """Abstract class for IQ data post-processing.""" +class AverageData(DataAction): + """A node to average data representable as numpy arrays.""" + + def __init__(self, axis: int = 0): + """Initialize a data averaging node. - def __init__(self, scale: Optional[float] = None, validate: bool = True): - """ Args: - scale: Float with which to multiply the IQ data. - validate: If set to False the DataAction will not validate its input. + axis: The axis along which to average the data. If not given 0 is the + default axis. """ - self.scale = scale - super().__init__(validate) + super().__init__() + self._axis = axis - @abstractmethod - def _process(self, datum: np.array) -> np.array: - """Defines how the IQ point will be processed. + def _format_data(self, datum: Any, error: Optional[Any] = None): + """Format the data into numpy arrays.""" + datum = np.asarray(datum, dtype=float) - Args: - datum: A 2D or a 3D array of complex IQ points as [real, imaginary]. + if error is not None: + error = np.asarray(error, dtype=float) - Returns: - Processed IQ point. + return datum, error + + def _process( + self, datum: np.array, error: Optional[np.array] = None + ) -> Tuple[np.array, np.array]: + """Average the data. + + Args: + datum: an array of data. + + Returns: + Two arrays with one less dimension than the given datum and error. The error + is the standard error of the mean, i.e. the standard deviation of the datum + divided by :math:`sqrt{N}` where :math:`N` is the number of data points. + + Raises: + DataProcessorError: If the axis is not an int. """ + standard_error = np.std(datum, axis=self._axis) / np.sqrt(datum.shape[0]) - @abstractmethod - def _required_dimension(self) -> int: - """Return the required dimension of the data.""" + return np.average(datum, axis=self._axis), standard_error - def _format_data(self, datum: Any) -> Any: - """Check that the IQ data has the correct format and convert to numpy array. +class SVD(TrainableDataAction): + """Singular Value Decomposition of averaged IQ data.""" + + def __init__(self, validate: bool = True): + """ Args: - datum: A single item of data which corresponds to single-shot IQ data. It's - dimension will depend on whether it is single-shot IQ data (three-dimensional) - or averaged IQ date (two-dimensional). + validate: If set to False the DataAction will not validate its input. + """ + super().__init__(validate=validate) + self._main_axes = None + self._means = None + self._scales = None + + def _format_data(self, datum: Any, error: Optional[Any] = None) -> Tuple[Any, Any]: + """Check that the IQ data is 2D and convert it to a numpy array. + + Args: + datum: A single item of data which corresponds to single-shot IQ data. Returns: - datum as a numpy array. + datum and any error estimate as a numpy array. Raises: DataProcessorError: If the datum does not have the correct format. """ datum = np.asarray(datum, dtype=float) - if self._validate and len(datum.shape) != self._required_dimension(): - raise DataProcessorError( - f"Single-shot data given {self.__class__.__name__}" - f"must be a {self._required_dimension()}D array. Instead, a {len(datum.shape)}D " - f"array was given." - ) + if error is not None: + error = np.asarray(error, dtype=float) - return datum + if self._validate: + if len(datum.shape) != 2: + raise DataProcessorError( + f"IQ data given to {self.__class__.__name__} must be an 2D array. " + f"Instead, a {len(datum.shape)}D array was given." + ) - def __repr__(self): - """String representation of the node.""" - return f"{self.__class__.__name__}(validate: {self._validate}, scale: {self.scale})" + if error is not None and len(error.shape) != 2: + raise DataProcessorError( + f"IQ data error given to {self.__class__.__name__} must be an 2D array." + f"Instead, a {len(error.shape)}D array was given." + ) + return datum, error -class ToReal(IQPart): - """IQ data post-processing. Isolate the real part of single-shot IQ data.""" + @property + def axis(self) -> List[np.array]: + """Return the axis of the trained SVD""" + return self._main_axes - def _required_dimension(self) -> int: - """Require memory to be a 3D array.""" - return 3 + def means(self, qubit: int, iq_index: int) -> float: + """Return the mean by which to correct the IQ data. - def _process(self, datum: np.array) -> np.array: - """Take the real part of the IQ data. + Before training the SVD the mean of the training data is subtracted from the + training data to avoid large offsets in the data. These means can be retrieved + with this function. Args: - datum: A 3D array of shots, qubits, and a complex IQ point as [real, imaginary]. + qubit: Index of the qubit. + iq_index: Index of either the in-phase (i.e. 0) or the quadrature (i.e. 1). Returns: - A 2D array of shots, qubits. Each entry is the real part of the given IQ data. + The mean that was determined during training for the given qubit and IQ index. """ - if self.scale is None: - return datum[:, :, 0] - - return datum[:, :, 0] * self.scale + return self._means[qubit][iq_index] + @property + def scales(self) -> List[float]: + """Return the scaling of the SVD.""" + return self._scales -class ToRealAvg(IQPart): - """IQ data post-processing. Isolate the real part of averaged IQ data.""" + @property + def is_trained(self) -> bool: + """Return True is the SVD has been trained. - def _required_dimension(self) -> int: - """Require memory to be a 2D array.""" - return 2 + Returns: + True if the SVD has been trained. + """ + return self._main_axes is not None - def _process(self, datum: np.array) -> np.array: - """Take the real part of the IQ data. + def _process( + self, datum: np.array, error: Optional[np.array] = None + ) -> Tuple[np.array, np.array]: + """Project the IQ data onto the axis defined by an SVD and scale it. Args: - datum: A 2D array of qubits, and a complex averaged IQ point as [real, imaginary]. + datum: A 2D array of qubits, and an average complex IQ point as [real, imaginary]. + error: An optional 2D array of qubits, and an error on an average complex IQ + point as [real, imaginary]. Returns: - A 1D array. Each entry is the real part of the averaged IQ data of a qubit. + A Tuple of 1D arrays of the result of the SVD and the associated error. Each entry + is the real part of the averaged IQ data of a qubit. + + Raises: + DataProcessorError: If the SVD has not been previously trained on data. """ - if self.scale is None: - return datum[:, 0] - return datum[:, 0] * self.scale + if not self.is_trained: + raise DataProcessorError("SVD must be trained on data before it can be used.") + n_qubits = datum.shape[0] + processed_data = [] -class ToImag(IQPart): - """IQ data post-processing. Isolate the imaginary part of single-shot IQ data.""" + if error is not None: + processed_error = [] + else: + processed_error = None - def _required_dimension(self) -> int: - """Require memory to be a 3D array.""" - return 3 + # process each averaged IQ point with its own axis. + for idx in range(n_qubits): - def _process(self, datum: np.array) -> np.array: - """Take the imaginary part of the IQ data. + centered = np.array( + [datum[idx][iq] - self.means(qubit=idx, iq_index=iq) for iq in [0, 1]] + ) + + processed_data.append((self._main_axes[idx] @ centered) / self.scales[idx]) + + if error is not None: + angle = np.arctan(self._main_axes[idx][1] / self._main_axes[idx][0]) + error_value = np.sqrt( + (error[idx][0] * np.cos(angle)) ** 2 + (error[idx][1] * np.sin(angle)) ** 2 + ) + processed_error.append(error_value) + + return np.array(processed_data), processed_error + + def train(self, data: List[Any]): + """Train the SVD on the given data. + + Each element of the given data will be converted to a 2D array of dimension + n_qubits x 2. The number of qubits is inferred from the shape of the data. + For each qubit the data is collected into an array of shape 2 x n_data_points. + The mean of the in-phase a quadratures is subtracted before passing the data + to numpy's svd function. The dominant axis and the scale is saved for each + qubit so that future data points can be projected onto the axis. Args: - datum: A 3D array of shots, qubits, and a complex IQ point as [real, imaginary]. + data: A list of datums. Each datum will be converted to a 2D array. + """ + if not data: + return + + n_qubits = self._format_data(data[0])[0].shape[0] + + self._main_axes = [] + self._scales = [] + self._means = [] + + for qubit_idx in range(n_qubits): + datums = np.vstack([self._format_data(datum)[0][qubit_idx] for datum in data]).T + + # Calculate the mean of the data to recenter it in the IQ plane. + mean_i = np.average(datums[0, :]) + mean_q = np.average(datums[1, :]) + + self._means.append((mean_i, mean_q)) + + datums[0, :] = datums[0, :] - mean_i + datums[1, :] = datums[1, :] - mean_q + + mat_u, mat_s, _ = np.linalg.svd(datums) + + self._main_axes.append(mat_u[:, 0]) + self._scales.append(mat_s[0]) + + +class IQPart(DataAction): + """Abstract class for IQ data post-processing.""" + + def __init__(self, scale: float = 1.0, validate: bool = True): + """ + Args: + scale: Float with which to multiply the IQ data. Defaults to 1.0. + validate: If set to False the DataAction will not validate its input. + """ + self.scale = scale + super().__init__(validate) + + @abstractmethod + def _process(self, datum: np.array, error: Optional[np.array] = None) -> np.array: + """Defines how the IQ point is processed. + + Args: + datum: A 2D or a 3D array of complex IQ points as [real, imaginary]. + error: A 2D or a 3D array of errors on complex IQ points as [real, imaginary]. Returns: - A 2D array of shots, qubits. Each entry is the imaginary part of the given IQ data. + Processed IQ point and its associated error estimate. """ - if self.scale is None: - return datum[:, :, 1] - return datum[:, :, 1] * self.scale + def _format_data(self, datum: Any, error: Optional[Any] = None) -> Tuple[Any, Any]: + """Check that the IQ data has the correct format and convert to numpy array. + Args: + datum: A single item of data which corresponds to single-shot IQ data. It's + dimension will depend on whether it is single-shot IQ data (three-dimensional) + or averaged IQ date (two-dimensional). -class ToImagAvg(IQPart): - """IQ data post-processing. Isolate the imaginary part of averaged IQ data.""" + Returns: + datum and any error estimate as a numpy array. - def _required_dimension(self) -> int: - """Require memory to be a 2D array.""" - return 2 + Raises: + DataProcessorError: If the datum does not have the correct format. + """ + datum = np.asarray(datum, dtype=float) - def _process(self, datum: np.array) -> np.array: - """Take the imaginary part of the IQ data. + if error is not None: + error = np.asarray(error, dtype=float) + + if self._validate: + if len(datum.shape) not in {2, 3}: + raise DataProcessorError( + f"IQ data given to {self.__class__.__name__} must be an N dimensional" + f"array with N in (2, 3). Instead, a {len(datum.shape)}D array was given." + ) + + if error is not None and len(error.shape) not in {2, 3}: + raise DataProcessorError( + f"IQ data error given to {self.__class__.__name__} must be an N dimensional" + f"array with N in (2, 3). Instead, a {len(error.shape)}D array was given." + ) + + if error is not None and len(error.shape) != len(datum.shape): + raise DataProcessorError( + "Datum and error do not have the same shape: " + f"{len(datum.shape)} != {len(error.shape)}." + ) + + return datum, error + + def __repr__(self): + """String representation of the node.""" + return f"{self.__class__.__name__}(validate: {self._validate}, scale: {self.scale})" + + +class ToReal(IQPart): + """IQ data post-processing. Isolate the real part of single-shot IQ data.""" + + def _process( + self, datum: np.array, error: Optional[np.array] = None + ) -> Tuple[np.array, np.array]: + """Take the real part of the IQ data. Args: - datum: A 2D array of qubits, and a complex averaged IQ point as [real, imaginary]. + datum: A 2D or 3D array of shots, qubits, and a complex IQ point as [real, imaginary]. + error: An optional 2D or 3D array of shots, qubits, and an error on a complex IQ point + as [real, imaginary]. Returns: - A 1D array. Each entry is the imaginary part of the averaged IQ data of a qubit. + A 1D or 2D array, each entry is the real part of the given IQ data and error. """ - if self.scale is None: - return datum[:, 1] + if error is not None: + return datum[..., 0] * self.scale, error[..., 0] * self.scale + else: + return datum[..., 0] * self.scale, None - return datum[:, 1] * self.scale + +class ToImag(IQPart): + """IQ data post-processing. Isolate the imaginary part of single-shot IQ data.""" + + def _process(self, datum: np.array, error: Optional[np.array] = None) -> np.array: + """Take the imaginary part of the IQ data. + + Args: + datum: A 2D or 3D array of shots, qubits, and a complex IQ point as [real, imaginary]. + error: An optional 2D or 3D array of shots, qubits, and an error on a complex IQ point + as [real, imaginary]. + + Returns: + A 1D or 2D array, each entry is the imaginary part of the given IQ data and error. + """ + if error is not None: + return datum[..., 1] * self.scale, error[..., 1] * self.scale + else: + return datum[..., 1] * self.scale, None class Probability(DataAction): """Count data post processing. This returns the probabilities of the outcome string used to initialize an instance of Probability.""" - def __init__(self, outcome: str, validate: bool = True): + def __init__(self, outcome: str = "1", validate: bool = True): """Initialize a counts to probability data conversion. Args: - outcome: The bitstring for which to compute the probability. + outcome: The bitstring for which to compute the probability which defaults to "1". validate: If set to False the DataAction will not validate its input. """ self._outcome = outcome super().