{ "cells": [ { "cell_type": "markdown", "source": [ "# Pulsed Sources" ], "metadata": { "collapsed": false } }, { "cell_type": "markdown", "id": "b92a6efe", "metadata": { "ExecuteTime": { "end_time": "2023-10-02T13:33:00.984779Z", "start_time": "2023-10-02T13:33:00.976948Z" } }, "source": [ "ZPGenerator is a source-focused simulator that is primarily designed to study realistic states of light produced by a physical source model. In [Photonic Circuits](photonic_circuits.ipynb) tutorial, we explored the Processor class and how to simulate photonic circuits using a few catalogue source types. However, one can imagine that, once a source is no longer perfect, it can be \"not perfect\" in _many_ different ways. Subtle differences in these photonic imperfections can have quite a big impact on how errors accumulate and impact a quantum information processing task. In addition, often a physical implementation of a source cannot be ideal even in principle. Thus, a key goal is to optimise the source parameters to accomplish a particular task most effectively. For these reasons, the second main functionality provided by ZPGenerator is the characterisation of pulsed sources of quantum light." ], "outputs": [], "execution_count": 20 }, { "cell_type": "code", "execution_count": 42, "outputs": [], "source": [ "from zpgenerator import *\n", "import numpy as np\n", "import matplotlib.pyplot as plt" ], "metadata": { "collapsed": false, "ExecuteTime": { "end_time": "2024-02-09T08:46:03.190768Z", "start_time": "2024-02-09T08:46:02.936530Z" } } }, { "cell_type": "markdown", "id": "a89b9410", "metadata": {}, "source": [ "## Fock states" ] }, { "cell_type": "markdown", "id": "25b210f2", "metadata": { "ExecuteTime": { "end_time": "2023-10-02T13:35:23.474103Z", "start_time": "2023-10-02T13:35:23.337426Z" } }, "source": [ "A SourceComponent object, such as those created using the Source class, have built-in methods that can be useful to characterise source behaviour. Let's first dig deeper into the Fock state source used in the previous section. The first thing we can check are the photon number probabilities of the source using the photon_statistics() method. This method produces a PhotonNumberDistribution object." ], "outputs": [ { "ename": "TypeError", "evalue": "expected a sequence of integers or a single integer, got '2.0'", "output_type": "error", "traceback": [ "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m", "\u001B[0;31mTypeError\u001B[0m Traceback (most recent call last)", "Cell \u001B[0;32mIn[22], line 2\u001B[0m\n\u001B[1;32m 1\u001B[0m source \u001B[38;5;241m=\u001B[39m Source\u001B[38;5;241m.\u001B[39mfock(\u001B[38;5;241m1\u001B[39m)\n\u001B[0;32m----> 2\u001B[0m \u001B[43msource\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mphoton_statistics\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 3\u001B[0m display_results(source)\n", "File \u001B[0;32m~/PycharmProjects/zpg_phonons/zero-photon-generator/zpg/simulate/source_base.py:63\u001B[0m, in \u001B[0;36mSourceBase.photon_statistics\u001B[0;34m(self, port, truncation, parameters)\u001B[0m\n\u001B[1;32m 61\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(port, \u001B[38;5;28mlist\u001B[39m): \u001B[38;5;66;03m# we call recursively for each port\u001B[39;00m\n\u001B[1;32m 62\u001B[0m truncation \u001B[38;5;241m=\u001B[39m [truncation] \u001B[38;5;241m*\u001B[39m \u001B[38;5;28mlen\u001B[39m(port) \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(truncation, \u001B[38;5;28mlist\u001B[39m) \u001B[38;5;28;01melse\u001B[39;00m truncation\n\u001B[0;32m---> 63\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m [\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mphoton_statistics(port\u001B[38;5;241m=\u001B[39mport[i], truncation\u001B[38;5;241m=\u001B[39mtruncation[i], parameters\u001B[38;5;241m=\u001B[39mparameters)\n\u001B[1;32m 64\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m i \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mrange\u001B[39m(\u001B[38;5;241m0\u001B[39m, \u001B[38;5;28mlen\u001B[39m(port))]\n\u001B[1;32m 65\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[1;32m 66\u001B[0m parameters \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mset_parameters(parameters)\n", "File \u001B[0;32m~/PycharmProjects/zpg_phonons/zero-photon-generator/zpg/simulate/source_base.py:63\u001B[0m, in \u001B[0;36m\u001B[0;34m(.0)\u001B[0m\n\u001B[1;32m 61\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(port, \u001B[38;5;28mlist\u001B[39m): \u001B[38;5;66;03m# we call recursively for each port\u001B[39;00m\n\u001B[1;32m 62\u001B[0m truncation \u001B[38;5;241m=\u001B[39m [truncation] \u001B[38;5;241m*\u001B[39m \u001B[38;5;28mlen\u001B[39m(port) \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(truncation, \u001B[38;5;28mlist\u001B[39m) \u001B[38;5;28;01melse\u001B[39;00m truncation\n\u001B[0;32m---> 63\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m [\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mphoton_statistics\u001B[49m\u001B[43m(\u001B[49m\u001B[43mport\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mport\u001B[49m\u001B[43m[\u001B[49m\u001B[43mi\u001B[49m\u001B[43m]\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mtruncation\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mtruncation\u001B[49m\u001B[43m[\u001B[49m\u001B[43mi\u001B[49m\u001B[43m]\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mparameters\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mparameters\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 64\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m i \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mrange\u001B[39m(\u001B[38;5;241m0\u001B[39m, \u001B[38;5;28mlen\u001B[39m(port))]\n\u001B[1;32m 65\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[1;32m 66\u001B[0m parameters \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mset_parameters(parameters)\n", "File \u001B[0;32m~/PycharmProjects/zpg_phonons/zero-photon-generator/zpg/simulate/source_base.py:71\u001B[0m, in \u001B[0;36mSourceBase.photon_statistics\u001B[0;34m(self, port, truncation, parameters)\u001B[0m\n\u001B[1;32m 69\u001B[0m truncation \u001B[38;5;241m=\u001B[39m \u001B[38;5;241m2\u001B[39m \u001B[38;5;241m*\u001B[39m (\u001B[38;5;241m1\u001B[39m \u001B[38;5;241m+\u001B[39m \u001B[38;5;28mmax\u001B[39m(\u001B[38;5;241m1\u001B[39m, \u001B[38;5;28mround\u001B[39m(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mmu(port, parameters)))) \u001B[38;5;28;01mif\u001B[39;00m truncation \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m \u001B[38;5;28;01melse\u001B[39;00m truncation\n\u001B[1;32m 70\u001B[0m truncation \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mmax\u001B[39m(truncation, \u001B[38;5;241m2\u001B[39m)\n\u001B[0;32m---> 71\u001B[0m pn \u001B[38;5;241m=\u001B[39m \u001B[43mcompute_photon_number_distribution\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mport\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mtruncation\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mparameters\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 72\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m pn[truncation] \u001B[38;5;241m>\u001B[39m \u001B[38;5;241m10\u001B[39m \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39m (\u001B[38;5;241m-\u001B[39m\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mprecision \u001B[38;5;241m+\u001B[39m \u001B[38;5;241m2\u001B[39m):\n\u001B[1;32m 73\u001B[0m \u001B[38;5;28mprint\u001B[39m(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mWarning: truncation for mode \u001B[39m\u001B[38;5;124m\"\u001B[39m \u001B[38;5;241m+\u001B[39m (\u001B[38;5;124m'\u001B[39m\u001B[38;5;124m'\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mmodes \u001B[38;5;241m==\u001B[39m \u001B[38;5;241m1\u001B[39m \u001B[38;5;28;01melse\u001B[39;00m \u001B[38;5;28mstr\u001B[39m(port)) \u001B[38;5;241m+\u001B[39m\n\u001B[1;32m 74\u001B[0m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m may be too low to achieve requested precision.\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n", "File \u001B[0;32m~/PycharmProjects/zpg_phonons/zero-photon-generator/zpg/simulate/algorithms/distributions.py:107\u001B[0m, in \u001B[0;36mcompute_photon_number_distribution\u001B[0;34m(source, truncation, port, parameters)\u001B[0m\n\u001B[1;32m 105\u001B[0m \u001B[38;5;66;03m# Get the photon number detection probabilities\u001B[39;00m\n\u001B[1;32m 106\u001B[0m pnd \u001B[38;5;241m=\u001B[39m PhotonNumberDistribution(precision\u001B[38;5;241m=\u001B[39m\u001B[38;5;28mround\u001B[39m(log10(source\u001B[38;5;241m.\u001B[39moptions\u001B[38;5;241m.\u001B[39matol)))\n\u001B[0;32m--> 107\u001B[0m pnd\u001B[38;5;241m.\u001B[39mupdate(\u001B[43mp\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mprobs\u001B[49m\u001B[43m(\u001B[49m\u001B[43mparameters\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mparameters\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mchop\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43;01mFalse\u001B[39;49;00m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43moptions\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43msource\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43moptions\u001B[49m\u001B[43m)\u001B[49m)\n\u001B[1;32m 109\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m pnd\n", "File \u001B[0;32m~/PycharmProjects/zpg_phonons/zero-photon-generator/zpg/simulate/processor.py:688\u001B[0m, in \u001B[0;36mProcessor.probs\u001B[0;34m(self, parameters, chop, initial_time, options)\u001B[0m\n\u001B[1;32m 685\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mprobs\u001B[39m(\u001B[38;5;28mself\u001B[39m, parameters: \u001B[38;5;28mdict\u001B[39m \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mNone\u001B[39;00m, chop: \u001B[38;5;28mbool\u001B[39m \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mFalse\u001B[39;00m,\n\u001B[1;32m 686\u001B[0m initial_time \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mNone\u001B[39;00m, options: qt\u001B[38;5;241m.\u001B[39mOptions \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mNone\u001B[39;00m):\n\u001B[1;32m 687\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m(parameters \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m \u001B[38;5;129;01mand\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_probs):\n\u001B[0;32m--> 688\u001B[0m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43msimulate\u001B[49m\u001B[43m(\u001B[49m\u001B[43mparameters\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mparameters\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mrank\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;241;43m0\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mchop\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mchop\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 689\u001B[0m \u001B[43m \u001B[49m\u001B[43moptions\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43moptions\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43minitial_time\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43minitial_time\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 690\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_probs\n", "File \u001B[0;32m~/PycharmProjects/zpg_phonons/zero-photon-generator/zpg/simulate/processor.py:564\u001B[0m, in \u001B[0;36mProcessor.simulate\u001B[0;34m(self, parameters, rank, dims, sel, basis, initial_time, chop, options, update)\u001B[0m\n\u001B[1;32m 560\u001B[0m basis \u001B[38;5;241m=\u001B[39m [psi1 \u001B[38;5;241m*\u001B[39m psi2\u001B[38;5;241m.\u001B[39mdag() \u001B[38;5;28;01mfor\u001B[39;00m psi2 \u001B[38;5;129;01min\u001B[39;00m basis \u001B[38;5;28;01mfor\u001B[39;00m psi1 \u001B[38;5;129;01min\u001B[39;00m basis]\n\u001B[1;32m 561\u001B[0m \u001B[38;5;66;03m# add warning about non-orthonormal bases here?\u001B[39;00m\n\u001B[1;32m 562\u001B[0m \n\u001B[1;32m 563\u001B[0m \u001B[38;5;66;03m# simulate the virtual tree\u001B[39;00m\n\u001B[0;32m--> 564\u001B[0m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_simulate_vtree\u001B[49m\u001B[43m(\u001B[49m\u001B[43mparameters\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43minitial_time\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43minitial_time\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mbasis\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mbasis\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43moptions\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43moptions\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mupdate\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mupdate\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 566\u001B[0m tensors \u001B[38;5;241m=\u001B[39m [tree\u001B[38;5;241m.\u001B[39mbuild_state_tensor() \u001B[38;5;28;01mfor\u001B[39;00m tree \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_current_basistree] \u001B[38;5;28;01mif\u001B[39;00m rank \u001B[38;5;241m==\u001B[39m \u001B[38;5;241m2\u001B[39m \\\n\u001B[1;32m 567\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m [\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_current_tree\u001B[38;5;241m.\u001B[39mbuild_state_tensor()] \u001B[38;5;28;01mif\u001B[39;00m rank \u001B[38;5;241m==\u001B[39m \u001B[38;5;241m1\u001B[39m \\\n\u001B[1;32m 568\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m [\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_current_tree\u001B[38;5;241m.\u001B[39mbuild_probability_tensor()]\n\u001B[1;32m 570\u001B[0m \u001B[38;5;66;03m# determine which axes of the tensor correspond to PNR (1) or Threshold (0) detectors\u001B[39;00m\n", "File \u001B[0;32m~/PycharmProjects/zpg_phonons/zero-photon-generator/zpg/simulate/processor.py:504\u001B[0m, in \u001B[0;36mProcessor._simulate_vtree\u001B[0;34m(self, parameters, basis, options, initial_time, update)\u001B[0m\n\u001B[1;32m 500\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m op \u001B[38;5;129;01min\u001B[39;00m source\u001B[38;5;241m.\u001B[39msystem\u001B[38;5;241m.\u001B[39mE\u001B[38;5;241m.\u001B[39mevaluate_dirac(t0, system_parameters):\n\u001B[1;32m 501\u001B[0m vtree\u001B[38;5;241m.\u001B[39mapply_generator(kron_inject(\n\u001B[1;32m 502\u001B[0m op \u001B[38;5;28;01mif\u001B[39;00m op\u001B[38;5;241m.\u001B[39missuper \u001B[38;5;28;01melse\u001B[39;00m qt\u001B[38;5;241m.\u001B[39mliouvillian(\u001B[38;5;241m0\u001B[39m \u001B[38;5;241m*\u001B[39m op, c_ops\u001B[38;5;241m=\u001B[39m[op]), j, \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39msubdims))\n\u001B[0;32m--> 504\u001B[0m prop \u001B[38;5;241m=\u001B[39m \u001B[43mfactory\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mbuild_propagator\u001B[49m\u001B[43m(\u001B[49m\u001B[43mt0\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43margs\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mparameters\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43moptions\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43moptions\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 505\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m vtree \u001B[38;5;129;01min\u001B[39;00m vtrees:\n\u001B[1;32m 506\u001B[0m vtree\u001B[38;5;241m.\u001B[39mpropagate(t1, prop, parameters) \u001B[38;5;66;03m# propagate to next stop time\u001B[39;00m\n", "File \u001B[0;32m~/PycharmProjects/zpg_phonons/zero-photon-generator/zpg/virtual/propagator.py:199\u001B[0m, in \u001B[0;36mPropagatorFactory.build_propagator\u001B[0;34m(self, t, args, options)\u001B[0m\n\u001B[1;32m 197\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mbuild_propagator\u001B[39m(\u001B[38;5;28mself\u001B[39m, t: \u001B[38;5;28mfloat\u001B[39m, args: \u001B[38;5;28mdict\u001B[39m \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mNone\u001B[39;00m, options: qt\u001B[38;5;241m.\u001B[39mOptions \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mNone\u001B[39;00m):\n\u001B[1;32m 198\u001B[0m S \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mcircuit\u001B[38;5;241m.\u001B[39mto_qutip(t, args)\n\u001B[0;32m--> 199\u001B[0m L, H, E \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mquadruple\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mto_qutip\u001B[49m\u001B[43m(\u001B[49m\u001B[43mt\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43margs\u001B[49m\u001B[43m)\u001B[49m[\u001B[38;5;241m1\u001B[39m::]\n\u001B[1;32m 200\u001B[0m eta \u001B[38;5;241m=\u001B[39m [coupling(t, args) \u001B[38;5;28;01mfor\u001B[39;00m coupling \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mdetector_couplings]\n\u001B[1;32m 202\u001B[0m is_Sdynamic \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mcircuit\u001B[38;5;241m.\u001B[39mis_time_dependent(t, args)\n", "File \u001B[0;32m~/PycharmProjects/zpg_phonons/zero-photon-generator/zpg/simulate/processor.py:408\u001B[0m, in \u001B[0;36mProcessor.to_qutip\u001B[0;34m(self, t, parameters)\u001B[0m\n\u001B[1;32m 405\u001B[0m newL \u001B[38;5;241m+\u001B[39m\u001B[38;5;241m=\u001B[39m newLi \u001B[38;5;66;03m# join the new source L vector to the total new L vector\u001B[39;00m\n\u001B[1;32m 407\u001B[0m \u001B[38;5;66;03m# For the HamiltonianBase, it's a bit simpler because we only have one operator to tensor and add together\u001B[39;00m\n\u001B[0;32m--> 408\u001B[0m newH \u001B[38;5;241m=\u001B[39m \u001B[38;5;28msum\u001B[39m((q[\u001B[38;5;241m2\u001B[39m]\u001B[38;5;241m.\u001B[39mkron(i, subdims) \u001B[38;5;28;01mfor\u001B[39;00m i, q \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28menumerate\u001B[39m(quadruple_list)), \u001B[43mqt\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mqzero\u001B[49m\u001B[43m(\u001B[49m\u001B[43msubdims\u001B[49m\u001B[43m)\u001B[49m)\n\u001B[1;32m 410\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m [newS, newL, newH, newE]\n", "File \u001B[0;32m~/PycharmProjects/zpg_phonons/venv/lib/python3.9/site-packages/qutip/operators.py:443\u001B[0m, in \u001B[0;36mqzero\u001B[0;34m(dimensions)\u001B[0m\n\u001B[1;32m 441\u001B[0m size, dimensions \u001B[38;5;241m=\u001B[39m _implicit_tensor_dimensions(dimensions)\n\u001B[1;32m 442\u001B[0m \u001B[38;5;66;03m# A sparse matrix with no data is equal to a zero matrix.