__init__(validate) - def _format_data(self, datum: dict) -> dict: + def _format_data(self, datum: dict, error: Optional[Any] = None) -> Tuple[dict, Any]: """ Checks that the given data has a counts format. @@ -211,9 +387,9 @@ def _format_data(self, datum: dict) -> dict: f"Count {bit_str} is not a valid count value in {self.__class__.__name__}." ) - return datum + return datum, None - def _process(self, datum: Dict[str, Any]) -> Tuple[float, float]: + def _process(self, datum: Dict[str, Any], error: Optional[Dict] = None) -> Tuple[float, float]: """ Args: datum: The data dictionary,taking the data under counts and @@ -222,6 +398,7 @@ def _process(self, datum: Dict[str, Any]) -> Tuple[float, float]: Returns: processed data: A dict with the populations. """ + shots = sum(datum.values()) p_mean = datum.get(self._outcome, 0.0) / shots p_var = p_mean * (1 - p_mean) / shots diff --git a/qiskit_experiments/randomized_benchmarking/clifford_utils.py b/qiskit_experiments/randomized_benchmarking/clifford_utils.py new file mode 100644 index 0000000000..adfd607a0c --- /dev/null +++ b/qiskit_experiments/randomized_benchmarking/clifford_utils.py @@ -0,0 +1,219 @@ +# This code is part of Qiskit. +# +# (C) Copyright IBM 2021. +# +# This code is licensed under the Apache License, Version 2.0. You may +# obtain a copy of this license in the LICENSE.txt file in the root directory +# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0. +# +# Any modifications or derivative works of this code must retain this +# copyright notice, and modified files need to carry a notice indicating +# that they have been altered from the originals. +""" +Utilities for using the Clifford group in randomized benchmarking +""" + +from typing import Optional, Union +from functools import lru_cache +from numpy.random import Generator, default_rng +from qiskit import QuantumCircuit, QuantumRegister +from qiskit.circuit import Gate +from qiskit.circuit.library import SdgGate, HGate, SGate +from qiskit.quantum_info import Clifford, random_clifford + + +class VGate(Gate): + """V Gate used in Clifford synthesis.""" + + def __init__(self): + """Create new V Gate.""" + super().__init__("v", 1, []) + + def _define(self): + """V Gate definition.""" + q = QuantumRegister(1, "q") + qc = QuantumCircuit(q) + qc.data = [(SdgGate(), [q[0]], []), (HGate(), [q[0]], [])] + self.definition = qc + + +class WGate(Gate): + """W Gate used in Clifford synthesis.""" + + def __init__(self): + """Create new W Gate.""" + super().__init__("w", 1, []) + + def _define(self): + """W Gate definition.""" + q = QuantumRegister(1, "q") + qc = QuantumCircuit(q) + qc.data = [(HGate(), [q[0]], []), (SGate(), [q[0]], [])] + self.definition = qc + + +class CliffordUtils: + """Utilities for generating 1 and 2 qubit clifford circuits and elements""" + + NUM_CLIFFORD_1_QUBIT = 24 + NUM_CLIFFORD_2_QUBIT = 11520 + CLIFFORD_1_QUBIT_SIG = (2, 3, 4) + CLIFFORD_2_QUBIT_SIGS = [ + (2, 2, 3, 3, 4, 4), + (2, 2, 3, 3, 3, 3, 4, 4), + (2, 2, 3, 3, 3, 3, 4, 4), + (2, 2, 3, 3, 4, 4), + ] + + def clifford_1_qubit(self, num): + """Return the 1-qubit clifford element corresponding to `num` + where `num` is between 0 and 23. + """ + return Clifford(self.clifford_1_qubit_circuit(num)) + + def clifford_2_qubit(self, num): + """Return the 2-qubit clifford element corresponding to `num` + where `num` is between 0 and 11519. + """ + return Clifford(self.clifford_2_qubit_circuit(num)) + + def random_cliffords( + self, num_qubits: int, size: int = 1, rng: Optional[Union[int, Generator]] = None + ): + """Generate a list of random clifford elements""" + if num_qubits > 2: + return random_clifford(num_qubits, seed=rng) + + if rng is None: + rng = default_rng() + + if isinstance(rng, int): + rng = default_rng(rng) + + if num_qubits == 1: + samples = rng.integers(24, size=size) + return [Clifford(self.clifford_1_qubit_circuit(i)) for i in samples] + else: + samples = rng.integers(11520, size=size) + return [Clifford(self.clifford_2_qubit_circuit(i)) for i in samples] + + def random_clifford_circuits( + self, num_qubits: int, size: int = 1, rng: Optional[Union[int, Generator]] = None + ): + """Generate a list of random clifford circuits""" + if num_qubits > 2: + return [random_clifford(num_qubits, seed=rng).to_circuit() for _ in range(size)] + + if rng is None: + rng = default_rng() + + if isinstance(rng, int): + rng = default_rng(rng) + + if num_qubits == 1: + samples = rng.integers(24, size=size) + return [self.clifford_1_qubit_circuit(i) for i in samples] + else: + samples = rng.integers(11520, size=size) + return [self.clifford_2_qubit_circuit(i) for i in samples] + + @lru_cache(maxsize=24) + def clifford_1_qubit_circuit(self, num): + """Return the 1-qubit clifford circuit corresponding to `num` + where `num` is between 0 and 23. + """ + # pylint: disable=unbalanced-tuple-unpacking + # This is safe since `_unpack_num` returns list the size of the sig + (i, j, p) = self._unpack_num(num, self.CLIFFORD_1_QUBIT_SIG) + qc = QuantumCircuit(1) + if i == 1: + qc.h(0) + if j == 1: + qc.append(VGate(), [0]) + if j == 2: + qc.append(WGate(), [0]) + if p == 1: + qc.x(0) + if p == 2: + qc.y(0) + if p == 3: + qc.z(0) + return qc + + @lru_cache(maxsize=11520) + def clifford_2_qubit_circuit(self, num): + """Return the 2-qubit clifford circuit corresponding to `num` + where `num` is between 0 and 11519. + """ + vals = self._unpack_num_multi_sigs(num, self.CLIFFORD_2_QUBIT_SIGS) + qc = QuantumCircuit(2) + if vals[0] == 0 or vals[0] == 3: + (form, i0, i1, j0, j1, p0, p1) = vals + else: + (form, i0, i1, j0, j1, k0, k1, p0, p1) = vals + if i0 == 1: + qc.h(0) + if i1 == 1: + qc.h(1) + if j0 == 1: + qc.append(VGate(), [0]) + if j0 == 2: + qc.append(WGate(), [0]) + if j1 == 1: + qc.append(VGate(), [1]) + if j1 == 2: + qc.append(WGate(), [1]) + if form in (1, 2, 3): + qc.cx(0, 1) + if form in (2, 3): + qc.cx(1, 0) + if form == 3: + qc.cx(0, 1) + if form in (1, 2): + if k0 == 1: + qc.append(VGate(), [0]) + if k0 == 2: + qc.append(WGate(), [0]) + if k1 == 1: + qc.append(VGate(), [1]) + if k1 == 2: + qc.append(VGate(), [1]) + qc.append(VGate(), [1]) + if p0 == 1: + qc.x(0) + if p0 == 2: + qc.y(0) + if p0 == 3: + qc.z(0) + if p1 == 1: + qc.x(1) + if p1 == 2: + qc.y(1) + if p1 == 3: + qc.z(1) + return qc + + def _unpack_num(self, num, sig): + r"""Returns a tuple :math:`(a_1, \ldots, a_n)` where + :math:`0 \le a_i \le \sigma_i` where + sig=:math:`(\sigma_1, \ldots, \sigma_n)` and num is the sequential + number of the tuple + """ + res = [] + for k in sig: + res.append(num % k) + num //= k + return res + + def _unpack_num_multi_sigs(self, num, sigs): + """Returns the result of `_unpack_num` on one of the + signatures in `sigs` + """ + for i, sig in enumerate(sigs): + sig_size = 1 + for k in sig: + sig_size *= k + if num < sig_size: + return [i] + self._unpack_num(num, sig) + num -= sig_size + return None diff --git a/qiskit_experiments/randomized_benchmarking/interleaved_rb_analysis.py b/qiskit_experiments/randomized_benchmarking/interleaved_rb_analysis.py index ee85befa89..65c2ae58ff 100644 --- a/qiskit_experiments/randomized_benchmarking/interleaved_rb_analysis.py +++ b/qiskit_experiments/randomized_benchmarking/interleaved_rb_analysis.py @@ -18,11 +18,12 @@ process_multi_curve_data, multi_curve_fit, ) -from qiskit_experiments.analysis import plotting from qiskit_experiments.analysis.data_processing import ( level2_probability, multi_mean_xy_data, ) +from qiskit_experiments.analysis import plotting + from .rb_analysis import RBAnalysis @@ -37,11 +38,10 @@ class InterleavedRBAnalysis(RBAnalysis): The error bounds are given by :math:`E=\min\left\{ \begin{array}{c} - \frac{\left(d-1\right)\left[\left|p-p_{\overline{\mathcal{C}}}/p\right|+\left(1-p\right)\right]}{d}\\ + \frac{\left(d-1\right)\left[\left|p-p_{\overline{\mathcal{C}}}\right|+\left(1-p\right)\right]}{d}\\ \frac{2\left(d^{2}-1\right)\left(1-p\right)}{pd^{2}}+\frac{4\sqrt{1-p}\sqrt{d^{2}-1}}{p} \end{array}\right.` """ - # pylint: disable=invalid-name def _run_analysis( self, @@ -50,37 +50,29 @@ def _run_analysis( plot: bool = True, ax: Optional["matplotlib.axes.Axes"] = None, ): + + data = experiment_data.data() + num_qubits = len(data[0]["metadata"]["qubits"]) + + # Process data def data_processor(datum): - return level2_probability(datum, datum["metadata"]["ylabel"]) + return level2_probability(datum, num_qubits * "0") - num_qubits = len(experiment_data.data[0]["metadata"]["qubits"]) - series, x, y, sigma = process_multi_curve_data(experiment_data.data, data_processor) - series, xdata, ydata, ydata_sigma = multi_mean_xy_data(series, x, y, sigma) + # Raw data for each sample + series_raw, x_raw, y_raw, sigma_raw = process_multi_curve_data(data, data_processor) - def fit_fun_standard(x, a, alpha_std, _, b): - return a * alpha_std ** x + b + # Data averaged over samples + series, xdata, ydata, ydata_sigma = multi_mean_xy_data(series_raw, x_raw, y_raw, sigma_raw) - def fit_fun_interleaved(x, a, _, alpha_int, b): - return a * alpha_int ** x + b + # pylint: disable = unused-argument + def fit_fun_standard(x, a, alpha, alpha_c, b): + return a * alpha ** x + b - std_idx = series == 0 - std_xdata = xdata[std_idx] - std_ydata = ydata[std_idx] - std_ydata_sigma = ydata_sigma[std_idx] - p0_std = self._p0(std_xdata, std_ydata, num_qubits) + def fit_fun_interleaved(x, a, alpha, alpha_c, b): + return a * (alpha * alpha_c) ** x + b - int_idx = series == 1 - int_xdata = xdata[int_idx] - int_ydata = ydata[int_idx] - int_ydata_sigma = ydata_sigma[int_idx] - p0_int = self._p0(int_xdata, int_ydata, num_qubits) - - p0 = ( - np.mean([p0_std[0], p0_int[0]]), - p0_std[1], - p0_int[1], - np.mean([p0_std[2], p0_int[2]]), - ) + p0 = self._p0_multi(series, xdata, ydata, num_qubits) + bounds = {"a": [0, 1], "alpha": [0, 1], "alpha_c": [0, 1], "b": [0, 1]} analysis_result = multi_curve_fit( [fit_fun_standard, fit_fun_interleaved], @@ -89,19 +81,23 @@ def fit_fun_interleaved(x, a, _, alpha_int, b): ydata, p0, ydata_sigma, - bounds=([0, 0, 0, 0], [1, 1, 1, 1]), + bounds=bounds, ) # Add EPC data nrb = 2 ** num_qubits - scale = (nrb - 1) / (2 ** nrb) + scale = (nrb - 1) / nrb _, alpha, alpha_c, _ = analysis_result["popt"] - _, alpha_err, alpha_c_err, _ = analysis_result["popt_err"] + _, _, alpha_c_err, _ = analysis_result["popt_err"] # Calculate epc_est (=r_c^est) - Eq. (4): - epc_est = scale * (1 - alpha_c / alpha) + epc_est = scale * (1 - alpha_c) + epc_est_err = scale * alpha_c_err + analysis_result["EPC"] = epc_est + analysis_result["EPC_err"] = epc_est_err + # Calculate the systematic error bounds - Eq. (5): - systematic_err_1 = scale * (abs(alpha - alpha_c / alpha) + (1 - alpha)) + systematic_err_1 = scale * (abs(alpha - alpha_c) + (1 - alpha)) systematic_err_2 = ( 2 * (nrb * nrb - 1) * (1 - alpha) / (alpha * nrb * nrb) + 4 * (np.sqrt(1 - alpha)) * (np.sqrt(nrb * nrb - 1)) / alpha @@ -109,31 +105,73 @@ def fit_fun_interleaved(x, a, _, alpha_int, b): systematic_err = min(systematic_err_1, systematic_err_2) systematic_err_l = epc_est - systematic_err systematic_err_r = epc_est + systematic_err + analysis_result["EPC_systematic_err"] = systematic_err + analysis_result["EPC_systematic_bounds"] = [max(systematic_err_l, 0), systematic_err_r] + + if plot and plotting.HAS_MATPLOTLIB: + ax = plotting.plot_curve_fit(fit_fun_standard, analysis_result, ax=ax, color="blue") + ax = plotting.plot_curve_fit( + fit_fun_interleaved, + analysis_result, + ax=ax, + color="green", + ) + ax = self._generate_multi_scatter_plot(series_raw, x_raw, y_raw, ax=ax) + ax = self._generate_multi_errorbar_plot(series, xdata, ydata, ydata_sigma, ax=ax) + self._format_plot(ax, analysis_result) + ax.legend(loc="center right") + figures = [ax.get_figure()] + else: + figures = None + return analysis_result, figures + + @staticmethod + def _generate_multi_scatter_plot(series, xdata, ydata, ax): + """Generate scatter plot of raw data""" + idx0 = series == 0 + idx1 = series == 1 + ax = plotting.plot_scatter(xdata[idx0], ydata[idx0], ax=ax) + ax = plotting.plot_scatter(xdata[idx1], ydata[idx1], ax=ax, marker="+", c="darkslategrey") + return ax - alpha_err_sq = (alpha_err / alpha) ** 2 - alpha_c_err_sq = (alpha_c_err / alpha_c) ** 2 - epc_est_err = ( - ((nrb - 1) / nrb) * (alpha_c / alpha) * (np.sqrt(alpha_err_sq + alpha_c_err_sq)) + @staticmethod + def _generate_multi_errorbar_plot(series, xdata, ydata, sigma, ax): + """Generate