\u001B[39;00m\n\u001B[0;32m--> 443\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m Qobj(\u001B[43mfast_csr_matrix\u001B[49m\u001B[43m(\u001B[49m\u001B[43mshape\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43msize\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43msize\u001B[49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mdtype\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43mcomplex\u001B[39;49m\u001B[43m)\u001B[49m,\n\u001B[1;32m 444\u001B[0m dims\u001B[38;5;241m=\u001B[39mdimensions, isherm\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mTrue\u001B[39;00m)\n", "File \u001B[0;32m~/PycharmProjects/zpg_phonons/venv/lib/python3.9/site-packages/qutip/fastsparse.py:36\u001B[0m, in \u001B[0;36mfast_csr_matrix.__init__\u001B[0;34m(self, args, shape, dtype, copy)\u001B[0m\n\u001B[1;32m 34\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mdata \u001B[38;5;241m=\u001B[39m np\u001B[38;5;241m.\u001B[39marray([], dtype\u001B[38;5;241m=\u001B[39m\u001B[38;5;28mcomplex\u001B[39m)\n\u001B[1;32m 35\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mindices \u001B[38;5;241m=\u001B[39m np\u001B[38;5;241m.\u001B[39marray([], dtype\u001B[38;5;241m=\u001B[39mnp\u001B[38;5;241m.\u001B[39mint32)\n\u001B[0;32m---> 36\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mindptr \u001B[38;5;241m=\u001B[39m 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{ "ExecuteTime": { "end_time": "2024-02-09T08:46:03.262736Z", "start_time": "2024-02-09T08:46:02.981443Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number | Probability\n", "0 | 0.00000\n", "1 | 1.00000\n", "2 | 0.00000\n", "3 | 0.00000\n", "4 | 0.00000\n", "\n" ] } ], "source": [ "source = Source.fock(1)\n", "pn = source.photon_statistics()\n", "pn.display()" ] }, { "cell_type": "markdown", "source": [ "With this distribution, we now have access to methds that compute other characteristics, such as the average photon number $\\mu$, the probability of emitting at least one photon $\\beta$, and the integrated intensity correlation $g^{(2)}$. This can be done using the mu(), beta(), and g2() methods." ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 44, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'mu': 1.0, 'beta': 1.0, 'g2': 0.0}\n" ] } ], "source": [ "print({'mu': pn.mu(), 'beta': pn.beta(), 'g2': pn.g2()})" ], "metadata": { "collapsed": false, "ExecuteTime": { "end_time": "2024-02-09T08:46:03.262920Z", "start_time": "2024-02-09T08:46:02.991961Z" } } }, { "cell_type": "markdown", "source": [ "Or, we can use display_figures() to automatically compute them and display them." ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 45, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Figure of Merit | Value\n", "Brightness | 1.0000\n", "Average photon number | 1.0000\n", "Intensity correlation | 0.0000\n", "\n" ] } ], "source": [ "pn.display_figures()" ], "metadata": { "collapsed": false, "ExecuteTime": { "end_time": "2024-02-09T08:46:03.264003Z", "start_time": "2024-02-09T08:46:02.994287Z" } } }, { "cell_type": "markdown", "source": [ "As expected, our source of perfect $|1\\rangle$ states produces an average photon number $\\mu$ of $1$, a probability of emitting at least one photon $\\beta$ of $1$, and an integrated intensity correlation $g^{(2)}$ of $0$. This is due to the fact that the photon number distribution is $p_1=1$.\n", "\n", "We can also evaluate basic figures of merit that cannot be obtained from the photon number distribution by performing Hong-Ou-Mandel interference of the wavepacket with itself using the hom() method. We _could_ manually build the interferometer as in the previous section, but since this is such a common circuit to simulate, it is integrated into the Processor class." ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 46, "outputs": [ { "data": { "text/plain": "{'c1': 0.0, 'c2': 0.0, 'vhom': 1.0000006344860717, 'M': 1.000002776951858}" }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "source.hom()" ], "metadata": { "collapsed": false, "ExecuteTime": { "end_time": "2024-02-09T08:46:03.272479Z", "start_time": "2024-02-09T08:46:03.000662Z" } } }, { "cell_type": "markdown", "source": [ "Like for the other figures of merit, we can compute and display them using the display_hom() method." ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 47, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Figure of Merit | Value\n", "First order number coherence | 0.0000\n", "Second order number coherence | 0.0000\n", "Hong-Ou-Mandel visibility | 1.0000\n", "Mean wavepacket overlap | 1.0000\n", "\n" ] } ], "source": [ "source.display_hom()" ], "metadata": { "collapsed": false, "ExecuteTime": { "end_time": "2024-02-09T08:46:03.276315Z", "start_time": "2024-02-09T08:46:03.070597Z" } } }, { "cell_type": "markdown", "id": "7e441d3a", "metadata": {}, "source": [ "Applying the vhom() method computes additional important source figures of merit: the Hong-Ou-Mandel visibility $V_\\text{HOM}$, the mean wavepacket overlap $M$, the first-order number coherence $c^{(1)}$, and the second-order number coherence $c^{(2)}$. As expected, we find $V_\\text{HOM}=M=1$ and $c^{(1)}=c^{(2)}=0$, characteristic of a single-photon Fock state. \n", "\n", "Another important characteristic of a pulsed source of light is its temporal profile: how the wavepacket intensity evolves in time. In the context of deterministic sources of quantum light, this is often referred to as the \"lifetime\" of the wavepacket because it reflects the decay dynamics of the source excited state population quantified by the lifetime timescale $T_1$. In fact, all parameters in ZPG are, by default, in units of this decay timescale $T_1$. The Source class provides the lifetime() method to simulate the temporal profile." ] }, { "cell_type": "code", "execution_count": 48, "id": "21cbcebd", "metadata": { "ExecuteTime": { "end_time": "2024-02-09T08:46:03.289158Z", "start_time": "2024-02-09T08:46:03.153012Z" } }, "outputs": [ { "data": { "text/plain": "
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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# we can use the 'start' and 'end' keywords to specify the window of time to look at (in units of T1)\n", "lifetime = source.lifetime(start=-1, end=6)\n", "plt.plot(lifetime.times, lifetime.population)\n", "plt.xlabel('Time, t ($T_1$)')\n", "plt.ylabel('Intensity')\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "2fe0869a", "metadata": {}, "source": [ "From the above plot, we can see that the Fock state source actually produces photons with an exponentially decaying temporal shape. This is because it is the simplest shape to simulate using the ZPG backend, which is also very close to the profile of the emission from a quickly-excited atom or quantum dot. This differs substantially from how Perceval models a Fock state $|1\\rangle$ source as an abstract complex vector. In ZPGenerator, the single photon is a physical object evolving in time with quantum properties dictated by its source. This allows the framework to capture various subtle characteristics of light produced by many different physical source models." ] }, { "cell_type": "markdown", "id": "aa4dece2", "metadata": {}, "source": [ "## Physical models" ] }, { "cell_type": "markdown", "id": "0da5d428", "metadata": {}, "source": [ "In reality, the excitation of the source can never be perfectly instantaneous. This limitation is the primary reason for incidental multi-photon emission when attempting to generate single photons. To dig deeper into the physics, we can take a look at a more realistic pre-built source model: the two-level() source. This source is a two-level system driven by a dirac laser pulse by default so that it behaves like a perfect single-photon source. Using the Pulse() class, we can set a new pulse shape for our two-level source. Let's use a square pulse with a width of $1/2$ the source lifetime, and then take a look at the lifetime, and plot the control pulse alongside for comparison." ] }, { "cell_type": "code", "execution_count": 49, "id": "19bf7ce7", "metadata": { "ExecuteTime": { "end_time": "2024-02-09T08:46:03.389435Z", "start_time": "2024-02-09T08:46:03.231976Z" } }, "outputs": [ { "data": { "text/plain": "
", "image/png": 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JJ59EXl4egoODMXr0aNxyyy32+pGsQtC72bkFarUafn5+qKyshK+vr9jluJ3cslqMe3UHFDIJTj4/BRLJ1ZdDFqnrMXJ5GiQCcOTZyfBSMo8TkeXq6+uRnZ2Nnj17QqVqe38tEt/V/p0s+fzmtBTZlbmZ2N+jw2ADAKG+KoT7qqDTA0fzOHpDREQdY7ghu8qvqAcARPp33G9jMjTGMDXF/W6IiKgzGG7IrgqrDOEm1Lfzu2oONW/mx5EbIiLqGMMN2VVhpSHctHemVFu4YoqIiCzBcEN2VajWAADCLAg3g40rpi6W16G0WmOTuoiIyHUw3JBdmaalLAk3vio54kMMm21xSTgRdYebLRB2Otb692G4IbsyTUuFWdBzAwCJpqkpNhUTURfI5XIAQG2tSCeBU6c0NDQAwBXHP1iKm4aQ3eh0ehRVGaaVwv0s22diSLQfvjqQx74bIuoSqVQKf39/FBUVAQA8PT0hCB1vR0H2o9PpUFxcDE9PT/PZWV3FcEN2U1rTgCadHoIABHtbNnJjWjF1MLcCer2e/6dERBYLDw8HAHPAIccjkUjQo0ePbv9/PMMN2U2h2jAlFeythFxq2YzogEhfKKQSlNc24kJpLeKCnefAOyJyDIIgICIiAqGhoVc9OZvEo1AoIJF0v2OG4YbsxhRuLO23AQClTIpBUb7IzKlAZk45ww0RdZlUKu12Twc5NjYUk92YloFbssdNS0k9AgAAmTnlVquJiIhcD8MN2U2B2rQ7cdfCzTBTuLlQYa2SiIjIBTHckN0UmaalfLoYbmL9AQAnC9SobWiyVllERORiGG7IbkwjN+F+lvfcAECEnwci/AwnhB/K5WZ+RETUNoYbspti4x43IT5dCzdAi6kp9t0QEVE7GG7IbkqrDTtPWrrHTUtJPfwBAAcYboiIqB0MN2QXer0epTWGkZvuhRvTyE0Fz4ghIqI2MdyQXajrmtCoNYSRIG9Fl99nUJRhM7+ymgbklPGMGCIiuhLDDdlFcbVh1MZHJYNS1vXNs5QyKQZG+QJg3w0REbWN4YbsosQYbkK6MSVlkhTD/W6IiKh9DDdkF6Zm4u5MSZmY9rvhyA0REbWF4YbswjRy051mYhPTcvCTBVXczI+IiK7AcEN2UWrFcBPp74FwXxW0Oj0OX+RmfkRE1BrDDdlFsRWnpQBOTRERUfsYbsgurDlyA7Q8RJPhhoiIWmO4Ibto7rmxzsjNiLhAAMDvF8qh03EzPyIiasZwQ3ZRYoWjF1oaGOkLlVyCitpGnC2utsp7EhGRa2C4Ibuw9rSUXCox73ez/zynpoiIqBnDDdlcXYMWNQ1aANZrKAaAP8QZws3v58us9p5EROT8GG7I5kz9NkqZBN5KmdXe19R3s/8Cww0RETVjuCGba7mBnyAIVnvfpB7+kAhAblkdCirrrfa+RETk3BhuyObKaw3NxIFe1puSAgAflRz9IwyHaO7n1BQRERkx3JDNldU0AgACrBxuAOAPpiXhDDdERGTEcEM2V15jHLnxlFv9vU3hhiumiIjIhOGGbM40LWWLkZsRcaZDNNVQ1zda/f2JiMj5MNyQzZl7bjytH27CfFXoEegJnR44kFNh9fcnIiLnw3BDNldWY7uRG6B59GZ/NvtuiIiI4YbsoNzYUGzt1VImzX03DDdERMRwQ3ZQZuq5scG0FNC8U/HB3Ao0NOlscg0iInIeDDdkc+XmaSnrr5YCgF4h3gjwlEPTpMPRS5U2uQYRETkPhhuyKZ1Ob9OGYgAQBMF8FMNv5zg1RUTk7hhuyKbU9Y3Q6Q1f+9so3ADAqJ7GcJNdarNrEBGRc2C4IZsyrZTyUcqgkNnuP7fR8UEAgN/Pl6NJy74bIiJ3xnBDNmXLDfxa6h/hC1+VDNWaJhy7pLbptYiIyLEx3JBN2fJcqZakEgEjexpGb349x6kpIiJ3xnBDNmXLc6UuNzre0HfDcENE5N4Ybsimyuw0LQWw74aIiAwYbsimmkdubB9u+kf4wkclQ5WmCcfz2XdDROSuGG7Ipmx9rlRLUolgXhLOqSkiIvfFcEM2VV5rbCi2w8gNAIwyNxVzMz8iInfFcEM2VVlnGLnxt0NDMdDcd7M/uwxa0+6BRETkVhhuyKYqjCM3/h72CTcDIn3hozT23XC/GyIit8RwQzZVUWcIN352Grkx7HfDvhsiInfGcEM2o9frUWkaubFTzw3QPDXFcENE5J4Ybshm6ht1aDDuN2OvaSkAGGXczG8f+26IiNwSww3ZTIWxmVgmEeCpkNrtugMimvtujl2qtNt1iYjIMTDckM2Ym4k95RAEwW7XlUklGGWcmvr5DKemiIjcDcMN2Ywp3PjZcUrK5JrepnBTYvdrExGRuBhuyGaa97ixXzOxydjewQCA/efLUN+otfv1iYhIPAw3ZDOVdfbd46al3qHeCPVRQtOkQ+aFcrtfn4iIxCN6uFmxYgXi4uKgUqkwatQo7Nu376rPr6iowLx58xAREQGlUok+ffrg+++/t1O1ZAkxp6UEQcA1xtGbPZyaIiJyK6KGmw0bNmDhwoVYtmwZMjMzMXToUEyePBlFRUVtPr+hoQE33HADzp8/j40bNyIrKwurV69GVFSUnSunzrD3Bn6XG2MMNz+fZVMxEZE7kYl58TfffBNz5szB7NmzAQArV67Eli1bsGbNGjz11FNXPH/NmjUoKyvDL7/8Arnc8IEZFxdnz5LJAs1HL9i/5wYAxhqbio9crEBlXaMoI0hERGR/oo3cNDQ0ICMjAykpKc3FSCRISUnB3r1723zNN998g+TkZMybNw9hYWEYNGgQli9fDq2WDaOOyN6HZl4uws8D8SFe0Om5WzERkTsRLdyUlJRAq9UiLCys1f1hYWEoKCho8zXnzp3Dxo0bodVq8f333+OZZ57BG2+8gRdffLHd62g0GqjV6lY3sg9zQ7FI4QaAue+GS8KJiNyH6A3FltDpdAgNDcWqVaswfPhwTJ8+Hf/85z+xcuXKdl+TmpoKPz8/8y0mJsaOFbs307SUr4jTQWMZboiI3I5o4SY4OBhSqRSFhYWt7i8sLER4eHibr4mIiECfPn0glTZv5d+/f38UFBSgoaGhzdcsWbIElZWV5ltubq71fgi6quaeG/HCzej4IEgE4GxxDQoq60Wrg4iI7Ee0cKNQKDB8+HCkpaWZ79PpdEhLS0NycnKbrxk7dizOnDkDnU5nvu/UqVOIiIiAQtF206pSqYSvr2+rG9lH87SUOA3FgGEZ+uBofwAcvSEicheiTkstXLgQq1evxrp163DixAnMnTsXNTU15tVT999/P5YsWWJ+/ty5c1FWVobHH38cp06dwpYtW7B8+XLMmzdPrB+B2tGo1aFa0wRA3JEbgEcxEBG5G1GXgk+fPh3FxcVYunQpCgoKkJiYiK1bt5qbjHNyciCRNOevmJgYbNu2DQsWLMCQIUMQFRWFxx9/HIsXLxbrR6B2qI2jNoC4PTcAMLZXMFbsOIvdZ0qg1+vteognERHZn6DX6/ViF2FParUafn5+qKys5BSVDZ0trsb1b+yEj0qGI89OFrUWTZMWic9tR12jFj88Pg79I/jvTkTkbCz5/Haq1VLkPMzNxCIuAzdRyqRI7mWYmtp5qljkaoiIyNYYbsgmzBv4ibQ78eXG9wkBAOxiuCEicnkMN2QTjjRyAwDXGsPN/vNlqDE2OhMRkWtiuCGbMC0Dd5TznOKCPNEj0BONWj328iBNIiKXxnBDNuFoIzeCIODaPobdined5tQUEZErY7ghm3C0kRsAGN8nFACbiomIXB3DDdlERa1jNRQDQHKvIMgkAi6U1uJ8SY3Y5RARkY0w3JBNVJhGbhxkWgoAvJUyjIgLAMCpKSIiV8ZwQzZhPlfKgaalgOZVUzuzGG6IiFwVww3ZRGWt4/XcAM373fxythSaJq3I1RARkS0w3JBNVDrgtBQADIjwRYiPEnWNWmScLxe7HCIisgGGG7I6vV4Pdb0h3PiqHCvcCIKAcQmGJeHpXDVFROSSGG7I6uobdWjUGs5jFftE8LZM6GtYEr7jZJHIlRARkS0w3JDVmUZtJALgpZCKXM2VxieEQCoRcLqoGjmltWKXQ0REVsZwQ1ZXZZqS8pBDEASRq7mSn6ccI2INS8L/d7JQ5GqIiMjaGG7I6irrDAdTOlq/TUvX9zdMTaVxaoqIyOUw3JDVmZuJPWQiV9K+if3CAAC/neMp4URErobhhqxOXeeYK6Va6hXihdggTzRoddhzpkTscoiIyIoYbsjq1PWOPy0lCAIm9jNMTf3vBKemiIhcCcMNWZ155MaBp6UA4Hrj1NT/soqg0+lFroaIiKzF4k+f7Oxs7N69GxcuXEBtbS1CQkKQlJSE5ORkqFQqW9RITsZRN/C73MiegfBSSFFcpcHRS5UYEu0vdklERGQFnQ43n332Gd555x38/vvvCAsLQ2RkJDw8PFBWVoazZ89CpVLh3nvvxeLFixEbG2vLmsnBqU2rpRxwA7+WFDIJxiWEYOuxAqSdKGK4ISJyEZ2alkpKSsK//vUvzJo1CxcuXEB+fj4yMjKwZ88eHD9+HGq1Gl9//TV0Oh1GjBiBL774wtZ1kwNrHrlx7GkpAJhoXBL+Py4JJyJyGZ369Hn55ZcxefLkdh9XKpWYMGECJkyYgJdeegnnz5+3Vn3khJp7bhx75AYArjMexXAkrxKF6nqE+XJqlYjI2XVq5OZqweZyQUFBGD58eJcLIudnWi3l4+A9NwAQ4qPE0Bh/AEAaV00REbmELs0baLVabNq0CSdOnAAA9O/fH9OmTYNM5vjTEGR7VXXOMy0FAJMGhOFQbgW2Hy/APaN6iF0OERF1k8VLwY8dO4Y+ffpg5syZ2LRpEzZt2oRZs2YhISEBR48etUWN5GTU9c4zLQUYwg0A/Hym1HwuFhEROS+Lw81DDz2EgQMH4uLFi8jMzERmZiZyc3MxZMgQ/OUvf7FFjeRkzJv4OUm46R3qjfhgLzRoddh5qljscoiIqJssDjcHDx5EamoqAgICzPcFBATgpZdewoEDB6xaHDmf+kYtGpp0AJxnWkoQBNww0DB6s+0YTwknInJ2FoebPn36oLDwyg+AoqIi9O7d2ypFkfMyTUlJBMBL4RzhBgAmDwwHAOw4WQRNk1bkaoiIqDs6FW7UarX5lpqaisceewwbN27ExYsXcfHiRWzcuBFPPPEEXnnlFVvXSw7OtIGfj0oOiUQQuZrOS4z2R6iPEtWaJuw9Wyp2OURE1A2d+tXa398fgtD8QaXX63HXXXeZ79PrDefyTJ06FVotf+t1Z83NxM4zagMAEomAGwaE4bPfcrDtWCEmGPe/ISIi59OpT6AdO3bYug5yEeYN/Jxgj5vLTRoYjs9+y8H244V4adogpxp5IiKiZp0KN+PHjwcANDU1Yfny5XjggQcQHR1t08LIOZlXSjlhuEmOD4KPUoaSag0O5JZjeGyg2CUREVEXWNRQLJPJ8Nprr6GpqclW9ZCTaz56wbmmpQDDQZrX9TNMR/3IVVNERE7L4tVSEydOxM6dO21RC7mA5kMznW/kBgAmmZeEF5h7yYiIyLlY/Ov1jTfeiKeeegpHjhzB8OHD4eXl1erxW2+91WrFkfMxrZZylg38LjehbygUMgnOl9Yiq7AK/cJ9xS6JiIgsZHG4eeSRRwAAb7755hWPCYLA1VJuzjRy4+MkG/hdzlspw7UJIfjpRCG+P5zPcENE5IQsnpbS6XTt3hhsyJlXS5ncPMSwod+WI/mcmiIickIWhxuiq3G2c6Xacn3/MCikEpwtrkFWYZXY5RARkYW6NHdQU1ODnTt3IicnBw0NDa0ee+yxx6xSGDmnKnNDsXNOSwGGUadr+3BqiojIWVn8CXTgwAHcdNNNqK2tRU1NDQIDA1FSUgJPT0+EhoYy3Li55qXgzjtyAximpn46UYgtR/Kx4IY+rXboJiIix2bxtNSCBQswdepUlJeXw8PDA7/++isuXLiA4cOH4/XXX7dFjeREnHkTv5ZS+odBITNMTZ0qrBa7HCIisoDF4ebgwYN48sknIZFIIJVKodFoEBMTg1dffRX/+Mc/bFEjORFn3sSvJR+VHNcmhAAAthy+JHI1RERkCYvDjVwuh0RieFloaChycnIAAH5+fsjNzbVudeRU6hu10DTpADj/tBTAVVNERM7K4l+vk5KSsH//fiQkJGD8+PFYunQpSkpK8J///AeDBg2yRY3kJKqMU1KCAHgrnHvkBrhyaqpvuI/YJRERUSdYPHKzfPlyREREAABeeuklBAQEYO7cuSguLsaqVausXiA5D/MGfkqZS5yozakpIiLnZPGv1yNGjDB/HRoaiq1bt1q1IHJerrJSqiXTqqnvuGqKiMhpcBM/shpXWSnVUkr/MChlEpwrrsGxS2qxyyEiok7oVLiZMmUKfv311w6fV1VVhVdeeQUrVqzodmHkfFxlpVRLPio5UvobTgr/+mCeyNUQEVFndOpT6M4778Sf/vQn+Pn5YerUqRgxYgQiIyOhUqlQXl6O48ePY8+ePfj+++9x880347XXXrN13eSA1PXOf65UW25LjMSWI/n45tAlPHVjf0hdoJ+IiMiVdSrcPPjgg7jvvvvwxRdfYMOGDVi1ahUqKysBGE4CHzBgACZPnoz9+/ejf//+Ni2YHJe6zjAt5eNi4WZ83xD4qmQoVGvwW3YpxvQKFrskIiK6ik7PHyiVStx333247777AACVlZWoq6tDUFAQ5HLX+jCjrjGP3LjQtBQAKGVS3DQ4Auv35+Kbg5cYboiIHFyXG4r9/PwQHh7OYENm5p4bFxu5AYDbEqMAAN8fyYemSStyNUREdDVcLUVWY9rEz5WWgpuM7BmIcF8V1PVNSM8qFrscIiK6CoYbsprmhmLXmpYCAKlEwNShhs0rvznIDf2IiBwZww1ZjStu4teSaWrqpxOFqDIGOSIicjwMN2Q1rriJX0sDI33RK8QLmiYdth0rFLscIiJqh8XhZubMmdi1a5ctaiEn54qb+LUkCIJ59OarzIsiV0NERO2xONxUVlYiJSUFCQkJWL58OfLyuGsrGbjqJn4t3Z5kCDd7z5XiYnmtyNUQEVFbLA43mzdvRl5eHubOnYsNGzYgLi4ON954IzZu3IjGRvYhuCtNkxb1jToArttzAwAxgZ5Ijg+CXg9symSwJyJyRF3quQkJCcHChQtx6NAh/Pbbb+jduzdmzJiByMhILFiwAKdPn7Z2neTgTMvABQHwUbrmtJTJHcOjAQBfZl6EXq8XuRoiIrpctxqK8/PzsX37dmzfvh1SqRQ33XQTjhw5ggEDBuCtt96yVo3kBEz9Nt5KGSQufvbSlEHh8FRIcb60FhkXysUuh4iILmNxuGlsbMSXX36JW265BbGxsfjiiy/wxBNP4NKlS1i3bh1++ukn/Pe//8Xzzz9vi3rJQbn6SqmWvJQy3DTYsOfNxgw2FhMRORqL5w8iIiKg0+lw9913Y9++fUhMTLziOddddx38/f2tUB45C9PIjY8LbuDXljuGR2NjxkV8dzgfy6YOhIdCKnZJRERkZPEn0VtvvYU777wTKpWq3ef4+/sjOzu7W4WRc2k+NNP1R24AYGRcIGICPZBbVodtxwowzbiKioiIxGfxtNSOHTvaXBVVU1ODBx54wCpFkfNR17nPtBQASCQC/jTM0FjMqSkiIsdicbhZt24d6urqrri/rq4On3zyiVWKIudTVe/aG/i1xRRufj5bgksVV/5vgoiIxNHpcKNWq1FZWQm9Xo+qqiqo1Wrzrby8HN9//z1CQ0NtWSs5MHfYwO9yMYGeGNUzEHo98MXvHL0hInIUnf4129/fH4IgQBAE9OnT54rHBUHAc889Z9XiyHmYp6XcpOfG5M8jY/Bbdhn++3su5k/sDamLL4MnInIGnR652bFjB9LS0qDX67Fx40b873//M9/27NmDnJwc/POf/+xSEStWrEBcXBxUKhVGjRqFffv2dep169evhyAImDZtWpeuS9bTPHLjPtNSAHDjoAj4eciRV1GH3aeLxS6HiIhgwcjN+PHjAQDZ2dno0aMHBME6v6Fu2LABCxcuxMqVKzFq1Ci8/fbbmDx5MrKysq46zXX+/HksWrQI48aNs0od1D3Nh2a618iNSi7F7UlR+PiX8/i/fTmY0JdTs0REYuvUyM3hw4eh0xnODaqsrMSRI0dw+PDhNm+WevPNNzFnzhzMnj0bAwYMwMqVK+Hp6Yk1a9a0+xqtVot7770Xzz33HOLj4y2+JlmfO23id7m7R/YAAKSdKEKRul7kaoiIqFMjN4mJiSgoKEBoaCgSExMhCEKbZ+oIggCtVtvpizc0NCAjIwNLliwx3yeRSJCSkoK9e/e2+7rnn38eoaGhePDBB7F79+6rXkOj0UCj0Zi/V6vVna6POq955Ma9pqUAoG+4D4bHBiDjQjm+yLiIedf1FrskIiK31qlPouzsbISEhJi/tpaSkhJotVqEhYW1uj8sLAwnT55s8zV79uzBRx99hIMHD3bqGqmpqWx0tgN3XC3V0p//EIOMC+VYvz8Hc8f3cvnztYiIHFmnwk1sbGybX9tbVVUVZsyYgdWrVyM4OLhTr1myZAkWLlxo/l6tViMmJsZWJbot02opPzfruTG5ZUgknv/uOHLL6vDL2VJck9C5/z6JiMj6urSJ35YtW8zf//3vf4e/vz/GjBmDCxcuWPRewcHBkEqlKCwsbHV/YWEhwsPDr3j+2bNncf78eUydOhUymQwymQyffPIJvvnmG8hkMpw9e/aK1yiVSvj6+ra6kXU1NOlQ12iYjnTXkRsPhRTTEg1HMPzfvhyRqyEicm8Wh5vly5fDw8MDALB371689957ePXVVxEcHIwFCxZY9F4KhQLDhw9HWlqa+T6dToe0tDQkJydf8fx+/frhyJEjOHjwoPl266234rrrrsPBgwc5IiMS0+7EAODtZkvBWzI1Fv94vADFVZoOnk1ERLZi8SdRbm4uevc2NExu3rwZd9xxB/7yl79g7NixmDBhgsUFLFy4EDNnzsSIESMwcuRIvP3226ipqcHs2bMBAPfffz+ioqKQmpoKlUqFQYMGtXq96fTxy+8n+zGtlPJWytx6E7sBkb5IjPHHwdwKbNifg/kTE8QuiYjILVk8cuPt7Y3S0lIAwI8//ogbbrgBAKBSqdo8c6oj06dPx+uvv46lS5ciMTERBw8exNatW81Nxjk5OcjPz7f4fcl+zCul3HjUxmTmGENP2qe/5qBJqxO5GiIi92Txp9ENN9yAhx56CElJSTh16hRuuukmAMCxY8cQFxfXpSLmz5+P+fPnt/lYenr6VV/78ccfd+maZD1V9e559EJbbhocgRe/O4ECdT22Hy/EjYMjxC6JiMjtWDxys2LFCiQnJ6O4uBhffvklgoKCAAAZGRm4++67rV4gOT53XwbeklImxZ9HGnq/PtlrWYM9ERFZh8UjN/7+/njvvfeuuJ97ybgvd97Ary33jorF++lnsfdcKU4VVqFPmI/YJRERuZUufRpVVFRg3759KCoqMh/LABh2KJ4xY4bViiPnwJGb1iL9PTBpQDi2HivAf/ZewAvT2OxORGRPFoebb7/9Fvfeey+qq6vh6+vb6gBNhhv3ZNrAjz03ze5PjsXWYwX4MvMi/jalL4MfEZEdWdxz8+STT+KBBx5AdXU1KioqUF5ebr6VlZXZokZycM0jN5yWMknuFYTeod6obdDiq4yLYpdDRORWLA43eXl5eOyxx+Dp6WmLesgJNffccHTCRBAEzEw2LAv/ZO8F6HRXHjRLRES2YXG4mTx5Mn7//Xdb1EJOyrSJH6deWrt9WDR8VDKcK6lB+qkiscshInIbFs8j3Hzzzfjb3/6G48ePY/DgwZDLW3+g3XrrrVYrjpwDV0u