errorbar plot of average data""" + idx0 = series == 0 + idx1 = series == 1 + ax = plotting.plot_errorbar( + xdata[idx0], + ydata[idx0], + sigma[idx0], + ax=ax, + label="Standard", + marker=".", + color="red", ) + ax = plotting.plot_errorbar( + xdata[idx1], + ydata[idx1], + sigma[idx1], + ax=ax, + label="Interleaved", + marker="^", + color="orange", + ) + return ax - analysis_result["EPC"] = epc_est - analysis_result["EPC_err"] = epc_est_err - analysis_result["systematic_err"] = systematic_err - analysis_result["systematic_err_L"] = systematic_err_l - analysis_result["systematic_err_R"] = systematic_err_r - analysis_result["plabels"] = ["A", "alpha", "alpha_c", "B"] - - if plot: - ax = plotting.plot_curve_fit(fit_fun_standard, analysis_result, ax=ax) - ax = plotting.plot_curve_fit(fit_fun_interleaved, analysis_result, ax=ax) - ax = plotting.plot_scatter(std_xdata, std_ydata, ax=ax) - ax = plotting.plot_scatter(int_xdata, int_ydata, ax=ax) - ax = plotting.plot_errorbar(std_xdata, std_ydata, std_ydata_sigma, ax=ax) - ax = plotting.plot_errorbar(int_xdata, int_ydata, int_ydata_sigma, ax=ax) - self._format_plot(ax, analysis_result) - analysis_result.plt = plotting.pyplot - - return analysis_result, None + @staticmethod + def _p0_multi(series, xdata, ydata, num_qubits): + """Initial guess for the fitting function""" + std_idx = series == 0 + p0_std = RBAnalysis._p0(xdata[std_idx], ydata[std_idx], num_qubits) + int_idx = series == 1 + p0_int = RBAnalysis._p0(xdata[int_idx], xdata[int_idx], num_qubits) + return { + "a": np.mean([p0_std["a"], p0_int["a"]]), + "alpha": p0_std["alpha"], + "alpha_c": min(p0_int["alpha"] / p0_std["alpha"], 1), + "b": np.mean([p0_std["b"], p0_int["b"]]), + } @classmethod def _format_plot(cls, ax, analysis_result, add_label=True): diff --git a/qiskit_experiments/randomized_benchmarking/interleaved_rb_experiment.py b/qiskit_experiments/randomized_benchmarking/interleaved_rb_experiment.py index 17ed0fcd1f..1ac6d1afd1 100644 --- a/qiskit_experiments/randomized_benchmarking/interleaved_rb_experiment.py +++ b/qiskit_experiments/randomized_benchmarking/interleaved_rb_experiment.py @@ -18,7 +18,7 @@ from qiskit import QuantumCircuit from qiskit.circuit import Instruction -from qiskit.quantum_info import Clifford, random_clifford +from qiskit.quantum_info import Clifford from .rb_experiment import RBExperiment from .interleaved_rb_analysis import InterleavedRBAnalysis @@ -62,7 +62,7 @@ def __init__( def _sample_circuits(self, lengths, seed=None): circuits = [] for length in lengths if self._full_sampling else [lengths[-1]]: - elements = [random_clifford(self.num_qubits, seed=seed) for _ in range(length)] + elements = self._clifford_utils.random_clifford_circuits(self.num_qubits, length, seed) element_lengths = [len(elements)] if self._full_sampling else lengths std_circuits = self._generate_circuit(elements, element_lengths) for circuit in std_circuits: diff --git a/qiskit_experiments/randomized_benchmarking/rb_analysis.py b/qiskit_experiments/randomized_benchmarking/rb_analysis.py index ccf6d5c173..45b21e0063 100644 --- a/qiskit_experiments/randomized_benchmarking/rb_analysis.py +++ b/qiskit_experiments/randomized_benchmarking/rb_analysis.py @@ -15,30 +15,41 @@ from typing import Optional, List +from qiskit.providers.options import Options +from qiskit_experiments.experiment_data import ExperimentData from qiskit_experiments.base_analysis import BaseAnalysis from qiskit_experiments.analysis.curve_fitting import curve_fit, process_curve_data from qiskit_experiments.analysis.data_processing import ( level2_probability, mean_xy_data, ) -from qiskit_experiments.analysis.plotting import ( - HAS_MATPLOTLIB, - plot_curve_fit, - plot_scatter, - plot_errorbar, -) +from qiskit_experiments.analysis import plotting class RBAnalysis(BaseAnalysis): - """RB Analysis class.""" + """RB Analysis class. + + Analysis Options: + p0: Optional, initial parameter values for curve_fit. + plot: If True generate a plot of fitted data. + ax: Optional, matplotlib axis to add plot to. + """ + + @classmethod + def _default_options(cls): + return Options( + p0=None, + plot=True, + ax=None, + ) # pylint: disable = arguments-differ, invalid-name def _run_analysis( self, - experiment_data: "ExperimentData", + experiment_data: ExperimentData, p0: Optional[List[float]] = None, plot: bool = True, - ax: Optional["AxesSubplot"] = None, + ax: Optional["plotting.pyplot.AxesSubplot"] = None, ): """Run analysis on circuit data. Args: @@ -60,7 +71,7 @@ def data_processor(datum): return level2_probability(datum, num_qubits * "0") # Raw data for each sample - x_raw, y_raw, sigma_raw = process_curve_data(data, data_processor, x_key="xdata") + x_raw, y_raw, sigma_raw = process_curve_data(data, data_processor) # Data averaged over samples xdata, ydata, ydata_sigma = mean_xy_data(x_raw, y_raw, sigma_raw, method="sample") @@ -80,10 +91,10 @@ def fit_fun(x, a, alpha, b): analysis_result["EPC"] = scale * (1 - popt[1]) analysis_result["EPC_err"] = scale * popt_err[1] / popt[1] - if plot and HAS_MATPLOTLIB: - ax = plot_curve_fit(fit_fun, analysis_result, ax=ax) - ax = plot_scatter(x_raw, y_raw, ax=ax) - ax = plot_errorbar(xdata, ydata, ydata_sigma, ax=ax) + if plot and plotting.HAS_MATPLOTLIB: + ax = plotting.plot_curve_fit(fit_fun, analysis_result, ax=ax) + ax = plotting.plot_scatter(x_raw, y_raw, ax=ax) + ax = plotting.plot_errorbar(xdata, ydata, ydata_sigma, ax=ax) self._format_plot(ax, analysis_result) figures = [ax.get_figure()] else: diff --git a/qiskit_experiments/randomized_benchmarking/rb_experiment.py b/qiskit_experiments/randomized_benchmarking/rb_experiment.py index 26b8631c19..85cb6d9835 100644 --- a/qiskit_experiments/randomized_benchmarking/rb_experiment.py +++ b/qiskit_experiments/randomized_benchmarking/rb_experiment.py @@ -19,14 +19,22 @@ from qiskit import QuantumCircuit from qiskit.providers import Backend -from qiskit.quantum_info import Clifford, random_clifford +from qiskit.quantum_info import Clifford +from qiskit.providers.options import Options +from qiskit.circuit import Gate from qiskit_experiments.base_experiment import BaseExperiment from .rb_analysis import RBAnalysis +from .clifford_utils import CliffordUtils class RBExperiment(BaseExperiment): - """RB Experiment class""" + """RB Experiment class. + + Experiment Options: + lengths: A list of RB sequences lengths. + num_samples: number of samples to generate for each sequence length. + """ # Analysis class for experiment __analysis_class__ = RBAnalysis @@ -45,8 +53,7 @@ def __init__( qubits: the number of qubits or list of physical qubits for the experiment. lengths: A list of RB sequences lengths. - num_samples: number of samples to generate for each - sequence length + num_samples: number of samples to generate for each sequence length. seed: Seed or generator object for random number generation. If None default_rng will be used. full_sampling: If True all Cliffords are independently sampled for @@ -54,14 +61,24 @@ def __init__( sequences are constructed by appending additional Clifford samples to shorter sequences. """ + # Initialize base experiment + super().__init__(qubits) + + # Set configurable options + self.set_experiment_options(lengths=list(lengths), num_samples=num_samples) + + # Set fixed options + self._full_sampling = full_sampling + self._clifford_utils = CliffordUtils() + if not isinstance(seed, Generator): self._rng = default_rng(seed=seed) else: self._rng = seed - self._lengths = list(lengths) - self._num_samples = num_samples - self._full_sampling = full_sampling - super().__init__(qubits) + + @classmethod + def _default_experiment_options(cls): + return Options(lengths=None, num_samples=None) # pylint: disable = arguments-differ def circuits(self, backend: Optional[Backend] = None) -> List[QuantumCircuit]: @@ -72,29 +89,8 @@ def circuits(self, backend: Optional[Backend] = None) -> List[QuantumCircuit]: A list of :class:`QuantumCircuit`. """ circuits = [] - for _ in range(self._num_samples): - circuits += self._sample_circuits(self._lengths, seed=self._rng) - return circuits - - def transpiled_circuits( - self, backend: Optional[Backend] = None, **kwargs - ) -> List[QuantumCircuit]: - """Return a list of transpiled RB circuits. - - Args: - backend: Optional, a backend object to use as the - argument for the :func:`qiskit.transpile` function. - kwargs: kwarg options for the :func:`qiskit.transpile` function. - - Returns: - A list of :class:`QuantumCircuit`. - - Raises: - QiskitError: if an initial layout is specified in the - kwarg options for transpilation. The initial - layout must be generated from the experiment. - """ - circuits = super().transpiled_circuits(backend=backend, **kwargs) + for _ in range(self.experiment_options.num_samples): + circuits += self._sample_circuits(self.experiment_options.lengths, seed=self._rng) return circuits def _sample_circuits( @@ -112,7 +108,7 @@ def _sample_circuits( """ circuits = [] for length in lengths if self._full_sampling else [lengths[-1]]: - elements = [random_clifford(self.num_qubits, seed=seed) for _ in range(length)] + elements = self._clifford_utils.random_clifford_circuits(self.num_qubits, length, seed) element_lengths = [len(elements)] if self._full_sampling else lengths circuits += self._generate_circuit(elements, element_lengths) return circuits @@ -136,18 +132,23 @@ def _generate_circuit( qubits = list(range(self.num_qubits)) circuits = [] - circ = QuantumCircuit(self.num_qubits) - circ.barrier(qubits) + circs = [QuantumCircuit(self.num_qubits) for _ in range(len(lengths))] + for circ in circs: + circ.barrier(qubits) circ_op = Clifford(np.eye(2 * self.num_qubits)) - for current_length, group_elt in enumerate(elements): - circ_op = circ_op.compose(group_elt) - circ.append(group_elt, qubits) - circ.barrier(qubits) + for current_length, group_elt_circ in enumerate(elements): + group_elt_gate = group_elt_circ + if not isinstance(group_elt_gate, Gate): + group_elt_gate = group_elt_gate.to_gate() + circ_op = circ_op.compose(Clifford(group_elt_circ)) + for circ in circs: + circ.append(group_elt_gate, qubits) + circ.barrier(qubits) if current_length + 1 in lengths: # copy circuit and add inverse inv = circ_op.adjoint() - rb_circ = circ.copy() + rb_circ = circs.pop() rb_circ.append(inv, qubits) rb_circ.barrier(qubits) rb_circ.metadata = { diff --git a/qiskit_experiments/randomized_benchmarking/rb_utils.py b/qiskit_experiments/randomized_benchmarking/rb_utils.py new file mode 100644 index 0000000000..31a1cfae64 --- /dev/null +++ b/qiskit_experiments/randomized_benchmarking/rb_utils.py @@ -0,0 +1,440 @@ +# -*- coding: utf-8 -*- + +# This code is part of Qiskit. +# +# (C) Copyright IBM 2019-2021. +# +# This code is licensed under the Apache License, Version 2.0. You may +# obtain a copy of this license in the LICENSE.txt file in the root directory +# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0. +# +# Any modifications or derivative works of this code must retain this +# copyright notice, and modified files need to carry a notice indicating +# that they have been altered from the originals. + +""" +RB Helper functions +""" + +from typing import List, Union, Dict, Optional +from warnings import warn + +import numpy as np +from qiskit import QuantumCircuit, QiskitError +from qiskit.qobj import QasmQobj + +def gates_per_clifford( + transpiled_circuits_list: Union[List[List[QuantumCircuit]], List[QasmQobj]], + clifford_lengths: Union[np.ndarray, List[int]], + basis: List[str], + qubits: List[int]) -> Dict[int, Dict[str, float]]: + """Take a list of transpiled ``QuantumCircuit`` and use these to calculate + the number of gates per Clifford. Each ``QuantumCircuit`` should be transpiled into + given ``basis`` set. The result can be used to convert a value of error per Clifford + into error per basis gate under appropriate assumption. + Example: + This example shows how to calculate gate per Clifford of 2Q RB sequence for + qubit 0 and qubit 1. You can refer to the function + :mod:`~qiskit.ignis.verification.randomized_benchmarking.randomized_benchmarking_seq` + for the detail of RB circuit generation. + .. jupyter-execute:: + import pprint + import qiskit + import qiskit.ignis.verification.randomized_benchmarking as rb + from qiskit.test.mock.backends import FakeAlmaden + rb_circs_list, xdata = rb.randomized_benchmarking_seq( + nseeds=5, + length_vector=[1, 20, 50, 100], + rb_pattern=[[0, 1]]) + basis = FakeAlmaden().configuration().basis_gates + # transpile + transpiled_circuits_list = [] + for rb_circs in rb_circs_list: + rb_circs_transpiled = qiskit.transpile(rb_circs, basis_gates=basis) + transpiled_circuits_list.append(rb_circs_transpiled) + # count gate per Clifford + ngates = rb.rb_utils.gates_per_clifford( + transpiled_circuits_list=transpiled_circuits_list, + clifford_lengths=xdata[0], + basis=basis, qubits=[0, 1]) + pprint.pprint(ngates) + The gate counts for qubit 0 (1) is obtained by ``ngates[0]`` (``ngates[1]``) + as usual python dictionary. If all gate counts are zero, + you might specify wrong ``basis`` or input circuit list is not transpiled into basis gates. + Args: + transpiled_circuits_list: List of transpiled RB circuit for each seed. + clifford_lengths: number of Cliffords in each circuit + basis: gates basis for the qobj + qubits: qubits to count over + Returns: + Nested dictionary of gate counts per Clifford. + Raises: + QiskitError: when input object is not a list of `QuantumCircuit`. + """ + ngates = {qubit: {base: 0 for base in basis} for qubit in qubits} + + for transpiled_circuits in transpiled_circuits_list: + for transpiled_circuit in transpiled_circuits: + if isinstance(transpiled_circuit, QuantumCircuit): + bit_indices = {bit: index + for index, bit in enumerate(transpiled_circuit.qubits)} + + for instr, qregs, _ in transpiled_circuit.data: + for qreg in qregs: + try: + ngates[bit_indices[qreg]][instr.name] += 1 + except KeyError: + pass + else: + raise QiskitError('Input object is not `QuantumCircuit`.') + + # include inverse, ie + 1 for all clifford length + total_ncliffs = len(transpiled_circuits_list) * np.sum(np.array(clifford_lengths) + 1) + + for qubit in qubits: + for base in basis: + ngates[qubit][base] /= total_ncliffs + + return ngates + + +def coherence_limit(nQ=2, T1_list=None, T2_list=None, + gatelen=0.1): + """ + The error per gate (1-average_gate_fidelity) given by the T1,T2 limit. + Args: + nQ (int): number of qubits (1 and 2 supported). + T1_list (list): list of T1's (Q1,...,Qn). + T2_list (list): list of T2's (as measured, not Tphi). + If not given assume T2=2*T1 . + gatelen (float): length of the gate. + Returns: + float: coherence limited error per gate. + Raises: + ValueError: if there are invalid inputs + """ + + T1 = np.array(T1_list) + + if T2_list is None: + T2 = 2*T1 + else: + T2 = np.array(T2_list) + + if len(T1) != nQ or len(T2) != nQ: + raise ValueError("T1 and/or T2 not the right length") + + coherence_limit_err = 0 + + if nQ == 1: + + coherence_limit_err = 0.5*(1.