1zVspwz0je+CDXefw4e5sTOwXJnZJRERuweJPozlz5gAAnn/++SseEwQBWq22+1WRU+FqqfbNHBOHD/dk45ezpTh2qRIDI/3ELomIyOVZPC2l0+navTHYuCeulmpfpL8HbjbuUvzR7myRqyEicg8Wh5uW6uvrrVUHOTHTyI0PV0u16aFxPQEA3xy6hIJK/m+GiMjWLA43Wq0WL7zwAqKiouDt7Y1z584BAJ555hl89NFHVi+QHFujVofaBsOIHael2jYk2h8j4wLRpNPjk73nxS6HiMjlWRxuXnrpJXz88cd49dVXoVAozPcPGjQIH374oVWLI8dnOjQT4MjN1TxoHL357Lcc1DY0dfBsIiLqDovDzSeffIJVq1bh3nvvhVQqNd8/dOhQnDx50qrFkeMzrZTyVsogk3ZrltOlpfQPQ2yQJyrrGrGRm/oREdlUlzbx69279xX363Q6NDY2WqUoch7cnbhzpBIBD15jGL1ZvfscmrS6Dl5BRERdZXG4GTBgAHbv3n3F/Rs3bkRSUpJViiLnwZVSnXfn8BgEeSmQW1aH7w7ni10OEZHLsvjX7aVLl2LmzJnIy8uDTqfDV199haysLHzyySf47rvvbFEjOTDucdN5HgopZo+Nw+s/nsL76Wdx69BISCRCxy8kIiKLWDxyc9ttt+Hbb7/FTz/9BC8vLyxduhQnTpzAt99+ixtuuMEWNZID4+7ElpmRHAdvpQxZhVX430keyUBEZAtd+kQaN24ctm/fbu1ayAlx5MYyfh5y3Du6Bz7YeQ7/Tj+D6/uHQhA4ekNEZE0Wj9zEx8ejtLT0ivsrKioQHx9vlaLIeVTyRHCLPXhNTyhkEmTmVOC37DKxyyEicjkWh5vz58+3ecyCRqNBXl6eVYoi52FuKOZqqU4L9VHhzuHRAIB/p58VuRoiItfT6U+kb775xvz1tm3b4OfXfACgVqtFWloa4uLirFocOT7ztBRHbizy12t74f/25WDXqWIczavEoCgeqElEZC2dDjfTpk0DYDj5e+bMma0ek8vliIuLwxtvvGHV4sjxqTkt1SU9gjwxdWgkvj54Cf9KO41V948QuyQiIpfR6Wkp08nfPXr0QFFRUavTwDUaDbKysnDLLbfYslZyQOp607QUw42lHp2YAEEAfjxeiKN5lWKXQ0TkMizuucnOzkZwcLAtaiEnxKXgXdc71Bu3Do0EALyTdlrkaoiIXEeXPpHS0tKQlpZmHsFpac2aNVYpjJwDl4J3z6MTE/DNoUvYbhy9Ye8NEVH3WRxunnvuOTz//PMYMWIEIiIiuEeHK2moAS4dtOglfeoOI1bQIbRMBTSqund9mQqITAQk0g6f6ipMozdfH7yEd9JOYzV7b4iIuk3Q6/V6S14QERGBV199FTNmzLBVTTalVqvh5+eHyspK+Pr6il2OY/loEpD7m7g1jFsEXP+MuDXY2Zmiakx6ayd0euC7R6/h6A0RURss+fy2eOSmoaEBY8aM6XJx5MBKjXuu+PcApMoOn96k1+NCSQ0AID7EG90aw6srB2pLgLJz3XkXp2QavdnM0RsiIquwONw89NBD+Pzzz/HMM+7127V7MA7i3fNfILR/h8/OKa7G9W/shI9ShiOPTu7epX/7APjh7801uJn5LXpvDuVWYGiMv9glERE5LYvDTX19PVatWoWffvoJQ4YMgVzeupH0zTfftFpxZGd6Y3O40LlFdOZl4NbY48Z0Tb3u6s9zUb1DvXF7UjS+zLyIV7edxGcPjRa7JCIip2VxuDl8+DASExMBAEePHm31GJuLnZy5/apz/4422cDPshYwl/JESgK+PXQJP58pxZ7TJbgmgVsuEBF1hcXhZseOHbaogxyBKVh0euTGtAzcCnvcuPnIDQDEBHri3tE9sPbn83h120mM7T2WvzAQEXWBxZv4kSszhZvOjtxYc1qKH+IAMO+63vBSSHH4YiW2Hi0QuxwiIqfU6V+5//jHP3bqeV999VWXiyGRmXtuOhlurLmBH0duAADB3ko8OC4e/0o7jdd+zMINA8Igk/J3ECIiS3Q63LQ8BZxcVJd7bqxx9IJwWQ3ua864nvjP3vM4V1yDLzMvYvofeohdEhGRU+n0p9LatWttWQc5AgtXS1XWceTGFnxUcsy7rjde3HICb24/halDI+Gp4NldRESdxfFuasHCnhurLgU3XZMjNwBw3+hYRAd4oFCtwepd2WKXQ0TkVBhuqJml+9zUcbWUrajkUjx1Yz8AwMqdZ1Gorhe5IiIi58FwQ80s7bkxNhT7WWWfG/bcXO7mwREY1sMfdY1avL4tS+xyiIicBsMNNevqyI01p6U4cmMmCAKevmUAAGBj5kUczasUuSIiIufAcEMtdLHnxpoNxey5aWVYjwDcOjQSej3w0pYT0HNki4ioQww31KzLIzfWXArOkZvL/X1KXyhkEuw9V4rtxwvFLoeIyOEx3JBBqxGBjkdu6hu10DQZgoh1p6U4MnG56ABPPHRNTwDAi1tOoL5RK3JFRESOjeGGDFqGik6M3FQZp6QEAfC2xh4sPH7hqh65rjfCfVXIKavFBzvPiV0OEZFDY7gho5bhpuOgYdrAz0cpg0RihWDCpeBX5a2U4Z839wcA/Dv9DHLLakWuiIjIcTHckEHLUNGJcGM+V8oqy8ABLgXv2C1DIjCmVxA0TTo89+1xscshInJYDDdkYGHPjamZ2Dp73IAjN50gCAKev20gZBIBP50oxP9OsrmYiKgtDDdk0GrkpuP/LKy6DBzg8Qud1DvUBw8am4uf/eY4m4uJiNrAcENGlvXcWHcZODhyY4FHr09AmK+SzcVERO1guCEDi0durHgiuOGixjo4ctMRb6UMT99s2Ln43+lncL6kRuSKiIgcC8MNGVjcc2PFE8EBjtxY6JYhEbimdzA0TTos+eoIdy4mImqB4YYMxB65Yc+NRQRBwPLbB0MlN+xcvGF/rtglERE5DIYbMuraPjfsuRFPjyBPPHlDXwDAS9+fQJG6XuSKiIgcA8MNGVg6clPHnhtHMHtsHIZE+6GqvglLvz4mdjlERA6B4YYMLO25qbd2zw3DTVfIpBK8/MchkEkEbD1WgK1H88UuiYhIdAw3ZGDp2VJW38SPPTddNSDSF38dHw8AeObrY+YpQyIid8VwQ0YW7nNjbCj2UbHnxhE8OjEB8SFeKK7S4IXveDQDEbk3hhsysOBsKb1ebx4d8Pdkz40jUMmleOVPQyAIwMaMi/jxWIHYJRERiYbhhgzMoaLjUZvaBi0atYbn82wpx/GHuED8ZZxhemrJV0dQUq0RuSIiInEw3JCBKVR0ot+mwjhqo5BK4CGXWuf67LmxioWT+qBvmA9Kaxq4uR8RuS2GGzIyfgh2ot+morYBAODnKYfQied3CkdurEIpk+Kt6YmQSwVsP16IjRkXxS6JiMjuGG7IwIKRm8paY7+NtaakDBc21sGRhu4aEOmLBTf0AQA89+1xXCyvFbkiIiL7YrghAwt6bqzfTAyO3FjZX6/theGxAajWNGHRF4eg1TE0EpH7YLghgy703FitmRhgz42VSSUC3rxrKDwVUvx6rgzvp58RuyQiIrthuCEjS3puTOFGYcXrm6alOHJjLbFBXnju1oEAgDe3n8K+7DKRKyIisg+GGzKwaOTG0FBsm2kp670lAXcMj8btSVHQ6YHH1x9AeU2D2CUREdkcww0ZWNJzY4uGYvOsFEdurEkQBLwwbRDig72QX1mPRV8c4vJwInJ5DDdkYPrA68xqKVs2FHPoxuq8lTK8e08SFDIJ0k4W4aM92WKXRERkUww3ZGQKNx0/09RzY7UTwVtemKMKNjEw0g9P39wfAPDK1pM4lFshbkFERDbkEOFmxYoViIuLg0qlwqhRo7Bv3752n7t69WqMGzcOAQEBCAgIQEpKylWfT53UhdVS/p5WbCjmUnCbmzE6FpMHhqFRq8cjn2WilMczEJGLEj3cbNiwAQsXLsSyZcuQmZmJoUOHYvLkySgqKmrz+enp6bj77ruxY8cO7N27FzExMZg0aRLy8vLsXLmLsajnxthQzKXgTkUQBLx6x1D0DPZCXkUdHv2/A2jSMkwSkesRPdy8+eabmDNnDmbPno0BAwZg5cqV8PT0xJo1a9p8/meffYZHHnkEiYmJ6NevHz788EPodDqkpaXZuXIX06WRG27i52z8POT4YMZweCqk+OVsKV7dliV2SUREVidquGloaEBGRgZSUlLM90kkEqSkpGDv3r2deo/a2lo0NjYiMDCwzcc1Gg3UanWrG7Wlc/vcNDTpUNugBQD422SfG47c2FqfMB+8dsdQAMCqXefw7aFLIldERGRdooabkpISaLVahIWFtbo/LCwMBQUFnXqPxYsXIzIyslVAaik1NRV+fn7mW0xMTLfrdkmdHLkxrZQSBMBHJbPe9TlyY1c3D4nAX8fHAwD+vvEwThYw9BOR6xB9Wqo7Xn75Zaxfvx6bNm2CSqVq8zlLlixBZWWl+Zabm2vnKp1EJ3tuKo0b+Pmq5JBIrHQiOMCeGxH8bVJfXNM7GHWNWvz1Pxnc4I+IXIao4SY4OBhSqRSFhYWt7i8sLER4ePhVX/v666/j5Zdfxo8//oghQ4a0+zylUglfX99WN2pDJ0duTMvArdpv0/K6HLmxG5lUgnfvTkJ0gAculNbir59mQNOkFbssIqJuEzXcKBQKDB8+vFUzsKk5ODk5ud3Xvfrqq3jhhRewdetWjBgxwh6luoHO9dxU2GJ3YsOFW5VB9hHgpcBHM/8Ab6UM+7LL8I+vjnIHYyJyeqJPSy1cuBCrV6/GunXrcOLECcydOxc1NTWYPXs2AOD+++/HkiVLzM9/5ZVX8Mwzz2DNmjWIi4tDQUEBCgoKUF1dLdaP4Bos7Lnxs+YeN0BzqOLIjd31DffBinuHQSoR8GXmRfw7/azYJRERdYvo4Wb69Ol4/fXXsXTpUiQmJuLgwYPYunWruck4JycH+fn55ue///77aGhowB133IGIiAjz7fXXXxfrR3AN5l/WOxi5MYUba4/csOdGVOP7hOBZ4wnir23LwpbD+R28gojIcVlxuUvXzZ8/H/Pnz2/zsfT09Fbfnz9/3vYFuSPzyE0HDcW22MAPYM+NA5gxOhbZxTVY83M2Fv73ICL9VUjqESB2WUREFhN95IYcRCfDjU028DNc2FgHR27E9M+b++P6fqHQNOnwwMf7caaI071E5HwYbsioc6eCmxqKrT8txZEbRyCVCPjX3UkYGuOP8tpGzFyzD/mVdWKXRURkEYYbMjCHis6O3NiooZg9N6LzUsqwdtYfEB9iOIPq/o/2oaKWe+AQkfNguCEDfedGbipt1lDMkRtHEuilwCcPjES4rwqni6rxwMf7UdfAPXCIyDkw3JCBpQ3F7LlxedEBnvjkwZHw85AjM6cCj3yWgYYmhk8icnwMN2TUyZ6bOhtt4seRG4fUJ8wHa2aNgEouwY6sYjz6f5lo1PLfiIgcG8MNGXSi50an07fYxI/73LiL4bGBWDVjBBRSCbYdK8QTGw6iiQGHiBwYww0ZdKLnpqq+yfw0m/XctKyFHMa1fUKwcsYwyKUCthzOx6IvDkGr478TETkmhhsy6ETPTYXxRHAPuRRKmdTKBbS4LsONQ5rYLwwr7hkGmUTA5oOXsPjLw9Ax4BCRA2K4IaOOD84sqzGEm0AvKy8Dv/y67LtxWJMGhuPdu5MglQjYmHERf//yMEdwiMjhMNyQgXm0pP1wU15rp3DDvhuHduPgCLw1PdEccB5bf4BNxkTkUBhuyKATPTdlNYZm4gBbhBtw5MaZ3Do0EivuSTL34Mz9NAP1jdwHh4gcA8MNGXSi56asRgMACLT6HjdgQ7ETmjIoAqvvHwGlTIKfThThwXX7UdvQJHZZREQMN2TS+ZGbQC+l9S/PaSmnNKFvKNY9MBJeCil+PlOKGTyqgYgcAMMNGXRin5tyc0OxrUduOC3lTEbHB+HTh0bBVyVDxoVy3LFyL/IqeNgmEYmH4YYMOtNzY/yN3PY9Nxy5cTZJPQLwxcNjEO6rwpmiavzx3z/j+CW12GURkZtiuCGDTvTcmEdurH0iOMCRGxfQN9wHXz0yBn3CvFGo1uCuD/bi5zMlYpdFRG6I4YaMOtNzw6XgdHWR/h744uExGNUzENWaJsxauw9fZlwUuywicjMMN2RgHrnpeFrKNuGGIzeuws9DjnUPjMTNgyPQqNXjyS8O4eUfTnKzPyKyG4YbMuhgE78mrc58aCZ7bqgjKrkU796dhHnX9QIArNx5Fn/9z++o1nCpOBHZHsMNGXTQc1NZ12jOHP7WPjQT4D43LkgiEfC3yf3wzp8ToTDuhfOnf/+C3LJasUsjIhfHcEOttRNuTEcv+HnIIZPa4D8b9ty4rNsSo/DfvyYjxEeJrMIq3PreHuw+XSx2WUTkwhhuyKCDnpvSakO4CbLJlBR4cKaLS4zxxzfzx2JwlB/Kaxtx/5p9eDftNE8VJyKbYLghgw56bsptuseNiXBZLeRKIvw88MXDybh7ZAz0euCN7afw4Lr93NGYiKyO4YYMOhi5MR+aaYs9bkxM1+bIjctSyaVI/eMQvHrHEChlEuzIKsYt7+7BodwKsUsjIhfCcENGpn1urj5yY5OjF0zM1+bIjau7a0QMvnpkDHoEeuJieR3+9P4v+Hf6GS4XJyKrYLghgw5HbkzhxgaHZppw5MatDIz0w7ePXoObB0egSafHq1uzcN+HvyG/kudSEVH3MNyQQQc9NyXVGgA2bChueW323LgNPw853rsnCa/eMQSeCin2nivFje/sxtajBWKXRkROjOGGDDoYuTGFmxAfjtyQdQmCgLtGxGDLY+MwJNoPFbWNePjTDCzeeBjq+kaxyyMiJ8RwQ0ZX77kprjKEm2BvW4Yb9ty4s57BXtj48Bg8PL4XBAHY8HsuJr+1CzuyisQujYicDMMNGXQ4cmPoueHIDdmSQibBUzf2w/o5oxEb5In8ynrMXrsfi744hMpajuIQUecw3JDBVfpcGrU6c0NxsDd7bsj2RsUHYevj1+LBa3pCEICNGRdxw1s78dPxQrFLIyInwHBDBlcZuTEFG6lEsNM+Nww3BHgopHjmlgHY+HAy4oO9UFSlwUOf/I6/fPI78iq4ooqI2sdwQ6210XNj6rcJ8lJAImm7J8c61zZ9wXBDzYbHBuL7x8fhr+PjIZMI+PF4IVLe2In308+ioYlTmER0JYYbMrjKyE1xtR2aiQ0Xb10LkZFKLsWSG/tjy2PjMDIuEHWNWryy9SRu+tdu7D1bKnZ5RORgGG7I4Cr73JhGbmzaTAxwWoo61DfcBxv+Ohpv3jUUQV4KnCmqxt2rf8XcTzNwobRG7PKIyEEw3JDBVUZuSuw1ciNw5IY6JggC/jgsGv97cgJmjI6FRAB+OFqAlDd34qUtx1FZx1VVRO6O4YaM2t/nxu4jN+y5oU7w85TjhWmD8MPj12JcQjAatXqs3p2NCa/twMc/Z6NRy5BM5K4YbsjgqiM39lgGDnApOHVF33Af/OfBUfh49h+QEOqN8tpGPPvtcVz/xk58mXGRh3ESuSGGGzK4as9NPQB79tzwN26y3IS+ofjh8XF4cdogBHsrkFNWiye/OITJb+/C90fyoWPIIXIbDDdk0ImRmxB79dxwWoq6SCaV4L7Rsdj19+uweEo/+HnIcaaoGo98lolb3t2DH48VMOQQuQGGGzJyoJ4bjtxQN3kqZJg7oRd2L74Oj1+fAG+lDMfz1fjLfzIw5Z1d2HwgD03sySFyWQw3ZGAeuWkdbhqadObVJ/bb54a/WZN1+KrkWHBDH+z++3WYO6EXfJQynCqsxhMbDuK6N9Lx6a8XUN+oFbtMIrIyhhsyaKfnplBt6LdRyCTw95Tbtgbuc0M2EuClwOIp/bDnqYn42+S+CPJSILesDk9vPopxr+7Aih1nUG48ZoSInB/DDRmYAsVlPTeXjGf4RPipILQxZWVVPH6BbMzPQ4551/XGnsUT8ezUAYj0U6G4SoPXtmVhdGoanvryME4WqMUuk4i6SSZ2AeQo2u65ya80jNxE+KlsXwJ7bshOPBRSzBrbE/eMisW3hy5hzc/ZOHZJjfX7c7F+fy6S44Mwe2wcru8fBqktz1MjIptguCGDdlZLmcJNpJ+HHYpgzw3Zl0ImwZ+GR+OPw6Lw+4VyrP05G9uOFWLvuVLsPVeKKH8P3DkiGneNiEGkvz3+N0BE1sBwQwbt9NzkVxqmpcI5ckMuTBAE/CEuEH+IC0ReRR3+s/cC1u/PQV5FHd7+6TT+lXYa4/uE4M8je2Biv1DIpZzRJ3JkDDdk0M7IzaUK47SUPX5r5T435ACi/D3w1I398ERKArYdK8D/7cvBr+fKsCOrGDuyihHio8Qfh0VhWmIU+kf4il0uEbWB4YaM2uu5MYzcRHLkhtyMSi7FbYlRuC0xCtklNVi/PwdfZlxEcZUGH+w8hw92nkPfMB/clhSJ2xKjEMVpKyKHwXBDBh303ESw54bcWM9gLyy5sT+evKEv/neyCJsP5OF/J4uQVViFV7dm4dWtWRjZMxDTEqMwZVA4Ar1sfQ4bEV0Nww0ZtNFzU9+oRZlx749If47cEClkEkwZFI4pg8JRWduIH47mY/PBPPyWXYZ9xtvTm49gVM8gTBkUjskDw+3Tr0ZErTDckEEbIzcFxlEbD7kUfh423sAPYM8NORU/Tzn+PLIH/jyyBy5V1OHbQ5fwzaFLOHZJbV5tteybY0jq4Y8pAw1BJy7YS+yyidwCww0ZXdlzc8nYbxPhb4cN/ACO3JDTivT3wF/H98Jfx/dCblktth0rwA9HC5BxoRwHcipwIKcCqT+cRHyIFyb0CcV1/UIwsmcglDKp2KUTuSSGGzJo42yp/Ap77nEDsOeGXEFMoCceGhePh8bFo1Bdjx+PF2Lr0Xz8eq4M54prcK44G2t+zoaHXIqxvYMwoW8oJvQNQXSAp9ilE7kMhhsyaKPnxrRSyi67EwPNwYrhhlxEmK8KM0bHYsboWKjrG/Hz6RLsyCpCelYxiqo0+OlEEX46UQQAiA/2QnKvICT3CsLo+CA7HFRL5LoYbsigjbOlcspqAQBRAXYauWHPDbkwX5UcNw6OwI2DI6DX63E8X430rGKkZxUh40I5zpXU4FxJDT77LQcA0C/cB6PjgzCmVxBG9QyCn60PriVyIQw3ZHRlz82FUkO4iQuyUxMke27ITQiCgIGRfhgY6Yd51/VGZV0jfjM2Ie89W4qTBVXm28e/nIcgAH1CfTAsNgDDjbe4IE/79MIROSGGGzJoY7WUKdzEBtmrF4DTUuSe/DzkmDQwHJMGhgMASqs1+PVcGfaeK8EvZ0txrrgGWYVVyCqswv/tM4zsBHopMKxHc9gZGOkLLyX/L50IYLghk8t6buobtShQGxqK7TdyYwo3HLkh9xbkrcTNQyJw85AIAEBJtQYZF8qReaEcGRfKcTivEmU1DfjpRCF+OlEIwPA/n94h3hgc5YdBUX4YHO2HAREMPOSe+F89GVw2cmPqt/FRyeBvr7l+86gRR26IWgr2VmKyca8cANA0aXHsktocdjIulKOoSoPTRdU4XVSNrw7kATAEnl7GwDMgwhd9w33QN9wHoT5KTmmRS2O4IaPWPTfniqsBGLadt9//CXLkhqgzlDIphvUIwLAeAXhonOG+InU9juRV4kheJY4a/yxUa3CmqBpniqqxyRh4AMDfU44+YT7oF+7T/Ge4D3xVbFom18BwQwaXjdycKjSEm4RQH/vVYG4o5sgNkaVCfVW43leF6/uHme8rqqrH0bxKHM1T42SBGlkFVcguqUFFbaP5uIiWwnyV6BnshfgQb8QHeyE+xAvxwd6IDvCATCq5/JJEDovhhgwu67k5VVgFAEgI87ZfDVwKTmRVoT4qTOynwsR+zYGnvlGLM0XVOFVYhSzjiqxThVXIr6xHoVqDQrWhmbkluVRAj0BPc+iJCfQ03AI8EBXgwZ2WyeEw3JDBZSM3p40jN33sGm64FJzI1lRyKQYZm45bqqxtxLmSapwrrkF2SU2rrzVNOpwtrsHZ4por3k8QgDAfFWICPRAT4IloY+iJCfRElL8HwnxVUMg46kP2xXBDRqaeG0Oz4rkSEaaluBScSDR+nnIk9QhAUo+AVvfrdHpcqqwzBB5j2Mktq0VueS1yy+pQZ1xZWaCux/7z5W2+d7C3EhF+KoT7qVr9GearQoSfB8J9VfBQcPSHrIfhhgxajNyczK9Co1aPAE85ou21OzHApeBEDkgiERAd4InoAE+MSwhp9Zher0dpTYMx7NQht6wWF42hJ7e8FvkV9WjQ6lBSrUFJtQZH8irbvY6fhxzB3goEeysR4qNs8Wfr+4K8FZwGow4x3JCBebBEwMHcCgDA0Bh/+y4X5dJUIqciCAKCvQ2h4/IRH8AQfspqGpBfWY+Cynrkq+tRUFln7O+pR35lPfIr6lHXqEVlXSMq6xrbnPq6nK9KhmAfJYK9lPD3lMPfU44ATwX8PRUI8JSb/wzwUhge91BwaszNMNyQQYuRG1O4SYzxt28N7LkhcimCICDIW4kgb+UVPT4mer0e6vomFKrrUVKlQXG1BiXVDSip1qC4SmMe9SmpMtzXpDM8X13fhHOdCEIm3kqZOQT5qGTGmxy+Krn5++avDX/6ejQ/xtEi5+IQ4WbFihV47bXXUFBQgKFDh+Ldd9/FyJEj233+F198gWeeeQbnz59HQkICXnnlFdx00012rNgVGYZudBCw50wJAGBEbKCda2DPDZG7EQQBfh5y+HkY9t65Gp1Oj8q6RkPwqdagrKYB5bWNqKhpQEVdI8prG1BR2/rPyrpG6PVAtaYJ1ZomXCyv61KdCpkEvioZPBUyeCqk8FIa/zR+76k0fX3594bnerR4rkouhUougUouhZxL7G1C9HCzYcMGLFy4ECtXrsSoUaPw9ttvY/LkycjKykJoaOgVz//ll19w9913IzU1Fbfccgs+//xzTJs2DZmZmRg0aJAIP4GLMI6WFKobUFylgZdCipE97RxuOHJDRFchkQgI8FIgwEuBhA6CkIlWp0dVfSPKzaGnAVXGkZ+q+kZUGf9U17X8vsXXmiYAQEOTDiXVDQAarPozSSUCVDJD0FGa/jSFH1lzCDI9Znjc9JjhfqVcAoVUAoVMArnU8LVcJoFcKkBpvE9ufFzR4mu5VDA/XyJxrbYA0cPNm2++iTlz5mD27NkAgJUrV2LLli1Ys2YNnnrqqSue/84772DKlCn429/+BgB44YUXsH37drz33ntYuXKlXWt3KcbRksPGhr9rEoLtP0fNfW6IyMqkEgH+xn6cnrD8nDydTo/qhiao6wxhp7ZBi9qGJtRotKhrNPxp+r62oQk1DVrUNWhRozE8t6ahCbUaw591DVpUa5qgaWr+BU6r06OmQYuaBq01f2yLySRCi9AjgUIqQCGTQCaVmB+TSgTIpQJkEglkUgEyiWB+XCaVQC4RIDV+HRfkib+O7yXezyPalQE0NDQgIyMDS5YsMd8nkUiQkpKCvXv3tvmavXv3YuHCha3umzx5MjZv3mzLUjtUkHMa2dveE7WG7kgo249gAPsvVAAA7hsda/8iTCM3xzYDpWfsf30iostIAPgabx0+UWW8dUCvN4SaJp0OTTq94Wtt8/dNOj20Wr3xa12rx81f63Vo0gJa431anR5avR46HaDVG77Xme9rflyrA3R6PXRtTf/rATQab92kD4gBxr/U/TfqIlHDTUlJCbRaLcLCwlrdHxYWhpMnT7b5moKCgjafX1BQ0ObzNRoNNBqN+Xu1Wt3NqttWUZSD5LyPbfLe9qTWq3BN72Bc0zvY/hdXGDcMPL3NcCMickECDB++onwA22lAvkQ61D4Xaofo01K2lpqaiueee87m1/ENjsavoXfZ/Dq2VCfzw6A+s/DCNf3FOTH4un8CftGA1gq/NhARkWiC/XuIen1Rw01wcDCkUikKCwtb3V9YWIjw8PA2XxMeHm7R85csWdJqGkutViMmJqablV8pMq4vIh9ZbfX3dSvBvYFJL4hdBREROTlR16ApFAoMHz4caWlp5vt0Oh3S0tKQnJzc5muSk5NbPR8Atm/f3u7zlUolfH19W92IiIjIdYk+LbVw4ULMnDkTI0aMwMiRI/H222+jpqbGvHrq/vvvR1RUFFJTUwEAjz/+OMaPH4833ngDN998M9avX4/ff/8dq1atEvPHICIiIgcheriZPn06iouLsXTpUhQUFCAxMRFbt241Nw3n5ORAImkeYBozZgw+//xzPP300/jHP/6BhIQEbN68mXvcEBEREQBA0OvdaztYtVoNPz8/VFZWcoqKiIjISVjy+c19n4mIiMilMNwQERGRS2G4ISIiIpfCcENEREQuheGGiIiIXArDDREREbkUhhsiIiJyKQw3RERE5FIYboiIiMiliH78gr2ZNmRWq9UiV0JERESdZfrc7szBCm4XbqqqqgAAMTExIldCRERElqqqqoKfn99Vn+N2Z0vpdDpcunQJPj4+EATBqu+tVqsRExOD3Nxctzy3yt1/foB/B/z53fvnB/h34O4/P2C7vwO9Xo+qqipERka2OlC7LW43ciORSBAdHW3Ta/j6+rrtf9QAf36Afwf8+d375wf4d+DuPz9gm7+DjkZsTNhQTERERC6F4YaIiIhcCsONFSmVSixbtgxKpVLsUkTh7j8/wL8D/vzu/fMD/Dtw958fcIy/A7drKCYiIiLXxpEbIiIicikMN0RERORSGG6IiIjIpTDcEBERkUthuLGRl156CWPGjIGnpyf8/f3FLscuVqxYgbi4OKhUKowaNQr79u0TuyS72bVrF6ZOnYrIyEgIgoDNmzeLXZJdpaam4g9/+AN8fHwQGhqKadOmISsrS+yy7Ob999/HkCFDzJuWJScn44cffhC7LNG8/PLLEAQBTzzxhNil2M2zzz4LQRBa3fr16yd2WXaVl5eH++67D0FBQfDw8MDgwYPx+++/i1ILw42NNDQ04M4778TcuXPFLsUuNmzYgIULF2LZsmXIzMzE0KFDMXnyZBQVFYldml3U1NRg6NChWLFihdiliGLnzp2YN28efv31V2zfvh2NjY2YNGkSampqxC7NLqKjo/Hyyy8jIyMDv//+OyZOnIjbbrsNx44dE7s0u9u/fz8++OADDBkyROxS7G7gwIHIz8833/bs2SN2SXZTXl6OsWPHQi6X44cffsDx48fxxhtvICAgQJyC9GRTa9eu1fv5+Yldhs2NHDlSP2/ePPP3Wq1WHxkZqU9NTRWxKnEA0G/atEnsMkRVVFSkB6DfuXOn2KWIJiAgQP/hhx+KXYZdVVVV6RMSEvTbt2/Xjx8/Xv/444+LXZLdLFu2TD906FCxyxDN4sWL9ddcc43YZZhx5Ia6raGhARkZGUhJSTHfJ5FIkJKSgr1794pYGYmlsrISABAYGChyJfan1Wqxfv161NTUIDk5Wexy7GrevHm4+eabW/1/gTs5ffo0IiMjER8fj3vvvRc5OTlil2Q333zzDUaMGIE777wToaGhSEpKwurVq0Wrh+GGuq2kpARarRZhYWGt7g8LC0NBQYFIVZFYdDodnnjiCYwdOxaDBg0Suxy7OXLkCLy9vaFUKvHwww9j06ZNGDBggNhl2c369euRmZmJ1NRUsUsRxahRo/Dxxx9j69ateP/995GdnY1x48ahqqpK7NLs4ty5c3j//feRkJCAbdu2Ye7cuXjsscewbt06Uepxu1PBu+Opp57CK6+8ctXnnDhxwu2ayIhamjdvHo4ePepW/QYA0LdvXxw8eBCVlZXYuHEjZs6ciZ07d7pFwMnNzcXjjz+O7du3Q6VSiV2OKG688Ubz10OGDMGoUaMQGxuL//73v3jwwQdFrMw+dDodRowYgeXLlwMAkpKScPToUaxcuRIzZ860ez0MNxZ48sknMWvWrKs+Jz4+3j7FOJDg4GBIpVIUFha2ur+wsBDh4eEiVUVimD9/Pr777jvs2rUL0dHRYpdjVwqFAr179wYADB8+HPv378c777yDDz74QOTKbC8jIwNFRUUYNmyY+T6tVotdu3bhvffeg0ajgVQqFbFC+/P390efPn1w5swZsUuxi4iIiCuCfP/+/fHll1+KUg/DjQVCQkIQEhIidhkOR6FQYPjw4UhLS8O0adMAGFJ8Wloa5s+fL25xZBd6vR6PPvooNm3ahPT0dPTs2VPskkSn0+mg0WjELsMurr/+ehw5cqTVfbNnz0a/fv2wePFitws2AFBdXY2zZ89ixowZYpdiF2PHjr1i+4dTp04hNjZWlHoYbmwkJycHZWVlyMnJgVarxcGDBwEAvXv3hre3t7jF2cDChQsxc+ZMjBgxAiNHjsTbb7+NmpoazJ49W+zS7KK6urrVb2jZ2dk4ePAgAgMD0aNHDxErs4958+bh888/x9dffw0fHx9zr5Wfnx88PDxErs72lixZghtvvBE9evRAVVUVPv/8c6Snp2Pbtm1il2YXPj4+V/RXeXl5ISgoyG36rhYtWoSpU6ciNjYWly5dwrJlyyCVSnH33XeLXZpdLFiwAGPGjMHy5ctx1113Yd++fVi1ahVWrVolTkFiL9dyVTNnztQDuOK2Y8cOsUuzmXfffVffo0cPvUKh0I8cOVL/66+/il2S3ezYsaPNf++ZM2eKXZpdtPWzA9CvXbtW7NLs4oEHHtDHxsbqFQqFPiQkRH/99dfrf/zxR7HLEpW7LQWfPn26PiIiQq9QKPRRUVH66dOn68+cOSN2WXb17bff6gcNGqRXKpX6fv366VetWiVaLYJer9fbP1IRERER2QaXghMREZFLYbghIiIil8JwQ0RERC6F4YaIiIhcCsMNERERuRSGGyIiInIpDDdERETkUhhuiIiIyKUw3BAREZFLYbghIpuYMGECnnjiCbHLsFhpaSlCQ0Nx/vz5br/Xn//8Z7zxxhvdL4qILMLjF4jIYoIgXPXxZcuW4bHHHoNcLoePj4+dqmrfhAkTkJiYiLfffrvD5y5cuBBVVVVYvXo1tm3bhilTplz1+du2bcOkSZPafOzo0aO49tprkZ2dDT8/v66UTkRdwFPBichi+fn55q83bNiApUuXIisry3yft7c3vL29xSitW2pra/HRRx+ZT/O+9tprW/2sgwYNwiOPPIJHHnnEfF9ISEi77zdo0CD06tULn376KebNm2e7womoFU5LEZHFwsPDzTc/Pz8IgtDqPm9v7yumpSZMmIBHH30UTzzxBAICAhAWFobVq1ejpqYGs2fPho+PD3r37o0ffvjB/BqdTofU1FT07NkTHh4eGDp0KDZu3GhRrbNmzcLOnTvxzjvvQBAECILQ7pTT999/D6VSidGjRwMAPDw8zD+TVqtFaWkpxo0b1+pnlUqlV73+1KlTsX79eotqJqLuYbghIrtZt24dgoODsW/fPjz66KOYO3cu7rzzTowZMwaZmZmYNGkSZsyYgdraWgBAamoqPvnkE6xcuRLHjh3DggULcN9992Hnzp2dvuY777yD5ORkzJkzB/n5+cjPz0dMTEybz929ezeGDx/e5mMHDhwAAAwbNsyin3nkyJHYt28fNBqNRa8joq5juCEiuxk6dCiefvppJCQkYMmSJVCpVAgODsacOXOQkJCApUuXorS0FIcPH4ZGo8Hy5cuxZs0aTJ48GfHx8Zg1axbuu+8+fPDBB52+pp+fHxQKBTw9PTscbblw4QIiIyPbfCwzMxMxMTEICgpqdf/tt9+OgIAA3HHHHW2+LjIyEg0NDSgoKOh0zUTUPey5ISK7GTJkiPlrqVSKoKAgDB482HxfWFgYAKCoqAhnzpxBbW0tbrjhhlbv0dDQgKSkJJvUV1dXB5VK1eZjmZmZbY7aPP7443jggQewbt26Nl/n4eEBAObRKCKyPYYbIrIbuVze6ntBEFrdZ1qFpdPpUF1dDQDYsmULoqKiWr1OqVTapL7g4GCUl5e3+VhmZiYeeuihK+6fMGEC0tPT233PsrIyAFdvPCYi62K4ISKHNGDAACiVSuTk5GD8+PHdei+FQgGtVtvh85KSkvDpp59ecX9JSQlyc3Mt7rcBDMvBo6OjERwcbPFriahrGG6IyCH5+Phg0aJFWLBgAXQ6Ha655hpUVlbi559/hq+vL2bOnNnp94qLi8Nvv/2G8+fPw9vbG4GBgZBIrmw5nDx5MpYsWYLy8nIEBASY78/MzARgeTMxYGhSbm8fHCKyDTYUE5HDeuGFF/DMM88gNTUV/fv3x5QpU7Blyxb07NnT/JyPP/64w00FFy1aBKlUigEDBiAkJAQ5OTltPm/w4MEYNmwY/vvf/7a6/8CBAwgLC2u32bg99fX12Lx5M+bMmWPR64ioe7hDMRE5tWXLlmHnzp1X7XuxxJYtW/C3v/0NR48ebXN0py3p6el47733rtiD5/3338emTZvw448/WqU2IuocTksRkVP74Ycf8N5771nt/W6++WacPn0aeXl57e6H01JKSgoOHTqEmpoaREdH44svvkBycjIAQwP1u+++a7XaiKhzOHJDRERELoU9N0RERORSGG6IiIjIpTDcEBERkUthuCEiIiKXwnBDRERELoXhhoiIiFwKww0RERG5FIYbIiIicikMN0RERORSGG6IiIjIpTDcEBERkUthuCEiIiKX8v9KHooMXqIxLQAAAABJRU5ErkJggg==" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pulse = Pulse.square(parameters={'width': 0.5})\n", "source = Source.two_level(pulse=pulse)\n", "lifetime = source.lifetime(start = -1, end = 6)\n", "plt.plot(lifetime.times, lifetime.population, label = 'Lifetime')\n", "\n", "# 1/20 for visual comparison\n", "pulse = [abs(pulse.evaluate(t)) / 20 for t in lifetime.times]\n", "plt.plot(lifetime.times, pulse, label = 'Pulse')\n", "\n", "plt.xlabel('Time, t ($T_1$)')\n", "plt.ylabel('Intensity (arb)')\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "4c770049", "metadata": {}, "source": [ "Let's also take a look at all figures of merit by using the display_quality() method." ] }, { "cell_type": "code", "execution_count": 50, "id": "a833ef5e", "metadata": { "ExecuteTime": { "end_time": "2024-02-09T08:46:03.569554Z", "start_time": "2024-02-09T08:46:03.391867Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number | Probability\n", "0 | 0.00560\n", "1 | 0.94076\n", "2 | 0.05319\n", "3 | 0.00044\n", "4 | 0.00000\n", "\n", "Figure of Merit | Value\n", "Brightness | 0.9944\n", "Average photon number | 1.0485\n", "Intensity correlation | 0.0992\n", "\n", "Figure of Merit | Value\n", "First order number coherence | 0.0784\n", "Second order number coherence | 0.0023\n", "Hong-Ou-Mandel visibility | 0.8108\n", "Mean wavepacket overlap | 0.9096\n", "\n" ] } ], "source": [ "source.display_quality()" ] }, { "cell_type": "markdown", "id": "17ee448d", "metadata": {}, "source": [ "We can now clearly see how the imperfect excitation pulse affects both the shape of our photon, and its key figures of merit. Most importantly, the finite pulse width causes a significant two-photon emission probability of $p_2=0.05$, which for $\\mu$ near $1$ gives us $g^{(2)} = 0.1$. This non-unity single photon purity drastically reduces the Hong-Ou-Mandel visibility down to $V_\\text{HOM} = 0.8$. Note that here, there is no pure dephasing at all! The reduction in visibility is only due to multi-photon emission.\n", "\n", "Using the methods available in the Processor class to analyze sources, it is straightforward to compute how figures of merit depend on control parameters, such as the pulse power (pulse area) of the excitation pulse. For this, we can take advantage of the fact that all evaluated figures of merit are automatically stored in the 'quality' property of the processor. However, since a processor may have more than one output mode, the processor 'quality' is a dictionary that stores the figures of merit for each output mode. Since we have a source with a single output mode, we must ask for mode '0'. In addition, since we will update this dictionary with every new parameter, we must copy the result to avoid mutating the dicationary from previous evaluations." ] }, { "cell_type": "code", "execution_count": 51, "id": "481140ec", "metadata": { "ExecuteTime": { "end_time": "2024-02-09T08:46:13.612780Z", "start_time": "2024-02-09T08:46:03.576986Z" } }, "outputs": [ { "data": { "text/plain": "
", "image/png": 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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def quality(area: float = np.pi): # Make a function to generate the figures of merit for a given pulse area\n", " source.hom(parameters={'area': area},\n", " pseudo_limit=0.01,\n", " update_mu = True, update_g2 = True) # we can ask hom() to also compute mu and g2 (saves some time)\n", " return source.quality['0'].copy() # we return a copy of the dictionary of current stored figures of merit of mode '0' from quality\n", "\n", "areas = np.linspace(0.1, 3, 70) # make a list of pulse areas to evaluate\n", "quality_set = [quality(area * np.pi) for area in areas] # evaluate the source quality\n", "\n", "foms = ['mu', 'g2', 'c1', 'c2', 'M'] # Now, let's plot the figures of merit!\n", "for fom in foms:\n", " plt.plot(areas, [q[fom] for q in quality_set], label = fom)\n", "plt.xlabel('Pulse area ($\\pi$)')\n", "plt.ylabel('Figure of Merit')\n", "plt.legend(loc = 'upper left')\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "922052b2", "metadata": {}, "source": [ "In this plot, we can see a plethora of interesting physical results produced with just a few lines of code. The average photon number undergoes a Rabi oscillation that is dampened by the emission of photons [[T. H. Stievater et al., Phys. Rev. Lett. 87, 133603 (2001)](https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.87.133603)]. Near the first peak of the Rabi oscillation, we are driving with a pulse area of $\\pi$, usually referred to as a '$\\pi$-pulse'. This is the point that produces light close to an ideal single photon. In essence, this is the point where the pulse performs a 'bit flip' gate on the two-level system. In the case above, our pulse is quite slow, so we see a lot of degradation of the source quality.\n", "\n", "We can also see the well-known spike in $g^{(2)}$ at $2\\pi$ [[K. A. Fischer et al., Nature Physics 13, 649–654 (2017)](https://www.nature.com/articles/nphys4052)], and the increase in $c^{(1)}$ as the pulse area approaches zero [[I. Maillette de Buy Wenniger et al., Phys. Rev. Lett. 131, 260401 (2023)](https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.131.260401)]. Additionally, we can see that $c^{(2)}$ follows $g^{(2)}$ and shows a spike at $2\\pi$ similar to results in this work [[S. C. Wein, Nature Photonics 16, 374–379 (2022)](https://www.nature.com/articles/s41566-022-00979-z)], while the mean wavepacket overlap $M$ is decreased due to the increase in $g^{(2)}$. This relationship between $M$ and $g^{(2)}$ can be quite complicated, and was the subject of this paper [[H. Ollivier et al., Phys. Rev. Lett. 126, 063602 (2021)](https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.126.063602)]." ] }, { "cell_type": "markdown", "id": "c280eaa6", "metadata": {}, "source": [ "## Excitation pulses" ] }, { "cell_type": "markdown", "id": "d75b5389", "metadata": {}, "source": [ "Often times, a realistic laser pulse is modelled by a Gaussian function rather than a square function. We can build our two-level source using a different pulse shape if we wish. This is again accomplished using the Pulse class.\n", "\n", "To quickly visualize what is happening, we can use the plot() method of the Pulse class along with the plot_lifetime() method of the Source class." ] }, { "cell_type": "code", "execution_count": 52, "id": "04708b32", "metadata": { "ExecuteTime": { "end_time": "2024-02-09T08:46:13.765542Z", "start_time": "2024-02-09T08:46:13.614850Z" } }, "outputs": [ { "data": { "text/plain": "
", "image/png": 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D2rVrWbRoEe+//37bfSgv8Itws2TJEnr27EloaCjjxo1j7dq1TZ47efJkz2JGR95a2u8o/s09Uyo5JrTV7zGht2u9m7V7Cqm1O7xSl4iIP7n22muZOXMmM2bM4PTTT6d3797HbLWJjY1l2bJlTJo0iWHDhvHpp5/y3nvvecbUPPfcc8yYMYNbb72VAQMGcOGFF/L999/TvXv3tvpIXmFy/rKzro2tWLGCGTNmsHTpUsaNG8fixYt57bXX2L59O507dz7q/MLCQmw2m+fnQ4cOMXz4cP71r381Oa//SCUlJcTExFBcXEx0tKYI+6tJj3zGgaJK3vzDRE7uHteq97A7nIx84GNKqmp5Z+4khqfGerdIEWk3qqqq2LNnD7169SI0tPW/NIlvHeu/U0u+vw1vuXn88ceZPXs2s2bNYvDgwSxdupTw8HCWL1/e6PmdOnUiOTnZc/vkk08IDw/n0ksvbePKxVccDqdn08vk6Nb/I2Qxmxjbq65rare6pkREOgpDw43NZmPdunWkpaV5jpnNZtLS0lizZk2z3uPZZ5/l8ssvJyKi8VHv1dXVlJSUNLiJfyussFHrcGIyQWJUyxao+qXxvTsBrq4pERHpGAwNNwUFBdjtdpKSkhocT0pKIicn57ivX7t2LZs3b+b6669v8pxFixYRExPjuaWmpp5w3eJb7lab+IgQgi0n9ld0bC9XuPlhr/aZEhHpKAzvljoRzz77LEOHDmXs2LFNnrNgwQKKi4s9t6ysrDasUFrDHW6Sok+s1QZgcJdowq0WSqpqtc+UiEgHYWi4SUhIwGKxkJub2+B4bm4uycnJx3xteXk5r7zyCtddd90xzwsJCSE6OrrBTfxbbkk1AEknMN7GLchi9gxI/l5dUyIdnsFzaOQ4vPXfx9BwY7VaGTVqFOnp6Z5jDoeD9PR0JkyYcMzXvvbaa1RXV3PVVVf5ukxpY/UtN96Z0TCmZ924m72HvfJ+ItL+BAcHA1BRUWFwJXIs7tnQv1whuaUMX6F4/vz5zJw5k9GjRzN27FgWL15MeXk5s2bNAmDGjBmkpKSwaNGiBq979tlnufDCC9vdfhdyfN7slgIY06u+5cbpdLZqryoRad8sFguxsbHk5eUBEB7eun3rxHccDgf5+fmEh4d7tpdoLcPDzfTp08nPz+fee+8lJyeHESNGsHLlSs8g48zMTMzmhg1M27dv56uvvuLjjz82omTxMW92SwGMTI0jyGwip6SK/YcrSW3llg4i0r65hzu4A474H7PZTPfu3U84eBoebgDmzZvHvHnzGn1u1apVRx0bMGCA+k0DmDfWuDlSmNXCkJQYNmQV8f3eQoUbkQ7KZDLRpUsXOnfufMzNJcU4Vqv1qAaN1vCLcCNyJHe46eylbilwTQl3h5vfnNzNa+8rIu2PxWI54TEd4t/a9VRwCTw1dgcFZa4BZd5quYH6QcXfa1CxiEjAU7gRv1JQ5hpvE2Q2ERdu9dr7ju7hGlS8M6+MwnLbcc4WEZH2TOFG/MqhulabuAgrZrP3ZjLERVjp1zkSgO/3ar0bEZFApnAjfuVQXatKfIT3Wm3cxhyxFYOIiAQuhRvxK4Xlrm6p+Ejvh5uxWsxPRKRDULgRv+LuloqP8N5MKbfRPV3jbrYcKKbCVuv19xcREf+gcCN+xd0t1ckH3VLd4sLpGhNKrcPJhswir7+/iIj4B4Ub8SuFdS03CT7oloL6cTdrNe5GRCRgKdyIXzlUN+amkw+6peDI9W4UbkREApXCjfgVX3ZLQX24ydhXRI3d4ZNriIiIsRRuxK8c8nG3VL/OkcSEBVNZY+engyU+uYaIiBhL4Ub8SqGPW27MZhNj6mZNqWtKRCQwKdyI36iqsVNW7ZqiHR/pmzE3AKPd693sUbgREQlECjfiN9ytNsEWE9Ghvtuw3j3u5od9h3E6nT67joiIGEPhRvyGO9zEhVsxmby3r9QvDU2JITTYTGG5jV35ZT67joiIGEPhRvyGe0dwX3ZJAViDzIxMdY27WbtHWzGIiAQahRvxG4U+3DTzlzyL+e055PNriYhI21K4Eb/h2VfKR9PAjzTWs5ifWm5ERAKNwo34DV8v4Hekkd1jsZhNHCiq5EBRpc+vJyIibUfhRvxGcWX9gGJfiwgJYkhKDADfa0q4iEhAUbgRv1FUUQNAbHhwm1xvbN1ift8p3IiIBBSFG/Eb7nATE9Y24UabaIqIBCaFG/Ebhytc3VKxbdAtBfXhZmdemWemloiItH8KN+I3iitdLTdxbdQtFRdhpV/nSECtNyIigUThRvyGZ8xNWNu03ED9ejcaVCwiEjgUbsQvVNXYqayxAxDTRi03AOPci/mp5UZEJGAo3IhfKKnrkjKbICrEd5tm/pJ73M2WgyWU1+1ILiIi7ZvCjfiFosr6mVJms+82zfylrrFhpMSGYXc4ycjUasUiIoFA4Ub8wuHytlvA75fGatyNiEhAUbgRv+BpuWnD8TZu7q4pjbsREQkMCjfiF4o9M6XaPty4W27WZxZRXWtv8+uLiIh3GR5ulixZQs+ePQkNDWXcuHGsXbv2mOcXFRUxd+5cunTpQkhICP379+eDDz5oo2rFV4oq23YBvyP1SYwgPsJKda2DzQeK2/z6IiLiXYaGmxUrVjB//nwWLlxIRkYGw4cPZ+rUqeTl5TV6vs1m46yzzmLv3r28/vrrbN++nWXLlpGSktLGlYu3tfXWC0cymUyMrttnau0eDSoWEWnvDA03jz/+OLNnz2bWrFkMHjyYpUuXEh4ezvLlyxs9f/ny5RQWFvL2228zadIkevbsyemnn87w4cPbuHLxtsMV7tWJ277lBrTPlIhIIDEs3NhsNtatW0daWlp9MWYzaWlprFmzptHXvPvuu0yYMIG5c+eSlJTEkCFDePjhh7Hbmx4nUV1dTUlJSYOb+J9iT7dU27fcwBEzpvYWYnc4DalBRES8w7BwU1BQgN1uJykpqcHxpKQkcnJyGn3N7t27ef3117Hb7XzwwQfcc889PPbYYzz00ENNXmfRokXExMR4bqmpqV79HOIdnq0XDAo3g7tEE2G1UFpVy/acUkNqEBER7zB8QHFLOBwOOnfuzDPPPMOoUaOYPn06d911F0uXLm3yNQsWLKC4uNhzy8rKasOKpbmMHHMDEGQxc3IP97ibQ4bUICIi3mFYuElISMBisZCbm9vgeG5uLsnJyY2+pkuXLvTv3x+LxeI5NmjQIHJycrDZbI2+JiQkhOjo6AY38T/uHcGNmC3lNr53PADfaTE/EZF2zbBwY7VaGTVqFOnp6Z5jDoeD9PR0JkyY0OhrJk2axM6dO3E4HJ5jO3bsoEuXLlitxn0pyokrqnCvUGxMyw3A+N6ucTff7j6EQ+NuRETaLUO7pebPn8+yZct44YUX2Lp1K3PmzKG8vJxZs2YBMGPGDBYsWOA5f86cORQWFnLTTTexY8cO3n//fR5++GHmzp1r1EcQL6ixOyi3uQaFR4caF26GdYslLNjC4Yoatudq3I2ISHvVdtsvN2L69Onk5+dz7733kpOTw4gRI1i5cqVnkHFmZiZmc33+Sk1N5aOPPuKWW25h2LBhpKSkcNNNN3HHHXcY9RHEC9w7ggNEhRr3VzLYYmZ0zzhW/1zAt7sPMaiLujBFRNojk9Pp7FDt7yUlJcTExFBcXKzxN35iT0E5Zzy6isiQIDbfP9XQWv65aid/Xbmdswcn8cyM0YbWIiIi9Vry/d2uZktJYHK33EQb2GrjNuGIQcUadyMi0j4p3IjhSqrqwo1B08CPNCQlhgirheLKGrbmaMFHEZH2SOFGDFdSWQv4R7gJtpgZ08s9a0pTwkVE2iOFGzGcp+XGwJlSR3Kvd7NmlxbzExFpjxRuxHDuBfyiw4wfcwNHjrs5pH2mRETaIYUbMVz9gGL/aLk5qWs0USFBlFbVsjVb425ERNobhRsxnD8NKAbXPlPucTfqmhIRaX8UbsRwngHFfjAV3M3dNfXtboUbEZH2RuFGDOdvLTdQP6h47Z5Cau2O45wtIiL+ROFGDOdvY24ABneNJjo