-2./3.*np.exp(-gatelen/T2[0]) - + 1./3.*np.exp(-gatelen/T1[0])) + + elif nQ == 2: + + T1factor = 0 + T2factor = 0 + + for i in range(2): + T1factor += 1./15.*np.exp(-gatelen/T1[i]) + T2factor += 2./15.*(np.exp(-gatelen/T2[i]) + + np.exp(-gatelen*(1./T2[i]+1./T1[1-i]))) + + T1factor += 1./15.*np.exp(-gatelen*np.sum(1/T1)) + T2factor += 4./15.*np.exp(-gatelen*np.sum(1/T2)) + + coherence_limit_err = 0.75*(1.-T1factor-T2factor) + + else: + raise ValueError('Not a valid number of qubits') + + return coherence_limit_err + +def calculate_1q_epg(gate_per_cliff: Dict[int, Dict[str, float]], + epc_1q: float, + qubit: int) -> Dict[str, float]: + r""" + Convert error per Clifford (EPC) into error per gates (EPGs) of single qubit basis gates. + Given that a standard 1Q RB sequences consist of ``rz``, ``x``, and ``sx`` gates, + the EPC can be written using those EPGs: + .. math:: + EPC = 1 - (1 - EPG_{x})^{N_{x}} (1 - EPG_{sx})^{N_{sx}} (1 - EPG_{rz})^{N_{rz}}. + where :math:`N_{G}` is the number of gate :math:`G` per Clifford. + Assuming ``rz`` composed of virtual-Z gate, ie FrameChange instruction, + the :math:`EPG_{rz}` is estimated to be zero within the range of quantization error. + Therefore the EPC can be written as: + .. math:: + EPC = 1 - (1 - EPG_{x})^{N_{x}} (1 - EPG_{sx})^{N_{sx}}. + Because ``x`` and ``sx`` gates are implemented by a single + half-pi pulses with virtual-Z rotations, we assume :math:`EPG_{x} = EPG_{sx}`. + Using this relation in the limit of :math:`EPG_{x} \ll 1`: + .. math:: + EPC & = 1 - (1 - EPG_{x})^{N_{x}} (1 - EPG_{sx})^{N_{sx}} \\ + & \simeq EPG_{x}(N_{x} + N_{sx}). + Finally the EPG of each basis gate can be written using EPC and number of gates: + .. math:: + EPG_{rz} &= 0 \\ + EPG_{x} &= EPC / (N_{x} + N_{sx}) \\ + EPG_{sx} &= EPC / (N_{x} + N_{sx}) + To run this function, you first need to run a standard 1Q RB experiment with transpiled + ``QuantumCircuit`` and count the number of basis gates composing the RB circuits. + .. jupyter-execute:: + import pprint + import qiskit.ignis.verification.randomized_benchmarking as rb + # assuming we ran 1Q RB experiment for qubit 0 + gpc = {0: {'cx': 0, 'rz': 0.13, 'x': 0.31, 'sx': 0.51}} + epc = 1.5e-3 TODO not accutrate for new gateset + # calculate 1Q EPGs + epgs = rb.rb_utils.calculate_1q_epg(gate_per_cliff=gpc, epc_1q=epc, qubit=0) + pprint.pprint(epgs) + In the example, ``gpc`` can be generated by :func:`gates_per_clifford`. + The output of the function ``epgs`` can be used to calculate EPG of CNOT gate + in conjugation with 2Q RB results, see :func:`calculate_2q_epg`. + Note: + This function presupposes the basis gate consists + of ``rz``, ``x`` and ``sx``. + Args: + gate_per_cliff: dictionary of gate per Clifford. see :func:`gates_per_clifford`. + epc_1q: EPC fit from 1Q RB experiment data. + qubit: index of qubit to calculate EPGs. + Returns: + Dictionary of EPGs of single qubit basis gates. + Raises: + QiskitError: when ``x`` or ``sx`` is not found, ``cx`` gate count is nonzero, + or specified qubit is not included in the gate count dictionary. + """ + if qubit not in gate_per_cliff: + raise QiskitError('Qubit %d is not included in the `gate_per_cliff`' % qubit) + + gpc_per_qubit = gate_per_cliff[qubit] + + if 'x' not in gpc_per_qubit or 'sx' not in gpc_per_qubit: + raise QiskitError('Invalid basis set is given. Use `rz`, `x`, `sx` for basis gates.') + + n_x = gpc_per_qubit['x'] + n_sx = gpc_per_qubit['sx'] + + if gpc_per_qubit.get('cx', 0) > 0: + raise QiskitError('Two qubit gate is included in the RB sequence.') + + return {'rz': 0, 'x': epc_1q / (n_x + n_sx), 'sx': epc_1q / (n_x + n_sx)} + + +def calculate_2q_epg(gate_per_cliff: Dict[int, Dict[str, float]], + epc_2q: float, + qubit_pair: List[int], + list_epgs_1q: Optional[List[Dict[str, float]]] = None, + two_qubit_name: Optional[str] = 'cx') -> float: + r""" + Convert error per Clifford (EPC) into error per gate (EPG) of two qubit ``cx`` gates. + Given that a standard 2Q RB sequences consist of ``rz``, ``x``, ``sx``, and ``cx`` gates, + the EPG of ``cx`` gate can be roughly approximated by :math:`EPG_{CX} = EPC/N_{CX}`, + where :math:`N_{CX}` is number of ``cx`` gates per Clifford which is designed to be 1.5. + Because an error from two qubit gates are usually dominant and the contribution of + single qubit gates in 2Q RB experiments is thus able to be ignored. + If ``list_epgs_1q`` is not provided, the function returns + the EPG calculated based upon this assumption. + When we know the EPG of every single qubit gates used in the 2Q RB experiment, + we can isolate the EPC of the two qubit gate, ie :math:`EPG_{CX} = EPC_{CX}/N_{CX}` [1]. + This will give you more accurate estimation of EPG, especially when the ``cx`` + gate fidelity is close to that of single qubit gate. + To evaluate EPGs of single qubit gates, you first need to run standard 1Q RB experiments + separately and feed the fit result and gate counts to :func:`calculate_1q_epg`. + .. jupyter-execute:: + import qiskit.ignis.verification.randomized_benchmarking as rb + # assuming we ran 1Q RB experiment for qubit 0 and qubit 1 + gpc = {0: {'cx': 0, 'rz': 0.13, 'x': 0.31, 'sx': 0.51}, + 1: {'cx': 0, 'rz': 0.10, 'x': 0.33, 'sx': 0.51}} + epc_q0 = 1.5e-3 TODO not accutrate for new gateset + epc_q1 = 5.8e-4 TODO not accutrate for new gateset + # calculate 1Q EPGs + epgs_q0 = rb.rb_utils.calculate_1q_epg(gate_per_cliff=gpc, epc_1q=epc_q0, qubit=0) + epgs_q1 = rb.rb_utils.calculate_1q_epg(gate_per_cliff=gpc, epc_1q=epc_q1, qubit=1) + # assuming we ran 2Q RB experiment for qubit 0 and qubit 1 + gpc = {0: {'cx': 1.49, 'rz': 0.25, 'x': 0.95, 'sx': 0.56}, + 1: {'cx': 1.49, 'rz': 0.24, 'x': 0.98, 'sx': 0.49}} + epc = 2.4e-2 TODO not accutrate for new gateset + # calculate 2Q EPG + epg_no_comp = rb.rb_utils.calculate_2q_epg( + gate_per_cliff=gpc, + epc_2q=epc, + qubit_pair=[0, 1]) + epg_comp = rb.rb_utils.calculate_2q_epg( + gate_per_cliff=gpc, + epc_2q=epc, + qubit_pair=[0, 1], + list_epgs_1q=[epgs_q0, epgs_q1]) + print('EPG without `list_epgs_1q`: %f, with `list_epgs_1q`: %f' % (epg_no_comp, epg_comp)) + Note: + This function presupposes the basis gate consists + of ``rz``, ``x``, ``sx`` and ``cx``. + References: + [1] D. C. McKay, S. Sheldon, J. A. Smolin, J. M. Chow, + and J. M. Gambetta, “Three-Qubit Randomized Benchmarking,” + Phys. Rev. Lett., vol. 122, no. 20, 2019 (arxiv:1712.06550). + Args: + gate_per_cliff: dictionary of gate per Clifford. see :func:`gates_per_clifford`. + epc_2q: EPC fit from 2Q RB experiment data. + qubit_pair: index of two qubits to calculate EPG. + list_epgs_1q: list of single qubit EPGs of qubit listed in ``qubit_pair``. + two_qubit_name: name of two qubit gate in ``basis gates``. + Returns: + EPG of 2Q gate. + Raises: + QiskitError: when ``cx`` is not found, specified ``qubit_pair`` is not included + in the gate count dictionary, or length of ``qubit_pair`` is not 2. + """ + list_epgs_1q = list_epgs_1q or [] + + if len(qubit_pair) != 2: + raise QiskitError('Number of qubit is not 2.') + + # estimate single qubit gate error contribution + alpha_1q = [1.0, 1.0] + for ind, (qubit, epg_1q) in enumerate(zip(qubit_pair, list_epgs_1q)): + if qubit not in gate_per_cliff: + raise QiskitError('Qubit %d is not included in the `gate_per_cliff`' % qubit) + gpc_per_qubit = gate_per_cliff[qubit] + for gate_name, epg in epg_1q.items(): + n_gate = gpc_per_qubit.get(gate_name, 0) + alpha_1q[ind] *= (1 - 2 * epg) ** n_gate + alpha_c_1q = 1 / 5 * (alpha_1q[0] + alpha_1q[1] + 3 * alpha_1q[0] * alpha_1q[1]) + alpha_c_2q = (1 - 4 / 3 * epc_2q) / alpha_c_1q + + n_gate_2q = gate_per_cliff[qubit_pair[0]].get(two_qubit_name, 0) + + if n_gate_2q > 0: + return 3 / 4 * (1 - alpha_c_2q) / n_gate_2q + + raise QiskitError('Two qubit gate %s is not included in the `gate_per_cliff`. ' + 'Set correct `two_qubit_name` or use 2Q RB gate count.' % two_qubit_name) + + +def calculate_1q_epc(gate_per_cliff: Dict[int, Dict[str, float]], + epg_1q: Dict[str, float], + qubit: int) -> float: + r""" + Convert error per gate (EPG) into error per Clifford (EPC) of single qubit basis gates. + Given that we know the number of gates per Clifford :math:`N_i` and those EPGs, + we can predict EPC of that RB sequence: + .. math:: + EPC = 1 - \prod_i \left( 1 - EPG_i \right)^{N_i} + To run this function, you need to know EPG of every single qubit basis gates. + For example, when you prepare 1Q RB experiment with appropriate error model, + you can define EPG of those basis gate set. Then you can estimate the EPC of + prepared RB sequence without running experiment. + .. jupyter-execute:: + import qiskit.ignis.verification.randomized_benchmarking as rb + # gate counts of your 1Q RB experiment + gpc = {0: {'cx': 0, 'rZ': 0.13, 'x': 0.31, 'sx': 0.51}} + # EPGs from error model + epgs_q0 = {'rz': 0, 'x': 0.001, 'sx': 0.001} + # calculate 1Q EPC + epc = rb.rb_utils.calculate_1q_epc( + gate_per_cliff=gpc, + epg_1q=epgs_q0, + qubit=0) + print(epc) + Args: + gate_per_cliff: dictionary of gate per Clifford. see :func:`gates_per_clifford`. + epg_1q: EPG of single qubit gates estimated by error model. + qubit: index of qubit to calculate EPC. + Returns: + EPG of 2Q gate. + Raises: + QiskitError: when specified ``qubit`` is not included in the gate count dictionary + """ + if qubit not in gate_per_cliff: + raise QiskitError('Qubit %d is not included in the `gate_per_cliff`' % qubit) + + fid = 1 + gpc_per_qubit = gate_per_cliff[qubit] + + for gate_name, epg in epg_1q.items(): + n_gate = gpc_per_qubit.get(gate_name, 0) + fid *= (1 - epg) ** n_gate + + return 1 - fid + + +def calculate_2q_epc(gate_per_cliff: Dict[int, Dict[str, float]], + epg_2q: float, + qubit_pair: List[int], + list_epgs_1q: List[Dict[str, float]], + two_qubit_name: Optional[str] = 'cx') -> float: + r""" + Convert error per gate (EPG) into error per Clifford (EPC) of two qubit ``cx`` gates. + Given that we know the number of gates per Clifford :math:`N_i` and those EPGs, + we can predict EPC of that RB sequence: + .. math:: + EPC = 1 - \prod_i \left( 1 - EPG_i \right)^{N_i} + This function isolates the contribution of two qubit gate to the EPC [1]. + This will give you more accurate estimation of EPC, especially when the ``cx`` + gate fidelity is close to that of single qubit gate. + To run this function, you need to know EPG of both single and two qubit gates. + For example, when you prepare 2Q RB experiment with appropriate error model, + you can define EPG of those basis gate set. Then you can estimate the EPC of + prepared RB sequence without running experiment. + .. jupyter-execute:: + import qiskit.ignis.verification.randomized_benchmarking as rb + # gate counts of your 2Q RB experiment + gpc = {0: {'cx': 1.49, 'rz': 0.25, 'x': 0.95, 'sx': 0.56}, + 1: {'cx': 1.49, 'rz': 0.24, 'x': 0.98, 'sx': 0.49}} + # EPGs from error model + epgs_q0 = {'rz': 0, 'x': 0.001, 'sx': 0.001} + epgs_q1 = {'rz': 0, 'x': 0.002, 'sx': 0.002} + epg_q01 = 0.03 TODO not accutrate for new gateset + # calculate 2Q EPC + epc_2q = rb.rb_utils.calculate_2q_epc( + gate_per_cliff=gpc, + epg_2q=epg_q01, + qubit_pair=[0, 1], + list_epgs_1q=[epgs_q0, epgs_q1]) + # calculate EPC according to the definition + fid = 1 + for qubit in (0, 1): + for epgs in (epgs_q0, epgs_q1): + for gate, val in epgs.items(): + fid *= (1 - val) ** gpc[qubit][gate] + fid *= (1 - epg_q01) ** 1.49 + epc = 1 - fid + print('Total sequence EPC: %f, 2Q gate contribution: %f' % (epc, epc_2q)) + As you can see two qubit gate contribution is dominant in this RB sequence. + References: + [1] D. C. McKay, S. Sheldon, J. A. Smolin, J. M. Chow, + and J. M. Gambetta, “Three-Qubit Randomized Benchmarking,” + Phys. Rev. Lett., vol. 122, no. 20, 2019 (arxiv:1712.06550). + Args: + gate_per_cliff: dictionary of gate per Clifford. see :func:`gates_per_clifford`. + epg_2q: EPG estimated by error model. + qubit_pair: index of two qubits to calculate EPC. + list_epgs_1q: list of single qubit EPGs of qubit listed in ``qubit_pair``. + two_qubit_name: name of two qubit gate in ``basis gates``. + Returns: + EPG of 2Q gate. + Raises: + QiskitError: when ``cx`` is not found, specified ``qubit_pair`` is not included + in the gate count dictionary, or length of ``qubit_pair`` is not 2. + """ + if len(qubit_pair) != 2: + raise QiskitError('Number of qubit is not 2.') + + n_gate_2q = gate_per_cliff[qubit_pair[0]].get(two_qubit_name, 0) + if n_gate_2q == 0: + raise QiskitError('Two qubit gate %s is not included in the `gate_per_cliff`. ' + 'Set correct `two_qubit_name` or use 2Q RB gate count.' % two_qubit_name) + + # estimate single qubit gate error contribution + alpha_1q = [1.0, 1.0] + alpha_2q = (1 - 4 / 3 * epg_2q) ** n_gate_2q + for ind, (qubit, epg_1q) in enumerate(zip(qubit_pair, list_epgs_1q)): + if qubit not in gate_per_cliff: + raise QiskitError('Qubit %d is not included in the `gate_per_cliff`' % qubit) + gpc_per_qubit = gate_per_cliff[qubit] + for gate_name, epg in epg_1q.items(): + n_gate = gpc_per_qubit.get(gate_name, 0) + alpha_1q[ind] *= (1 - 2 * epg) ** n_gate + alpha_c_2q = 1 / 5 * (alpha_1q[0] + alpha_1q[1] + 3 * alpha_1q[0] * alpha_1q[1]) * alpha_2q + + return 3 / 4 * (1 - alpha_c_2q) \ No newline at end of file diff --git a/qiskit_experiments/randomized_benchmarking/utils.py b/qiskit_experiments/randomized_benchmarking/utils.py new file mode 100644 index 0000000000..39fae1f669 --- /dev/null +++ b/qiskit_experiments/randomized_benchmarking/utils.py @@ -0,0 +1,368 @@ +# This code is part of Qiskit. +# +# (C) Copyright IBM 2019-2021. +# +# This code is licensed under the Apache License, Version 2.0. You may +# obtain a copy of this license in the LICENSE.txt file in the root directory +# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0. +# +# Any modifications or derivative works of this code must retain this +# copyright notice, and modified files need to carry a notice indicating +# that they have been altered from the originals. + +""" +RB Helper functions +""" + +from typing import List, Dict, Optional +import numpy as np +from qiskit import QiskitError + + +class RBUtils(): + """Utilities for randomized benchmarking""" + @staticmethod + def coherence_limit(nQ=2, T1_list=None, T2_list=None, + gatelen=0.1): + + """ + The error per gate (1-average_gate_fidelity) given by the T1,T2 limit. + Args: + nQ (int): number of qubits (1 and 2 supported). + T1_list (list): list of T1's (Q1,...,Qn). + T2_list (list): list of T2's (as measured, not Tphi). + If not given assume T2=2*T1 . + gatelen (float): length of the gate. + Returns: + float: coherence limited error per gate. + Raises: + ValueError: if there are invalid inputs + """ + # pylint: disable=invalid-name + + T1 = np.array(T1_list) + + if T2_list is None: + T2 = 2 * T1 + else: + T2 = np.array(T2_list) + + if len(T1) != nQ or len(T2) != nQ: + raise ValueError("T1 and/or T2 not the right length") + + coherence_limit_err = 0 + + if nQ == 1: + + coherence_limit_err = 0.5 * (1. - 2. / 3. * np.exp(-gatelen / T2[0]) - + 1. / 3. * np.exp(-gatelen / T1[0])) + + elif nQ == 2: + + T1factor = 0 + T2factor = 0 + + for i in range(2): + T1factor += 1. / 15. * np.exp(-gatelen / T1[i]) + T2factor += 2. / 15. * (np.exp(-gatelen / T2[i]) + + np.exp(-gatelen * (1. / T2[i] + 1. / T1[1 - i]))) + + T1factor += 1. / 15. * np.exp(-gatelen * np.sum(1 / T1)) + T2factor += 4. / 15. * np.exp(-gatelen * np.sum(1 / T2)) + + coherence_limit_err = 0.75 * (1. - T1factor - T2factor) + + else: + raise ValueError('Not a valid number of qubits') + + return coherence_limit_err + + @staticmethod + def calculate_1q_epg(gate_per_cliff: Dict[int, Dict[str, float]], + epc_1q: float, + qubit: int) -> Dict[str, float]: + r""" + Convert error per Clifford (EPC) into error per gates (EPGs) of single qubit basis gates. + Given that a standard 1Q RB sequences consist of ``u1``, ``u2`` and ``u3`` gates, + the EPC can be written using those EPGs: + .. math:: + EPC = 1 - (1 - EPG_{U1})^{N_{U1}} (1 - EPG_{U2})^{N_{U2}} (1 - EPG_{U3})^{N_{U3}}. + where :math:`N_{x}` is the number of gate :math:`x` per Clifford. + Assuming ``u1`` composed of virtual-Z gate, ie FrameChange instruction, + the :math:`EPG_{U1}` is estimated to be zero within the range of quantization error. + Therefore the EPC can be written as: + .. math:: + EPC = 1 - (1 - EPG_{U2})^{N_{U2}} (1 - EPG_{U3})^{N_{U3}}. + Because ``u2`` and ``u3`` gates are respectively implemented by a single and two + half-pi pulses with virtual-Z rotations, we assume :math:`EPG_{U3} = 2EPG_{U2}`. + Using this relation in the limit of :math:`EPG_{U2} \ll 1`: + .. math:: + EPC & = 1 - (1 - EPG_{U2})^{N_{U2}} (1 - 2 EPG_{U2})^{N_{U3}} \\ + & \simeq EPG_{U2}(N_{U2} + 2 N_{U3}). + Finally the EPG of each basis gate can be written using EPC and number of gates: + .. math:: + EPG_{U1} &= 0 \\ + EPG_{U2} &= EPC / (N_{U2} + 2 N_{U3}) \\ + EPG_{U3} &= 2 EPC / (N_{U2} + 2 N_{U3}) + To run this function, you first need to run a standard 1Q RB experiment with transpiled + ``QuantumCircuit`` and count the number of basis gates composing the RB circuits. + .. jupyter-execute:: + import pprint + import qiskit.ignis.verification.randomized_benchmarking as rb + # assuming we ran 1Q RB experiment for qubit 0 + gpc = {0: {'cx': 0, 'u1': 0.13, 'u2': 0.31, 'u3': 0.51}} + epc = 1.5e-3 + # calculate 1Q EPGs + epgs = rb.rb_utils.calculate_1q_epg(gate_per_cliff=gpc, epc_1q=epc, qubit=0) + pprint.pprint(epgs) + In the example, ``gpc`` can be generated by :func:`gates_per_clifford`. + The output of the function ``epgs`` can be used to calculate EPG of CNOT gate + in conjugation with 2Q RB results, see :func:`calculate_2q_epg`. + Note: + This function presupposes the basis gate consists + of ``u1``, ``u2`` and ``u3``. + Args: + gate_per_cliff: dictionary of gate per Clifford. see :func:`gates_per_clifford`. + epc_1q: EPC fit from 1Q RB experiment data. + qubit: index of qubit to calculate EPGs. + Returns: + Dictionary of EPGs of single qubit basis gates. + Raises: + QiskitError: when ``u2`` or ``u3`` is not found, ``cx`` gate count is nonzero, + or specified qubit is not included in the gate count dictionary. + """ + if qubit not in gate_per_cliff: + raise QiskitError('Qubit %d is not included in the `gate_per_cliff`' % qubit) + + gpc_per_qubit = gate_per_cliff[qubit] + + if 'u3' not in gpc_per_qubit or 'u2' not in gpc_per_qubit: + raise QiskitError('Invalid basis set is given. Use `u1`, `u2`, `u3` for basis gates.') + + n_u2 = gpc_per_qubit['u2'] + n_u3 = gpc_per_qubit['u3'] + + if gpc_per_qubit.get('cx', 0) > 0: + raise QiskitError('Two qubit gate is included in the RB sequence.') + + return {'u1': 0, 'u2': epc_1q / (n_u2 + 2 * n_u3), 'u3': 2 * epc_1q / (n_u2 + 2 * n_u3)} + + @staticmethod + def calculate_2q_epg(gate_per_cliff: Dict[int, Dict[str, float]], + epc_2q: float, + qubit_pair: List[int], + list_epgs_1q: Optional[List[Dict[str, float]]] = None, + two_qubit_name: Optional[str] = 'cx') -> float: + r""" + Convert error per Clifford (EPC) into error per gate (EPG) of two qubit ``cx`` gates. + Given that a standard 2Q RB sequences consist of ``u1``, ``u2``, ``u3``, and ``cx`` gates, + the EPG of ``cx`` gate can be roughly approximated by :math:`EPG_{CX} = EPC/N_{CX}`, + where :math:`N_{CX}` is number of ``cx`` gates per Clifford which is designed to be 1.5. + Because an error from two qubit gates are usually dominant and the contribution of + single qubit gates in 2Q RB experiments is thus able to be ignored. + If ``list_epgs_1q`` is not provided, the function returns + the EPG calculated based upon this assumption. + When we know the EPG of every single qubit gates used in the 2Q RB experiment, + we can isolate the EPC of the two qubit gate, ie :math:`EPG_{CX} = EPC_{CX}/N_{CX}` [1]. + This will give you more accurate estimation of EPG, especially when the ``cx`` + gate fidelity is close to that of single qubit gate. + To evaluate EPGs of single qubit gates, you first need to run standard 1Q RB experiments + separately and feed the fit result and gate counts to :func:`calculate_1q_epg`. + .. jupyter-execute:: + import qiskit.ignis.verification.randomized_benchmarking as rb + # assuming we ran 1Q RB experiment for qubit 0 and qubit 1 + gpc = {0: {'cx': 0, 'u1': 0.13, 'u2': 0.31, 'u3': 0.51}, + 1: {'cx': 0, 'u1': 0.10, 'u2': 0.33, 'u3': 0.51}} + epc_q0 = 1.5e-3 + epc_q1 = 5.8e-4 + # calculate 1Q EPGs + epgs_q0 = rb.rb_utils.calculate_1q_epg(gate_per_cliff=gpc, epc_1q=epc_q0, qubit=0) + epgs_q1 = rb.rb_utils.calculate_1q_epg(gate_per_cliff=gpc, epc_1q=epc_q1, qubit=1) + # assuming we ran 2Q RB experiment for qubit 0 and qubit 1 + gpc = {0: {'cx': 1.49, 'u1': 0.25, 'u2': 0.95, 'u3': 0.56}, + 1: {'cx': 1.49, 'u1': 0.24, 'u2': 0.98, 'u3': 0.49}} + epc = 2.4e-2 + # calculate 2Q EPG + epg_no_comp = rb.rb_utils.calculate_2q_epg( + gate_per_cliff=gpc, + epc_2q=epc, + qubit_pair=[0, 1]) + epg_comp = rb.rb_utils.calculate_2q_epg( + gate_per_cliff=gpc, + epc_2q=epc, + qubit_pair=[0, 1], + list_epgs_1q=[epgs_q0, epgs_q1]) + print('EPG without `list_epgs_1q`: %f, with `list_epgs_1q`: %f'%(epg_no_comp, epg_comp)) + Note: + This function presupposes the basis gate consists + of ``u1``, ``u2``, ``u3`` and ``cx``. + References: + [1] D. C. McKay, S. Sheldon, J. A. Smolin, J. M. Chow, + and J. M. Gambetta, “Three-Qubit Randomized Benchmarking,” + Phys. Rev. Lett., vol. 122, no. 20, 2019 (arxiv:1712.06550). + Args: + gate_per_cliff: dictionary of gate per Clifford. see :func:`gates_per_clifford`. + epc_2q: EPC fit from 2Q RB experiment data. + qubit_pair: index of two qubits to calculate EPG. + list_epgs_1q: list of single qubit EPGs of qubit listed in ``qubit_pair``. + two_qubit_name: name of two qubit gate in ``basis gates``. + Returns: + EPG of 2Q gate. + Raises: + QiskitError: when ``cx`` is not found, specified ``qubit_pair`` is not included + in the gate count dictionary, or length of ``qubit_pair`` is not 2. + """ + list_epgs_1q = list_epgs_1q or [] + + if len(qubit_pair) != 2: + raise QiskitError('Number of qubit is not 2.') + + # estimate single qubit gate error contribution + alpha_1q = [1.0, 1.0] + for ind, (qubit, epg_1q) in enumerate(zip(qubit_pair, list_epgs_1q)): + if qubit not in gate_per_cliff: + raise QiskitError('Qubit %d is not included in the `gate_per_cliff`' % qubit) + gpc_per_qubit = gate_per_cliff[qubit] + for gate_name, epg in epg_1q.items(): + n_gate = gpc_per_qubit.get(gate_name, 0) + alpha_1q[ind] *= (1 - 2 * epg) ** n_gate + alpha_c_1q = 1 / 5 * (alpha_1q[0] + alpha_1q[1] + 3 * alpha_1q[0] * alpha_1q[1]) + alpha_c_2q = (1 - 4 / 3 * epc_2q) / alpha_c_1q + + n_gate_2q = gate_per_cliff[qubit_pair[0]].get(two_qubit_name, 0) + + if n_gate_2q > 0: + return 3 / 4 * (1 - alpha_c_2q) / n_gate_2q + + raise QiskitError('Two qubit gate %s is not included in the `gate_per_cliff`. ' + 'Set correct `two_qubit_name` or use 2Q RB gate count.' % two_qubit_name) + + @staticmethod + def calculate_1q_epc(gate_per_cliff: Dict[int, Dict[str, float]], + epg_1q: Dict[str, float], + qubit: int) -> float: + r""" + Convert error per gate (EPG) into error per Clifford (EPC) of single qubit basis gates. + Given that we know the number of gates per Clifford :math:`N_i` and those EPGs, + we can predict EPC of that RB sequence: + .. math:: + EPC = 1 - \prod_i \left( 1 - EPG_i \right)^{N_i} + To run this function, you need to know EPG of every single qubit basis gates. + For example, when you prepare 1Q RB experiment with appropriate error model, + you can define EPG of those basis gate set. Then you can estimate the EPC of + prepared RB sequence without running experiment. + .. jupyter-execute:: + import qiskit.ignis.verification.randomized_benchmarking as rb + # gate counts of your 1Q RB experiment + gpc = {0: {'cx': 0, 'u1': 0.13, 'u2': 0.31, 'u3': 0.51}} + # EPGs from error model + epgs_q0 = {'u1': 0, 'u2': 0.001, 'u3': 0.002} + # calculate 1Q EPC + epc = rb.rb_utils.calculate_1q_epc( + gate_per_cliff=gpc, + epg_1q=epgs_q0, + qubit=0) + print(epc) + Args: + gate_per_cliff: dictionary of gate per Clifford. see :func:`gates_per_clifford`. + epg_1q: EPG of single qubit gates estimated by error model. + qubit: index of qubit to calculate EPC. + Returns: + EPG of 2Q gate. + Raises: + QiskitError: when specified ``qubit`` is not included in the gate count dictionary + """ + if qubit not in gate_per_cliff: + raise QiskitError('Qubit %d is not included in the `gate_per_cliff`' % qubit) + + fid = 1 + gpc_per_qubit = gate_per_cliff[qubit] + + for gate_name, epg in epg_1q.items(): + n_gate = gpc_per_qubit.get(gate_name, 0) + fid *= (1 - epg) ** n_gate + + return 1 - fid + + @staticmethod + def calculate_2q_epc(gate_per_cliff: Dict[int, Dict[str, float]], + epg_2q: float, + qubit_pair: List[int], + list_epgs_1q: List[Dict[str, float]], + two_qubit_name: Optional[str] = 'cx') -> float: + r""" + Convert error per gate (EPG) into error per Clifford (EPC) of two qubit ``cx`` gates. + Given that we know the number of gates per Clifford :math:`N_i` and those EPGs, + we can predict EPC of that RB sequence: + .. math:: + EPC = 1 - \prod_i \left( 1 - EPG_i \right)^{N_i} + This function isolates the contribution of two qubit gate to the EPC [1]. + This will give you more accurate estimation of EPC, especially when the ``cx`` + gate fidelity is close to that of single qubit gate. + To run this function, you need to know EPG of both single and two qubit gates. + For example, when you prepare 2Q RB experiment with appropriate error model, + you can define EPG of those basis gate set. Then you can estimate the EPC of + prepared RB sequence without running experiment. + .. jupyter-execute:: + import qiskit.ignis.verification.randomized_benchmarking as rb + # gate counts of your 2Q RB experiment + gpc = {0: {'cx': 1.49, 'u1': 0.25, 'u2': 0.95, 'u3': 0.56}, + 1: {'cx': 1.49, 'u1': 0.24, 'u2': 0.98, 'u3': 0.49}} + # EPGs from error model + epgs_q0 = {'u1': 0, 'u2': 0.001, 'u3': 0.002} + epgs_q1 = {'u1': 0, 'u2': 0.002, 'u3': 0.004} + epg_q01 = 0.03 + # calculate 2Q EPC + epc_2q = rb.rb_utils.calculate_2q_epc( + gate_per_cliff=gpc, + epg_2q=epg_q01, + qubit_pair=[0, 1], + list_epgs_1q=[epgs_q0, epgs_q1]) + # calculate EPC according to the definition + fid = 1 + for qubit in (0, 1): + for epgs in (epgs_q0, epgs_q1): + for gate, val in epgs.items(): + fid *= (1 - val) ** gpc[qubit][gate] + fid *= (1 - epg_q01) ** 1.49 + epc = 1 - fid + print('Total sequence EPC: %f, 2Q gate contribution: %f' % (epc, epc_2q)) + As you can see two qubit gate contribution is dominant in this RB sequence. + References: + [1] D. C. McKay, S. Sheldon, J. A. Smolin, J. M. Chow, + and J. M. Gambetta, “Three-Qubit Randomized Benchmarking,” + Phys. Rev. Lett., vol. 122, no. 20, 2019 (arxiv:1712.06550). + Args: + gate_per_cliff: dictionary of gate per Clifford. see :func:`gates_per_clifford`. + epg_2q: EPG estimated by error model. + qubit_pair: index of two qubits to calculate EPC. + list_epgs_1q: list of single qubit EPGs of qubit listed in ``qubit_pair``. + two_qubit_name: name of two qubit gate in ``basis gates``. + Returns: + EPG of 2Q gate. + Raises: + QiskitError: when ``cx`` is not found, specified ``qubit_pair`` is not included + in the gate count dictionary, or length of ``qubit_pair`` is not 2. + """ + if len(qubit_pair) != 2: + raise QiskitError('Number of qubit is not 2.') + + n_gate_2q = gate_per_cliff[qubit_pair[0]].get(two_qubit_name, 0) + if n_gate_2q == 0: + raise QiskitError('Two qubit gate %s is not included in the `gate_per_cliff`. ' + 'Set correct `two_qubit_name` or use 2Q RB gate count.' + % two_qubit_name) + + # estimate single qubit gate error contribution + alpha_1q = [1.0, 1.0] + alpha_2q = (1 - 4 / 3 * epg_2q) ** n_gate_2q + for ind, (qubit, epg_1q) in enumerate(zip(qubit_pair, list_epgs_1q)): + if qubit not in gate_per_cliff: + raise QiskitError('Qubit %d is not included in the `gate_per_cliff`' % qubit) + gpc_per_qubit = gate_per_cliff[qubit] + for gate_name, epg in epg_1q.items(): + n_gate = gpc_per_qubit.get(gate_name, 0) + alpha_1q[ind] *= (1 - 2 * epg) ** n_gate + alpha_c_2q = 1 / 5 * (alpha_1q[0] + alpha_1q[1] + 3 * alpha_1q[0] * alpha_1q[1]) * alpha_2q + + return 3 / 4 * (1 - alpha_c_2q) \ No newline at end of file diff --git a/requirements-dev.txt b/requirements-dev.txt index cc2c77b04c..1e4aa6c841 100644 --- a/requirements-dev.txt +++ b/requirements-dev.txt @@ -10,3 +10,10 @@ pygments>=2.4 reno>=3.2.0 sphinx-panels nbsphinx + +numpy~=1.19.2 +qiskit~=0.25.3 +ddt~=1.4.2 +setuptools~=56.1.0 +scipy~=1.5.2 +matplotlib~=3.3.2 \ No newline at end of file diff --git a/test/data_processing/fake_experiment.py b/test/data_processing/fake_experiment.py new file mode 100644 index 0000000000..d925c8d68e --- /dev/null +++ b/test/data_processing/fake_experiment.py @@ -0,0 +1,51 @@ +# This code is part of Qiskit. +# +# (C) Copyright IBM 2021. +# +# This code is licensed under the Apache License, Version 2.0. You may +# obtain a copy of this license in the LICENSE.txt file in the root directory +# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0. +# +# Any modifications or derivative works of this code must retain this +# copyright notice, and modified files need to carry a notice indicating +# that they have been altered from the originals. + +"""A FakeExperiment for data processor testing.""" + +from qiskit.test import QiskitTestCase +from qiskit.qobj.common import QobjExperimentHeader +from qiskit_experiments.base_experiment import BaseExperiment + + +class FakeExperiment(BaseExperiment): + """Fake experiment class for testing.""" + + def __init__(self): + """Initialise the fake experiment.""" + self._type = None + super().__init__((0,), "fake_test_experiment") + + def circuits(self, backend=None): + """Fake circuits.""" + return [] + + +class BaseDataProcessorTest(QiskitTestCase): + """Define some basic setup functionality for data processor tests.""" + + def setUp(self): + """Define variables needed for most tests.""" + super().setUp() + + self.base_result_args = dict( + backend_name="test_backend", + backend_version="1.0.0", + qobj_id="id-123", + job_id="job-123", + success=True, + ) + + self.header = QobjExperimentHeader( + memory_slots=2, + metadata={"experiment_type": "fake_test_experiment"}, + ) diff --git a/test/data_processing/test_data_processing.py b/test/data_processing/test_data_processing.py index 0322a83365..c07da481a6 100644 --- a/test/data_processing/test_data_processing.py +++ b/test/data_processing/test_data_processing.py @@ -12,50 +12,31 @@ """Data processor tests.""" -import numpy as np +# pylint: disable=unbalanced-tuple-unpacking +from test.data_processing.fake_experiment import FakeExperiment, BaseDataProcessorTest +import numpy as np from qiskit.result.models import ExperimentResultData, ExperimentResult from qiskit.result import Result -from qiskit.test import QiskitTestCase -from qiskit.qobj.common import QobjExperimentHeader + from qiskit_experiments import ExperimentData -from qiskit_experiments.base_experiment import BaseExperiment from qiskit_experiments.data_processing.data_processor import DataProcessor from qiskit_experiments.data_processing.exceptions import DataProcessorError from qiskit_experiments.data_processing.nodes import ( + AverageData, + SVD, ToReal, - ToRealAvg, ToImag, - ToImagAvg, Probability, ) -class FakeExperiment(BaseExperiment): - """Fake experiment class for testing.""" - - def __init__(self): - """Initialise the fake experiment.""" - self._type = None - super().__init__((0,), "fake_test_experiment") - - def circuits(self, backend=None, **circuit_options): - """Fake circuits.""" - return [] - - -class DataProcessorTest(QiskitTestCase): +class DataProcessorTest(BaseDataProcessorTest): """Class to test DataProcessor.""" def setUp(self): """Setup variables used for testing.""" - self.base_result_args = dict( - backend_name="test_backend", - backend_version="1.0.0", - qobj_id="id-123", - job_id="job-123", - success=True, - ) + super().setUp() mem1 = ExperimentResultData( memory=[ @@ -73,50 +54,26 @@ def setUp(self): ] ) - header1 = QobjExperimentHeader( - clbit_labels=[["meas", 0], ["meas", 1]], - creg_sizes=[["meas", 2]], - global_phase=0.0, - memory_slots=2, - metadata={"experiment_type": "fake_test_experiment", "x_values": 0.0}, - ) - - header2 = QobjExperimentHeader( - clbit_labels=[["meas", 0], ["meas", 1]], - creg_sizes=[["meas", 2]], - global_phase=0.0, - memory_slots=2, - metadata={"experiment_type": "fake_test_experiment", "x_values": 1.0}, - ) - - res1 = ExperimentResult(shots=3, success=True, meas_level=1, data=mem1, header=header1) - res2 = ExperimentResult(shots=3, success=True, meas_level=1, data=mem2, header=header2) + res1 = ExperimentResult(shots=3, success=True, meas_level=1, data=mem1, header=self.header) + res2 = ExperimentResult(shots=3, success=True, meas_level=1, data=mem2, header=self.header) self.result_lvl1 = Result(results=[res1, res2], **self.base_result_args) raw_counts = {"0x0": 4, "0x2": 6} data = ExperimentResultData(counts=dict(**raw_counts)) - header = QobjExperimentHeader( - metadata={"experiment_type": "fake_test_experiment"}, - clbit_labels=[["c", 0], ["c", 1]], - creg_sizes=[["c", 2]], - n_qubits=2, - memory_slots=2, - ) - res = ExperimentResult(shots=9, success=True, meas_level=2, data=data, header=header) + res = ExperimentResult(shots=9, success=True, meas_level=2, data=data, header=self.header) self.exp_data_lvl2 = ExperimentData(FakeExperiment()) self.exp_data_lvl2.add_data(Result(results=[res], **self.base_result_args)) - super().setUp() - def test_empty_processor(self): """Check that a DataProcessor without steps does nothing.""" data_processor = DataProcessor("counts") - datum = data_processor(self.exp_data_lvl2.data(0)) + datum, error = data_processor(self.exp_data_lvl2.data(0)) self.assertEqual(datum, {"00": 4, "10": 6}) + self.assertIsNone(error) - datum, history = data_processor.call_with_history(self.exp_data_lvl2.data(0)) + datum, error, history = data_processor.call_with_history(self.exp_data_lvl2.data(0)) self.assertEqual(datum, {"00": 4, "10": 6}) self.assertEqual(history, []) @@ -127,7 +84,7 @@ def test_to_real(self): exp_data = ExperimentData(FakeExperiment()) exp_data.add_data(self.result_lvl1) - new_data = processor(exp_data.data(0)) + new_data, error = processor(exp_data.data(0)) expected_old = { "memory": [ @@ -135,16 +92,17 @@ def test_to_real(self): [[442170.0, -19283206.0], [-5279410.0, -15339630.0]], [[3016514.0, -14548009.0], [-3404756.0, -16743348.0]], ], - "metadata": {"experiment_type": "fake_test_experiment", "x_values": 0.0}, + "metadata": {"experiment_type": "fake_test_experiment"}, } expected_new = np.array([[1103.26, 2959.012], [442.17, -5279.41], [3016.514, -3404.7560]]) self.assertEqual(exp_data.data(0), expected_old) self.assertTrue(np.allclose(new_data, expected_new)) + self.assertIsNone(error) # Test that we can call with history. - new_data, history = processor.call_with_history(exp_data.data(0)) + new_data, error, history = processor.call_with_history(exp_data.data(0)) self.assertEqual(exp_data.data(0), expected_old) self.assertTrue(np.allclose(new_data, expected_new)) @@ -160,7 +118,7 @@ def test_to_imag(self): exp_data = ExperimentData(FakeExperiment()) exp_data.add_data(self.result_lvl1) - new_data = processor(exp_data.data(0)) + new_data, error = processor(exp_data.data(0)) expected_old = { "memory": [ @@ -168,7 +126,7 @@ def test_to_imag(self): [[442170.0, -19283206.0], [-5279410.0, -15339630.0]], [[3016514.0, -14548009.0], [-3404756.0, -16743348.0]], ], - "metadata": {"experiment_type": "fake_test_experiment", "x_values": 0.0}, + "metadata": {"experiment_type": "fake_test_experiment"}, } expected_new = np.array( @@ -181,9 +139,10 @@ def test_to_imag(self): self.assertEqual(exp_data.data(0), expected_old) self.assertTrue(np.allclose(new_data, expected_new)) + self.assertIsNone(error) # Test that we can call with history. - new_data, history = processor.call_with_history(exp_data.data(0)) + new_data, error, history = processor.call_with_history(exp_data.data(0)) self.assertEqual(exp_data.data(0), expected_old) self.assertTrue(np.allclose(new_data, expected_new)) @@ -196,10 +155,10 @@ def test_populations(self): processor = DataProcessor("counts") processor.append(Probability("00")) - new_data = processor(self.exp_data_lvl2.data(0)) + new_data, error = processor(self.exp_data_lvl2.data(0)) - self.assertEqual(new_data[0], 0.4) - self.assertEqual(new_data[1], 0.4 * (1 - 0.4) / 10) + self.assertEqual(new_data, 0.4) + self.assertEqual(error, 0.4 * (1 - 0.4) / 10) def test_validation(self): """Test the validation mechanism.""" @@ -212,21 +171,13 @@ def test_validation(self): processor({"counts": [0, 1, 2]}) -class TestIQSingleAvg(QiskitTestCase): +class TestIQSingleAvg(BaseDataProcessorTest): """Test the IQ data processing nodes single and average.""" def setUp(self): """Setup some IQ data.""" super().setUp() - self.base_result_args = dict( - backend_name="test_backend", - backend_version="1.0.0", - qobj_id="id-123", - job_id="job-123", - success=True, - ) - mem_avg = ExperimentResultData( memory=[[-539698.0, -153030784.0], [5541283.0, -160369600.0]] ) @@ -241,23 +192,16 @@ def setUp(self): ] ) - header = QobjExperimentHeader( - metadata={"experiment_type": "fake_test_experiment"}, - clbit_labels=[["c", 0], ["c", 1]], - creg_sizes=[["c", 2]], - n_qubits=2, - memory_slots=2, - ) res_single = ExperimentResult( shots=3, success=True, meas_level=1, meas_return="single", data=mem_single, - header=header, + header=self.header, ) res_avg = ExperimentResult( - shots=6, success=True, meas_level=1, meas_return="avg", data=mem_avg, header=header + shots=6, success=True, meas_level=1, meas_return="avg", data=mem_avg, header=self.header ) # result_single = Result(results=[res_single], **self.base_result_args) @@ -272,13 +216,11 @@ def setUp(self): def test_avg_and_single(self): """Test that the different nodes process the data correctly.""" - real_single = DataProcessor("memory", [ToReal(scale=1)]) - imag_single = DataProcessor("memory", [ToImag(scale=1)]) - real_avg = DataProcessor("memory", [ToRealAvg(scale=1)]) - imag_avg = DataProcessor("memory", [ToImagAvg(scale=1)]) + to_real = DataProcessor("memory", [ToReal(scale=1)]) + to_imag = DataProcessor("memory", [ToImag(scale=1)]) # Test the real single shot node - new_data = real_single(self.exp_data_single.data(0)) + new_data, error = to_real(self.exp_data_single.data(0)) expected = np.array( [ [-56470872.0, -53407256.0], @@ -290,12 +232,10 @@ def test_avg_and_single(self): ] ) self.assertTrue(np.allclose(new_data, expected)) - - with self.assertRaises(DataProcessorError): - real_single(self.exp_data_avg.data(0)) + self.assertIsNone(error) # Test the imaginary single shot node - new_data = imag_single(self.exp_data_single.data(0)) + new_data, error = to_imag(self.exp_data_single.data(0)) expected = np.array( [ [-136691568.0, -176278624.0], @@ -309,12 +249,138 @@ def test_avg_and_single(self): self.assertTrue(np.allclose(new_data, expected)) # Test the real average node - new_data = real_avg(self.exp_data_avg.data(0)) + new_data, error = to_real(self.exp_data_avg.data(0)) self.assertTrue(np.allclose(new_data, np.array([-539698.0, 5541283.0]))) # Test the imaginary average node - new_data = imag_avg(self.exp_data_avg.data(0)) + new_data, error = to_imag(self.exp_data_avg.data(0)) self.assertTrue(np.allclose(new_data, np.array([-153030784.0, -160369600.0]))) - with self.assertRaises(DataProcessorError): - real_avg(self.exp_data_single.data(0)) + +class TestAveragingAndSVD(BaseDataProcessorTest): + """Test the averaging of single-shot IQ data followed by a SVD.""" + + def setUp(self): + """Here, single-shots average to points at plus/minus 1.""" + super().setUp() + + circ_es = ExperimentResultData( + memory=[ + [[1.1, 0.9], [-0.8, 1.0]], + [[1.2, 1.1], [-0.9, 1.0]], + [[0.8, 1.1], [-1.2, 1.0]], + [[0.9, 0.9], [-1.1, 1.0]], + ] + ) + + circ_gs = ExperimentResultData( + memory=[ + [[-1.1, -0.9], [0.8, -1.0]], + [[-1.2, -1.1], [0.9, -1.0]], + [[-0.8, -1.1], [1.2, -1.0]], + [[-0.9, -0.9], [1.1, -1.0]], + ] + ) + + circ_x90p = ExperimentResultData( + memory=[ + [[-1.0, -1.0], [1.0, -1.0]], + [[-1.0, -1.0], [1.0, -1.0]], + [[1.0, 1.0], [-1.0, 1.0]], + [[1.0, 1.0], [-1.0, 1.0]], + ] + ) + + circ_x45p = ExperimentResultData( + memory=[ + [[-1.0, -1.0], [1.0, -1.0]], + [[-1.0, -1.0], [1.0, -1.0]], + [[-1.0, -1.0], [1.0, -1.0]], + [[1.0, 1.0], [-1.0, 1.0]], + [[-1.0, -1.0], [1.0, -1.0]], + [[-1.0, -1.0], [1.0, -1.0]], + [[-1.0, -1.0], [1.0, -1.0]], + [[1.0, 1.0], [-1.0, 1.0]], + ] + ) + + res_es = ExperimentResult( + shots=4, + success=True, + meas_level=1, + meas_return="single", + data=circ_es, + header=self.header, + ) + + res_gs = ExperimentResult( + shots=4, + success=True, + meas_level=1, + meas_return="single", + data=circ_gs, + header=self.header, + ) + + res_x90p = ExperimentResult( + shots=4, + success=True, + meas_level=1, + meas_return="single", + data=circ_x90p, + header=self.header, + ) + + res_x45p = ExperimentResult( + shots=8, + success=True, + meas_level=1, + meas_return="single", + data=circ_x45p, + header=self.header, + ) + + self.data = ExperimentData(FakeExperiment()) + self.data.add_data( + Result(results=[res_es, res_gs, res_x90p, res_x45p], **self.base_result_args) + ) + + def test_averaging(self): + """Test that averaging of the datums produces the expected IQ points.""" + + processor = DataProcessor("memory", [AverageData()]) + + # Test that we get the expected outcome for the excited state + processed, error = processor(self.data.data(0)) + expected_avg = np.array([[1.0, 1.0], [-1.0, 1.0]]) + expected_std = np.array([[0.15811388300841894, 0.1], [0.15811388300841894, 0.0]]) / 2.0 + self.assertTrue(np.allclose(processed, expected_avg)) + self.assertTrue(np.allclose(error, expected_std)) + + # Test that we get the expected outcome for the ground state + processed, error = processor(self.data.data(1)) + expected_avg = np.array([[-1.0, -1.0], [1.0, -1.0]]) + expected_std = np.array([[0.15811388300841894, 0.1], [0.15811388300841894, 0.0]]) / 2.0 + self.assertTrue(np.allclose(processed, expected_avg)) + self.assertTrue(np.allclose(error, expected_std)) + + def test_averaging_and_svd(self): + """Test averaging followed by a SVD.""" + + processor = DataProcessor("memory", [AverageData(), SVD()]) + + # Test training using the calibration points + self.assertFalse(processor.is_trained) + processor.train([self.data.data(idx) for idx in [0, 1]]) + self.assertTrue(processor.is_trained) + + # Test the x90p rotation + processed, error = processor(self.data.data(2)) + self.assertTrue(np.allclose(processed, np.array([0, 0]))) + self.assertTrue(np.allclose(error, np.array([0.5, 0.5]))) + + # Test the x45p rotation + processed, error = processor(self.data.data(3)) + expected_std = np.array([np.std([1, 1, 1, -1, 1, 1, 1, -1]) / np.sqrt(8.0)] * 2) + self.assertTrue(np.allclose(processed, np.array([0.5, -0.5]) / np.sqrt(2.0))) + self.assertTrue(np.allclose(error, expected_std)) diff --git a/test/data_processing/test_nodes.py b/test/data_processing/test_nodes.py new file mode 100644 index 0000000000..fb7a76cde2 --- /dev/null +++ b/test/data_processing/test_nodes.py @@ -0,0 +1,220 @@ +# This code is part of Qiskit. +# +# (C) Copyright IBM 2021. +# +# This code is licensed under the Apache License, Version 2.0. You may +# obtain a copy of this license in the LICENSE.txt file in the root directory +# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0. +# +# Any modifications or derivative works of this code must retain this +# copyright notice, and modified files need to carry a notice indicating +# that they have been altered from the originals. + +"""Data processor tests.""" + +# pylint: disable=unbalanced-tuple-unpacking + +from test.data_processing.fake_experiment import FakeExperiment, BaseDataProcessorTest + +from typing import Any, List +import numpy as np + +from qiskit.result.models import ExperimentResultData, ExperimentResult +from qiskit.result import Result +from qiskit.test import QiskitTestCase +from qiskit_experiments.experiment_data import ExperimentData +from qiskit_experiments.data_processing.nodes import SVD, AverageData +from qiskit_experiments.data_processing.data_processor import DataProcessor + + +class TestAveraging(QiskitTestCase): + """Test the averaging nodes.""" + + def test_simple(self): + """Simple test of averaging.""" + + datum = np.array([[1, 2], [3, 4]]) + + node = AverageData(axis=1) + self.assertTrue(np.allclose(node(datum)[0], np.array([1.5, 3.5]))) + self.assertTrue(np.allclose(node(datum)[1], np.array([0.5, 0.5]) / np.sqrt(2))) + + node = AverageData(axis=0) + self.assertTrue(np.allclose(node(datum)[0], np.array([2.0, 3.0]))) + self.assertTrue(np.allclose(node(datum)[1], np.array([1.0, 1.0]) / np.sqrt(2))) + + +class TestSVD(BaseDataProcessorTest): + """Test the SVD nodes.""" + + def create_experiment(self, iq_data: List[Any], single_shot: bool = False): + """Populate avg_iq_data to use it for testing. + + Args: + iq_data: A List of IQ data. + single_shot: Indicates if the data is single-shot or not. + """ + results = [] + if not single_shot: + for circ_data in iq_data: + res = ExperimentResult( + success=True, + meas_level=1, + meas_return="avg", + data=ExperimentResultData(memory=circ_data), + header=self.header, + shots=1024, + ) + results.append(res) + else: + res = ExperimentResult( + success=True, + meas_level=1, + meas_return="single", + data=ExperimentResultData(memory=iq_data), + header=self.header, + shots=1024, + ) + results.append(res) + + # pylint: disable=attribute-defined-outside-init + self.iq_experiment = ExperimentData(FakeExperiment()) + self.iq_experiment.add_data(Result(results=results, **self.base_result_args)) + + def test_simple_data(self): + """ + A simple setting where the IQ data of qubit 0 is oriented along (1,1) and + the IQ data of qubit 1 is oriented along (1,-1). + """ + + iq_data = [[[0.0, 0.0], [0.0, 0.0]], [[1.0, 1.0], [-1.0, 1.0]], [[-1.0, -1.0], [1.0, -1.0]]] + + self.create_experiment(iq_data) + + iq_svd = SVD() + iq_svd.train([datum["memory"] for datum in self.iq_experiment.data()]) + + # qubit 0 IQ data is oriented along (1,1) + self.assertTrue(np.allclose(iq_svd._main_axes[0], np.array([-1, -1]) / np.sqrt(2))) + + # qubit 1 IQ data is oriented along (1, -1) + self.assertTrue(np.allclose(iq_svd._main_axes[1], np.array([-1, 1]) / np.sqrt(2))) + + processed, _ = iq_svd(np.array([[1, 1], [1, -1]])) + expected = np.array([-1, -1]) / np.sqrt(2) + self.assertTrue(np.allclose(processed, expected)) + + processed, _ = iq_svd(np.array([[2, 2], [2, -2]])) + self.assertTrue(np.allclose(processed, expected * 2)) + + # Check that orthogonal data gives 0. + processed, _ = iq_svd(np.array([[1, -1], [1, 1]])) + expected = np.array([0, 0]) + self.assertTrue(np.allclose(processed, expected)) + + def test_svd(self): + """Use IQ data gathered from the hardware.""" + + # This data is primarily oriented along the real axis with a slight tilt. + # The is a large offset in the imaginary dimension when comparing qubits + # 0 and 1. + iq_data = [ + [[-6.20601501e14, -1.33257051e15], [-1.70921324e15, -4.05881657e15]], + [[-5.80546502e14, -1.33492509e15], [-1.65094637e15, -4.05926942e15]], + [[-4.04649069e14, -1.33191056e15], [-1.29680377e15, -4.03604815e15]], + [[-2.22203874e14, -1.30291309e15], [-8.57663429e14, -3.97784973e15]], + [[-2.92074029e13, -1.28578530e15], [-9.78824053e13, -3.92071056e15]], + [[1.98056981e14, -1.26883024e15], [3.77157017e14, -3.87460328e15]], + [[4.29955888e14, -1.25022995e15], [1.02340118e15, -3.79508679e15]], + [[6.38981344e14, -1.25084614e15], [1.68918514e15, -3.78961044e15]], + [[7.09988897e14, -1.21906634e15], [1.91914171e15, -3.73670664e15]], + [[7.63169115e14, -1.20797552e15], [2.03772603e15, -3.74653863e15]], + ] + + self.create_experiment(iq_data) + + iq_svd = SVD() + iq_svd.train([datum["memory"] for datum in self.iq_experiment.data()]) + + self.assertTrue(np.allclose(iq_svd._main_axes[0], np.array([-0.99633018, -0.08559302]))) + self.assertTrue(np.allclose(iq_svd._main_axes[1], np.array([-0.99627747, -0.0862044]))) + + def test_svd_error(self): + """Test the error formula of the SVD.""" + + iq_svd = SVD() + iq_svd._main_axes = np.array([[1.0, 0.0]]) + iq_svd._scales = [1.0] + iq_svd._means = [[0.0, 0.0]] + + # Since the axis is along the real part the imaginary error is irrelevant. + processed, error = iq_svd([[1.0, 0.2]], [[0.2, 0.1]]) + self.assertEqual(processed, np.array([1.0])) + self.assertEqual(error, np.array([0.2])) + + # Since the axis is along the real part the imaginary error is irrelevant. + processed, error = iq_svd([[1.0, 0.2]], [[0.2, 0.3]]) + self.assertEqual(processed, np.array([1.0])) + self.assertEqual(error, np.array([0.2])) + + # Title the axis to an angle of 36.9... degrees + iq_svd._main_axes = np.array([[0.8, 0.6]]) + processed, error = iq_svd([[1.0, 0.0]], [[0.2, 0.3]]) + cos_ = np.cos(np.arctan(0.6 / 0.8)) + sin_ = np.sin(np.arctan(0.6 / 0.8)) + self.assertEqual(processed, np.array([cos_])) + expected_error = np.sqrt((0.2 * cos_) ** 2 + (0.3 * sin_) ** 2) + self.assertEqual(error, np.array([expected_error])) + + def test_train_svd_processor(self): + """Test that we can train a DataProcessor with an SVD.""" + + processor = DataProcessor("memory", [SVD()]) + + self.assertFalse(processor.is_trained) + + iq_data = [[[0.0, 0.0], [0.0, 0.0]], [[1.0, 1.0], [-1.0, 1.0]], [[-1.0, -1.0], [1.0, -1.0]]] + self.create_experiment(iq_data) + + processor.train(self.iq_experiment.data()) + + self.assertTrue(processor.is_trained) + + # Check that we can use the SVD + iq_data = [[[2, 2], [2, -2]]] + self.create_experiment(iq_data) + + processed, _ = processor(self.iq_experiment.data(0)) + expected = np.array([-2, -2]) / np.sqrt(2) + self.assertTrue(np.allclose(processed, expected)) + + def test_iq_averaging(self): + """Test averaging of IQ-data.""" + + iq_data = [ + [[-6.20601501e14, -1.33257051e15], [-1.70921324e15, -4.05881657e15]], + [[-5.80546502e14, -1.33492509e15], [-1.65094637e15, -4.05926942e15]], + [[-4.04649069e14, -1.33191056e15], [-1.29680377e15, -4.03604815e15]], + [[-2.22203874e14, -1.30291309e15], [-8.57663429e14, -3.97784973e15]], + [[-2.92074029e13, -1.28578530e15], [-9.78824053e13, -3.92071056e15]], + [[1.98056981e14, -1.26883024e15], [3.77157017e14, -3.87460328e15]], + [[4.29955888e14, -1.25022995e15], [1.02340118e15, -3.79508679e15]], + [[6.38981344e14, -1.25084614e15], [1.68918514e15, -3.78961044e15]], + [[7.09988897e14, -1.21906634e15], [1.91914171e15, -3.73670664e15]], + [[7.63169115e14, -1.20797552e15], [2.03772603e15, -3.74653863e15]], + ] + + self.create_experiment(iq_data, single_shot=True) + + avg_iq = AverageData() + + avg_datum, error = avg_iq(self.iq_experiment.data(0)["memory"]) + + expected_avg = np.array([[8.82943876e13, -1.27850527e15], [1.43410186e14, -3.89952402e15]]) + + expected_std = np.array( + [[5.07650185e14, 4.44664719e13], [1.40522641e15, 1.22326831e14]] + ) / np.sqrt(10) + + self.assertTrue(np.allclose(avg_datum, expected_avg)) + self.assertTrue(np.allclose(error, expected_std)) diff --git a/test/test_rb.py b/test/test_rb.py new file mode 100644 index 0000000000..df8cb9764c --- /dev/null +++ b/test/test_rb.py @@ -0,0 +1,159 @@ +# -*- coding: utf-8 -*- + +# This code is part of Qiskit. +# +# (C) Copyright IBM 2021. +# +# This code is licensed under the Apache License, Version 2.0. You may +# obtain a copy of this license in the LICENSE.txt file in the root directory +# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0. +# +# Any modifications or derivative works of this code must retain this +# copyright notice, and modified files need to carry a notice indicating +# that they have been altered from the originals. + +""" +A Tester for the RB experiment +""" + +import numpy as np +from ddt import ddt, data, unpack +from qiskit.quantum_info.operators.predicates import matrix_equal +from qiskit.quantum_info import Clifford +from qiskit.test import QiskitTestCase +from qiskit.test.mock import FakeParis +from qiskit.circuit.library import XGate, CXGate +import qiskit_experiments as qe + + +@ddt +class TestRB(QiskitTestCase): + """ + A test class for the RB Experiment to check that the RBExperiment class is working correctly. + """ + + @data([[3]], [[4, 7]], [[0, 5, 3]]) + @unpack + def test_rb_experiment(self, qubits: list): + """ + Initializes data and executes an RB experiment with specific parameters. + Args: + qubits (list): A list containing qubit indices for the experiment + """ + backend = FakeParis() + exp_attributes = { + "qubits": qubits, + "lengths": [1, 4, 6, 9, 13, 16], + "num_samples": 2, + "seed": 100, + } + rb = qe.randomized_benchmarking + rb_exp = rb.RBExperiment( + exp_attributes["qubits"], + exp_attributes["lengths"], + num_samples=exp_attributes["num_samples"], + seed=exp_attributes["seed"], + ) + experiment_obj = rb_exp.run(backend) + exp_data = experiment_obj.experiment + exp_circuits = rb_exp.circuits() + self.validate_metadata(exp_circuits, exp_attributes) + self.validate_circuit_data(exp_data, exp_attributes) + self.is_identity(exp_circuits) + + def is_identity(self, circuits: list): + """Standard randomized benchmarking test - Identity check. + (assuming all the operator are spanned by clifford group) + Args: + circuits (list): list of the circuits which we want to check + """ + for qc in circuits: + num_qubits = qc.num_qubits + qc.remove_final_measurements() + # Checking if the matrix representation is the identity matrix + self.assertTrue( + matrix_equal(Clifford(qc).to_matrix(), np.identity(2 ** num_qubits)), + "Clifford sequence doesn't result in the identity matrix.", + ) + + def validate_metadata(self, circuits: list, exp_attributes: dict): + """ + Validate the fields in "metadata" for the experiment. + Args: + circuits (list): A list containing quantum circuits + exp_attributes (dict): A dictionary with the experiment variable and values + """ + for qc in circuits: + self.assertTrue( + qc.metadata["xval"] in exp_attributes["lengths"], + "The number of gates in the experiment metadata doesn't match " + "any of the provided lengths", + ) + self.assertTrue( + qc.metadata["qubits"] == tuple(exp_attributes["qubits"]), + "The qubits indices in the experiment metadata doesn't match to the one provided.", + ) + + def validate_circuit_data( + self, + experiment: qe.randomized_benchmarking.rb_experiment.RBExperiment, + exp_attributes: dict, + ): + """ + Validate that the metadata of the experiment after it had run matches the one provided. + Args: + experiment: The experiment data and results after it run + exp_attributes (dict): A dictionary with the experiment variable and values + """ + self.assertTrue( + exp_attributes["lengths"] == experiment.experiment_options.lengths, + "The number of gates in the experiment doesn't match to the one in the metadata.", + ) + self.assertTrue( + exp_attributes["num_samples"] == experiment.experiment_options.num_samples, + "The number of samples in the experiment doesn't match to the one in the metadata.", + ) + self.assertTrue( + tuple(exp_attributes["qubits"]) == experiment.physical_qubits, + "The qubits indices in the experiment doesn't match to the one in the metadata.", + ) + + +@ddt +class TestInterleavedRB(TestRB): + """ + A test class for the interleaved RB Experiment to check that the + InterleavedRBExperiment class is working correctly. + """ + + @data([XGate(), [3]], [CXGate(), [4, 7]]) + @unpack + def test_interleaved_rb_experiment(self, interleaved_element: "Gate", qubits: list): + """ + Initializes data and executes an interleaved RB experiment with specific parameters. + Args: + interleaved_element: The Clifford element to interleave + qubits (list): A list containing qubit indices for the experiment + """ + backend = FakeParis() + exp_attributes = { + "interleaved_element": interleaved_element, + "qubits": qubits, + "lengths": [1, 4, 6, 9, 13, 16], + "num_samples": 2, + "seed": 100, + } + rb = qe.randomized_benchmarking + rb_exp = rb.InterleavedRBExperiment( + exp_attributes["interleaved_element"], + exp_attributes["qubits"], + exp_attributes["lengths"], + num_samples=exp_attributes["num_samples"], + seed=exp_attributes["seed"], + ) + experiment_obj = rb_exp.run(backend) + exp_data = experiment_obj.experiment + exp_circuits = rb_exp.circuits() + self.validate_metadata(exp_circuits, exp_attributes) + self.validate_circuit_data(exp_data, exp_attributes) + self.is_identity(exp_circuits) diff --git a/test/test_t1.py b/test/test_t1.py index 75f794db11..6ba8c416e3 100644 --- a/test/test_t1.py +++ b/test/test_t1.py @@ -17,6 +17,7 @@ import unittest import numpy as np +from qiskit.test import QiskitTestCase from qiskit.providers import BaseBackend from qiskit.providers.models import QasmBackendConfiguration from qiskit.result import Result @@ -134,7 +135,7 @@ def run(self, qobj): return Result.from_dict(result) -class TestT1(unittest.TestCase): +class TestT1(QiskitTestCase): """ Test measurement of T1 """ diff --git a/test/test_t2star.py b/test/test_t2star.py new file mode 100644 index 0000000000..a7ce03920e --- /dev/null +++ b/test/test_t2star.py @@ -0,0 +1,270 @@ +# (C) Copyright IBM 2021. +# +# This code is licensed under the Apache License, Version 2.0. You may +# obtain a copy of this license in the LICENSE.txt file in the root directory +# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0. +# +# Any modifications or derivative works of this code must retain this +# copyright notice, and modified files need to carry a notice indicating +# that they have been altered from the originals. +""" +Test T2Star experiment +""" +import unittest +import numpy as np + +from qiskit.utils import apply_prefix +from qiskit.providers import BaseBackend +from qiskit.providers.models import QasmBackendConfiguration +from qiskit.result import Result +from qiskit.test import QiskitTestCase +from qiskit_experiments.composite import ParallelExperiment +from qiskit_experiments.characterization import T2StarExperiment + + +class T2starBackend(BaseBackend): + """ + A simple and primitive backend, to be run by the T2Star tests + """ + + def __init__( + self, p0=None, initial_prob_plus=None, readout0to1=None, readout1to0=None, dt_factor=1 + ): + """ + Initialize the T2star backend + """ + + configuration = QasmBackendConfiguration( + backend_name="t2star_simulator", + backend_version="0", + n_qubits=int(1e6), + basis_gates=["barrier", "h", "p", "delay", "measure"], + gates=[], + local=True, + simulator=True, + conditional=False, + open_pulse=False, + memory=False, + max_shots=int(1e6), + coupling_map=None, + dt=dt_factor, + ) + + self._t2star = p0["t2star"] + self._a_guess = p0["a_guess"] + self._f_guess = p0["f_guess"] + self._phi_guess = p0["phi_guess"] + self._b_guess = p0["b_guess"] + self._initial_prob_plus = initial_prob_plus + self._readout0to1 = readout0to1 + self._readout1to0 = readout1to0 + self._dt_factor = dt_factor + self._rng = np.random.default_rng(0) + super().__init__(configuration) + + # pylint: disable = arguments-differ + def run(self, qobj): + """ + Run the T2star backend + """ + shots = qobj.config.shots + result = { + "backend_name": "T2star backend", + "backend_version": "0", + "qobj_id": 0, + "job_id": 0, + "success": True, + "results": [], + } + + for circ in qobj.experiments: + nqubits = circ.config.n_qubits + counts = dict() + if self._readout0to1 is None: + ro01 = np.zeros(nqubits) + else: + ro01 = self._readout0to1 + if self._readout1to0 is None: + ro10 = np.zeros(nqubits) + else: + ro10 = self._readout1to0 + for _ in range(shots): + if self._initial_prob_plus is None: + prob_plus = np.ones(nqubits) + else: + prob_plus = self._initial_prob_plus.copy() + + clbits = np.zeros(circ.config.memory_slots, dtype=int) + for op in circ.instructions: + qubit = op.qubits[0] + + if op.name == "delay": + delay = op.params[0] + t2star = self._t2star[qubit] * self._dt_factor + freq = self._f_guess[qubit] / self._dt_factor + + prob_plus[qubit] = ( + self._a_guess[qubit] + * np.exp(-delay / t2star) + * np.cos(2 * np.pi * freq * delay + self._phi_guess[qubit]) + + self._b_guess[qubit] + ) + + if op.name == "measure": + # we measure in |+> basis which is the same as measuring |0> + meas_res = self._rng.binomial( + 1, + (1 - prob_plus[qubit]) * (1 - ro10[qubit]) + + prob_plus[qubit] * ro01[qubit], + ) + clbits[op.memory[0]] = meas_res + + clstr = "" + for clbit in clbits[::-1]: + clstr = clstr + str(clbit) + + if clstr in counts: + counts[clstr] += 1 + else: + counts[clstr] = 1 + result["results"].append( + { + "shots": shots, + "success": True, + "header": {"metadata": circ.header.metadata}, + "data": {"counts": counts}, + } + ) + return Result.from_dict(result) + + +class TestT2Star(QiskitTestCase): + """Test T2Star experiment""" + + def test_t2star_run_end2end(self): + """ + Run the T2 backend on all possible units + """ + # For some reason, 'ps' was not precise enough - need to check this + + for unit in ["s", "ms", "us", "ns", "dt"]: + if unit in ("s", "dt"): + dt_factor = 1 + else: + dt_factor = apply_prefix(1, unit) + estimated_t2star = 20 + estimated_freq = 0.1 + # Set up the circuits + qubit = 0 + if unit == "dt": + delays = list(range(1, 46)) + else: + delays = np.append( + (np.linspace(1.0, 15.0, num=15)).astype(float), + (np.linspace(16.0, 45.0, num=59)).astype(float), + ) + + # dummy numbers to avoid exception triggerring + instruction_durations = [ + ("measure", [0], 3, unit), + ("h", [0], 3, unit), + ("p", [0], 3, unit), + ("delay", [0], 3, unit), + ] + + exp = T2StarExperiment(qubit, delays, unit=unit) + exp.set_analysis_options( + user_p0={ + "A": 0.5, + "t2star": estimated_t2star, + "f": estimated_freq, + "phi": 0, + "B": 0.5, + } + ) + + backend = T2starBackend( + p0={ + "a_guess": [0.5], + "t2star": [estimated_t2star], + "f_guess": [estimated_freq], + "phi_guess": [0.0], + "b_guess": [0.5], + }, + initial_prob_plus=[0.0], + readout0to1=[0.02], + readout1to0=[0.02], + dt_factor=dt_factor, + ) + if unit == "dt": + dt_factor = getattr(backend._configuration, "dt") + + # run circuits + + expdata = exp.run( + backend=backend, + # plot=False, + instruction_durations=instruction_durations, + shots=2000, + ) + result = expdata.analysis_result(0) + self.assertAlmostEqual( + result["t2star_value"], + estimated_t2star * dt_factor, + delta=0.08 * result["t2star_value"], + ) + self.assertAlmostEqual( + result["frequency_value"], + estimated_freq / dt_factor, + delta=0.08 * result["frequency_value"], + ) + self.assertEqual( + result["quality"], "computer_good", "Result quality bad for unit " + str(unit) + ) + + def test_t2star_parallel(self): + """ + Test parallel experiments of T2* using a simulator. + """ + + t2star = [30, 25] + estimated_freq = [0.1, 0.12] + delays = [list(range(1, 60)), list(range(1, 50))] + + exp0 = T2StarExperiment(0, delays[0]) + exp2 = T2StarExperiment(2, delays[1]) + par_exp = ParallelExperiment([exp0, exp2]) + + p0 = { + "a_guess": [0.5, None, 0.5], + "t2star": [t2star[0], None, t2star[1]], + "f_guess": [estimated_freq[0], None, estimated_freq[1]], + "phi_guess": [0, None, 0], + "b_guess": [0.5, None, 0.5], + } + backend = T2starBackend(p0) + res = par_exp.run( + backend=backend, + # plot=False, + shots=1000, + ) + + for i in range(2): + sub_res = res.component_experiment_data(i).analysis_result(0) + self.assertAlmostEqual( + sub_res["t2star_value"], t2star[i], delta=0.08 * sub_res["t2star_value"] + ) + self.assertAlmostEqual( + sub_res["frequency_value"], + estimated_freq[i], + delta=0.08 * sub_res["frequency_value"], + ) + self.assertEqual( + sub_res["quality"], + "computer_good", + "Result quality bad for experiment on qubit " + str(i), + ) + + +if __name__ == "__main__": + unittest.main()