0iNLqWn7SuBsRkXZF4UYMV1Ll6paK8aOWG4vZxNhemhIuItIeKdyI4fxtKrjb+N51g4o17kZEpF1RuBHD+WO3FMDEPgmAa9yNrVbjbkRE2guFGzFUVY2d6rrg4E8DigEGJkcRH2GlwmZnfeZho8sREZFmUrgRQ5XWjbcxmSAqxL+6pcxmE5P6ulpvvtpZYHA1IiLSXAo3Yij3NPDIkCDMZpPB1RztlH4KNyIi7Y3CjRjKX8fbuJ1S13LzY1aRZ+CziIj4N4UbMZQ7MPjTNPAjdY0No3diBA6npoSLiLQXCjdiKPcaN/42DfxIp9a13nytrikRkXZB4UYM5e/dUoAGFYuItDMKN2Io94DiKD8ON+P7xGMxm9hTUM7+wxVGlyMiIsehcCOGKqvrloryox3Bfyk6NJgRqbEAfPWzWm9ERPydwo0YqrQdhBtQ15SISHuicCOGKvV0S/l3uDm1br2bb3YdwuFwGlyNiIgci8KNGKqs2t1y479jbgBGpMYSGRJEYbmNn7JLjC5HRESOQeFGDOWeCh7pZ1sv/FKwxezZJVxdUyIi/k3hRgzVXsbcwBHjbjSoWETErynciKHKqv1/Kribe9zN2r2FVNXYDa5GRESaonAjhnK33ES3g5abPomRJEeHYqt18N2eQqPLERGRJijciGGcTqcn3ES2g3BjMpk4vX8iAKu25xlcjYiINEXhRgxTVePAXjetuj10SwFMHuAKN19szze4EhERaYrCjRjGvcaNyQQRVovB1TTPpH4JBJlN7C4oJ/OQtmIQEfFHCjdimCOngZtMJoOraZ7o0GBO7hEHwKod6poSEfFHfhFulixZQs+ePQkNDWXcuHGsXbu2yXOff/55TCZTg1toaGgbVive4m658ecdwRvj7ppapa4pERG/ZHi4WbFiBfPnz2fhwoVkZGQwfPhwpk6dSl5e078VR0dHk52d7bnt27evDSsWb6lfndj/BxMfaXL/zgB8s6tAU8JFRPyQ4eHm8ccfZ/bs2cyaNYvBgwezdOlSwsPDWb58eZOvMZlMJCcne25JSUltWLF4S2k7WZ34lwZ1iSIpOoSqGgdrNSVcRMTvGBpubDYb69atIy0tzXPMbDaTlpbGmjVrmnxdWVkZPXr0IDU1lQsuuIAtW7Y0eW51dTUlJSUNbuIf2summb/UcEq4uqZERPyNoeGmoKAAu91+VMtLUlISOTk5jb5mwIABLF++nHfeeYcXX3wRh8PBxIkT2b9/f6PnL1q0iJiYGM8tNTXV659DWqd+64X2NeYGYPIAV9eUBhWLiPgfw7ulWmrChAnMmDGDESNGcPrpp/Pmm2+SmJjI008/3ej5CxYsoLi42HPLyspq44qlKe1pX6lfmtQ3AYvZxO78crIKNSVcRMSfGBpuEhISsFgs5ObmNjiem5tLcnJys94jODiYkSNHsnPnzkafDwkJITo6usFN/EN7Wp34l2LCghnVvW5KuFYrFhHxK4aGG6vVyqhRo0hPT/ccczgcpKenM2HChGa9h91uZ9OmTXTp0sVXZYqPuDfNbG9Twd1O15RwERG/ZHi31Pz581m2bBkvvPACW7duZc6cOZSXlzNr1iwAZsyYwYIFCzznP/DAA3z88cfs3r2bjIwMrrrqKvbt28f1119v1EeQVmrP3VIAZ9SNu/laU8JFRPyK4d8q06dPJz8/n3vvvZecnBxGjBjBypUrPYOMMzMzMZvrM9jhw4eZPXs2OTk5xMXFMWrUKL755hsGDx5s1EeQVmqvU8HdBnWJIiU2jANFlXy9s4Apg7QkgYiIPzA5nU6n0UW0pZKSEmJiYiguLtb4G4Nd8ORX/Li/mGUzRnPW4PYZDO59ZzP/XrOPK8amsug3w4wuR0QkYLXk+9vwbinpuErb6QrFR3K31qRvzcPh6FC/J4iI+C2FGzFMe++WAhjfuxMRVgt5pdVsPlhsdDkiIoLCjRiovW6ceaSQIAun1a1W/OlPucc5W0RE2oLCjRiixu6gqsYBtO9uKajvmvp0q9a7ERHxBy36VikqKuKtt95i9erV7Nu3j4qKChITExk5ciRTp05l4sSJvqpTAkxZXZcUtM9F/I50xoBEzCb4KbuEg0WVdI0NM7okEZEOrVktNwcPHuT666+nS5cuPPTQQ1RWVjJixAimTJlCt27d+PzzzznrrLMYPHgwK1as8HXNEgDc421Cg80EW9p3A2J8ZAgn161WnL5VXVMiIkZr1q/MI0eOZObMmaxbt67J9WQqKyt5++23Wbx4MVlZWdx2221eLVQCS2m1e0fw9jve5khTBiXxw77DfLo1j6sn9DS6HBGRDq1Z4eann34iPj7+mOeEhYVxxRVXcMUVV3Do0CGvFCeBq72vTvxLZw3uzF9WbmPNrkOUV9cS0Y5ngImItHfN6g84XrA50fOl4/GEmwAJAX0SI+kRH47N7mD1z9prSkTESK0a7LB9+3bmzZvHlClTmDJlCvPmzWP79u3erk0CWFmAdUuZTCbOqps19fEWjbsRETFSi8PNG2+8wZAhQ1i3bh3Dhw9n+PDhZGRkMGTIEN544w1f1CgBKNC6pQCmDkkG4NOtudhqHQZXIyLScbX4m+X2229nwYIFPPDAAw2OL1y4kNtvv52LL77Ya8VJ4AqE1Yl/6eTucSREhlBQVs23uw95FvcTEZG21eKWm+zsbGbMmHHU8auuuors7GyvFCWBr6QqsLqlACxmE2ef5OqaWrklx+BqREQ6rhaHm8mTJ7N69eqjjn/11VeceuqpXilKAl9ZAHZLAZxzkqtr6uMtudi1kaaIiCGa9c3y7rvveh6ff/753HHHHaxbt47x48cD8O233/Laa69x//33+6ZKCTiBOOYGYHzveKJDgygoqyYj8zBjenYyuiQRkQ7H5HQ6j/vrpdncvAYek8mE3W4/4aJ8qaSkhJiYGIqLi4mOjja6nA7r2ue/57Ntefzl4qFMH9Pd6HK86pYVG3hr/QGuO6UX9/y68UUvRUSkZVry/d2s1OJwOJp18/dgI/6jzDOgOHDG3LhNreuaWrk5h2b87iAiIl7WojE3NTU1TJkyhZ9//tlX9UgHUVpdF24CrFsK4PT+iYQGmzlQVMmWgyVGlyMi0uG0KNwEBwezceNGX9UiHUipZ7ZU4IWbMKuFyf07A/CRZk2JiLS5Fs+Wuuqqq3j22Wd9UYt0IGXVgbX9wi+dU7eg34ebFW5ERNpai79ZamtrWb58OZ9++imjRo0iIiKiwfOPP/6414qTwOR0OuvH3ARgyw3AGQM7E2wxsTOvjJ9zS+mXFGV0SSIiHUaLv1k2b97MySefDMCOHTsaPGcymbxTlQS0qhoHtXVrwATSIn5HigkL5tR+iXy2LY/3NmYz/yyFGxGRttLicPP555/7og7pQErrNs00mSA82GJwNb7z62Fd+GxbHu9vPMgtaf0U/kVE2kirdgUXORGeLilrEGZz4H7hnzU4CWuQmV355WzLKTW6HBGRDqNVAx5++OEHXn31VTIzM7HZbA2ee/PNN71SmASuQF2d+JeiQoOZ3D+Rj3/K5X8bDzKoixaNFBFpCy1uuXnllVeYOHEiW7du5a233qKmpoYtW7bw2WefERMT44saJcCUBfAaN7/06+FdAXh/Y7YW9BMRaSMtDjcPP/wwf//733nvvfewWq383//9H9u2beOyyy6je/fAWkZffKPUszpx4IebKQM7ExpsZu+hCi3oJyLSRlocbnbt2sV5550HgNVqpby8HJPJxC233MIzzzzj9QIl8NQv4BeYM6WOFBESxJkDXQv6vbfxoMHViIh0DC0ON3FxcZSWugZHpqSksHnzZgCKioqoqKjwbnUSkDpStxTAr4epa0pEpC21ONycdtppfPLJJwBceuml3HTTTcyePZsrrriCKVOmeL1ACTzu2VKBujrxL50xoDPhVgv7D1fy4/5io8sREQl4Lf52efLJJ6mqqgLgrrvuIjg4mG+++YaLL76Yu+++2+sFSuBxb5oZ6LOl3MKsFtIGJfHujwd578eDjEiNNbokEZGA1uJvl06dOnkem81m7rzzTq8WJIGvfkBx4I+5cfv1sC6ecPOnXw3CEsDr+4iIGK1Z3VLl5eUtetOWnr9kyRJ69uxJaGgo48aNY+3atc163SuvvILJZOLCCy9s0fXEWB1tzA3A5AGdiQ0PJq+0mjW7DhldjohIQGtWuOnbty+PPPII2dnZTZ7jdDr55JNPOPfcc/nHP/7R7AJWrFjB/PnzWbhwIRkZGQwfPpypU6eSl5d3zNft3buX2267jVNPPbXZ1xL/UD9bquOEG2uQmfOGdgHgrfUHDK5GRCSwNSvcrFq1iu+//55evXoxbtw45s6dy5///Gcee+wx7r77bn7zm9/QtWtXrr32WqZNm8btt9/e7AIef/xxZs+ezaxZsxg8eDBLly4lPDyc5cuXN/kau93OlVdeyf3330/v3r2bfS3xDx1tQLHbRSNTAFi5OZtKm93gakREAlezvl0GDBjAG2+8QWZmJq+99hqrV6/mm2++obKykoSEBEaOHMmyZcs499xzsViavxGizWZj3bp1LFiwwHPMbDaTlpbGmjVrmnzdAw88QOfOnbnuuutYvXr1Ma9RXV1NdXW15+eSEi2kZrSO2C0FMKpHHN3iwth/uJJPtuZyft3qxSIi4l0t+nbp3r07t956K7feeqtXLl5QUIDdbicpKanB8aSkJLZt29boa7766iueffZZNmzY0KxrLFq0iPvvv/9ESxUvqt9bquMMKAYwmUxcNDKFJz7byTvrDyjciIj4SLvaFby0tJSrr76aZcuWkZCQ0KzXLFiwgOLiYs8tKyvLx1XK8bjH3HSE7Rd+6YIRrq6pL3bkc6is+jhni4hIaxj67ZKQkIDFYiE3N7fB8dzcXJKTk486f9euXezdu5dp06Z5jjkcDgCCgoLYvn07ffr0afCakJAQQkJCfFC9tIbT6fR0S3WkAcVufTtHMqxbDBv3F/P+pmxmTOhpdEkiIgHH0JYbq9XKqFGjSE9P9xxzOBykp6czYcKEo84fOHAgmzZtYsOGDZ7b+eefzxlnnMGGDRtITU1ty/KlFSpsdhx1OxB0xHADcGFd641mTYmI+Ibh3y7z589n5syZjB49mrFjx7J48WLKy8uZNWsWADNmzCAlJYVFixYRGhrKkCFDGrw+NjYW4Kjj4p/crTZmE4QFN3/weSCZNrwrf/5gK+szi9hTUE6vhAijSxIRCSiGh5vp06eTn5/PvffeS05ODiNGjGDlypWeQcaZmZmYze1qaJAcQ/3qxEGYTB1zld7EqBBO6ZvAFzvyeWPdfm6bOsDokkREAorJ2YptilevXs3TTz/Nrl27eP3110lJSeE///kPvXr14pRTTvFFnV5TUlJCTEwMxcXFREdHG11Oh7M+8zAX/fMbUmLD+PrOM40uxzD/23iQeS+vp0tMKF/dcaa2YxAROY6WfH+3uEnkjTfeYOrUqYSFhbF+/XrPGjLFxcU8/PDDratYOoyOPJj4SGmDkogJCya7uIqvdxYYXY6ISEBpcbh56KGHWLp0KcuWLSM4uH6dkkmTJpGRkeHV4iTwlB3RLdWRhQZbuHCEa52bV3/Q8gQiIt7U4nCzfft2TjvttKOOx8TEUFRU5I2aJIDVL+DXscMNwKWjXbP7Pv4pl+KKGoOrEREJHC0ON8nJyezcufOo41999ZX2eZLjKvVsvdCxViduzEldoxnUJRpbrYN3f9S0cBERb2lxuJk9ezY33XQT3333HSaTiYMHD/LSSy9x2223MWfOHF/UKAFE3VL1TCYTl47qBsCrP+w3uBoRkcDR4m+YO++8E4fDwZQpU6ioqOC0004jJCSE2267jRtuuMEXNUoAcW+9EK1uKQAuHJnCog+3sulAMdtyShiYrBl8IiInqsUtNyaTibvuuovCwkI2b97Mt99+S35+Pg8++KAv6pMA49kRXC03AHSKsJI2yLWm02tqvRER8YpWr45ntVqJioqiS5cuREZGerMmCWD1Y24UbtwuHe3qmnpr/QGqa+0GVyMi0v61ONzU1tZyzz33EBMTQ8+ePenZsycxMTHcfffd1NRoxoccW6nG3BzltH6JdIkJpbDcxkdbco//AhEROaYWh5sbbriBZ555hr/+9a+sX7+e9evX89e//pVnn32WG2+80Rc1SgApqxtzE6XZUh5BFjPTx7imhb/07T6DqxERaf9a/Ovzyy+/zCuvvMK5557rOTZs2DBSU1O54ooreOqpp7xaoAQWrVDcuOljUvlH+s98t6eQnXll9O2srl4RkdZqcctNSEgIPXv2POp4r169sFqt3qhJApi6pRrXJSaMMwe6Bhb/d22mwdWIiLRvLQ438+bN48EHH/TsKQVQXV3Nn//8Z+bNm+fV4iTwlGmF4iZdOa47AG9k7KeqRgOLRURaq8XfMOvXryc9PZ1u3boxfPhwAH788UdsNhtTpkzhN7/5jefcN99803uVSrvncDgps2m2VFNO659ISmwYB4oq+WBTNr85uZvRJYmItEst/oaJjY3l4osvbnAsNTXVawVJ4KqoseN0uh5HhWhA8S9ZzCYuH5PKY5/s4OXvMhVuRERaqcXh5rnnnvNFHdIBuFcnDjKbCA1u9RJLAW36mFQWp//MD/sOsyO3lP5JUUaXJCLS7ugbRtqMZ1+p0CBMJpPB1finztGhnFW3YvGLmhYuItIqrRr48Prrr/Pqq6+SmZmJzWZr8FxGRoZXCpPAU6qtF5rl6gk9WLklhzfW7eePUwdoTSARkRZqccvNP/7xD2bNmkVSUhLr169n7NixxMfHs3v37gZr34j8UqlnppS+rI9lYp94+nWOpNxm5/V12m9KRKSlWhxu/vnPf/LMM8/wxBNPYLVauf322/nkk0+48cYbKS4u9kWNEiA808DVcnNMJpOJGRN7AvDCN3txOJzGFiQi0s60ONxkZmYyceJEAMLCwigtLQXg6quv5r///a93q5OAUlbtGlCsaeDH95uRKUSFBrH3UAVf/JxvdDkiIu1Ki8NNcnIyhYWFAHTv3p1vv/0WgD179uB06jdMaVqpFvBrtoiQIC4b7Vpi4YVv9hpbjIhIO9PicHPmmWfy7rvvAjBr1ixuueUWzjrrLKZPn85FF13k9QIlcGjrhZaZMaEHJhOs2p7P7vwyo8sREWk3Wvwt88wzz+BwOACYO3cu8fHxfPPNN5x//vn8v//3/7xeoAQO96aZ6pZqnh7xEZw5oDPp2/L495p93Hf+SUaXJCLSLrT4W8ZsNmM21zf4XH755Vx++eVeLUoCk3sRv2jNlmq2mRN7kr4tj9fX7efWs/trppmISDO06lfooqIi1q5dS15enqcVx23GjBleKUwCT5nWuWmxU/sl0Ccxgl355az4PovrT+1tdEkiIn6vxd8y7733HldeeSVlZWVER0c3WGnWZDIp3EiTNOam5UwmE9ef2psFb27iua/3MnNiT4ItWlhcRORYWvyv5K233sq1115LWVkZRUVFHD582HNzz6ISaYxmS7XORSNTSIi0enYLFxGRY2txuDlw4AA33ngj4eHhvqhHApgGFLdOaLCFGRN6ArBs9W4tuSAichwtDjdTp07lhx9+8EUtEuDqVyjWoNiWump8D0KDzWw+UMKa3YeMLkdExK8161do97o2AOeddx5//OMf+emnnxg6dCjBwQ2/qM4//3zvVigBwz1bquvPL8F//w7mIJh8J4yaaXBl/q9ThJVLR6Xyn2/38a/Ve5jYJ8HokkRE/JbJ2Yw27iOnfh/zzUwm7Hb7CRflSyUlJcTExFBcXEx0dLTR5XQYdoeTPn/6gCss6SwKfrbhkxc/C0MvMaawdmRvQTlnPLYKpxM+ueU0+iVFGV2SiEibacn3d7NSi8PhaNbN34ONGKfcVksih7kr6CXXgUk3w/i5rscf3AYVGox+PD0TIpg6OBlwjb0REZHG+cWc0iVLltCzZ09CQ0MZN24ca9eubfLcN998k9GjRxMbG0tERAQjRozgP//5TxtWK61RWlXLdUEfEmmqgpRRMGUhnHU/JA6CysPw/bPHfxNh9mmudW7eWn+Ag0WVBlcjIuKfmh1u1qxZw//+978Gx/7973/Tq1cvOnfuzO9+9zuqq6tbXMCKFSuYP38+CxcuJCMjg+HDhzN16lTy8vIaPb9Tp07cddddrFmzho0bNzJr1ixmzZrFRx991OJrS9spLyvlt5Z01w+n3wFmM1iC4bTbXMfWPgP2WuMKbCdG9YhjfO9O1NidPPOlWm9ERBrT7HDzwAMPsGXLFs/PmzZt4rrrriMtLY0777yT9957j0WLFrW4gMcff5zZs2cza9YsBg8ezNKlSwkPD2f58uWNnj958mQuuugiBg0aRJ8+fbjpppsYNmwYX331VYuvLW3HsvNjok2V5JgSoe9Z9U8MvgDCOkF5Huz5wrgC25F5Z/QD4JXvMykoa/kvFCIiga7Z4WbDhg1MmTLF8/Mrr7zCuHHjWLZsGfPnz+cf//gHr776aosubrPZWLduHWlpafUFmc2kpaWxZs2a477e6XSSnp7O9u3bOe200xo9p7q6mpKSkgY3aXvRO98GYHXI6a5WGzdLMJxUt5v8ptfbvrB2aFLfeIZ3i6GqxsGzX+0xuhwREb/T7HBz+PBhkpKSPD9/8cUXnHvuuZ6fx4wZQ1ZWVosuXlBQgN1ub/C+AElJSeTk5DT5uuLiYiIjI7FarZx33nk88cQTnHXWWY2eu2jRImJiYjy31NTUFtUoXmCroNNBV6vMuugzj35+6KWu+23/A3tNGxbWPplMJuae0ReA/6zZR3Gl/sxERI7U7HCTlJTEnj2u3xJtNhsZGRmMHz/e83xpaelRa974SlRUFBs2bOD777/nz3/+M/Pnz2fVqlWNnrtgwQKKi4s9t5YGMPGCzG+wOGwcdHaiKHLA0c+njoWwOKgugf1aILI50gYlMSApirLqWv79zV6jyxER8SvNDje/+tWvuPPOO1m9ejULFiwgPDycU0891fP8xo0b6dOnT4sunpCQgMViITc3t8Hx3NxckpOTmy7abKZv376MGDGCW2+9lUsuuaTJ8T4hISFER0c3uEkb2/U5AKvtw4gObyQAmy3Qp65FZ1d6GxbWfpnNJv5whuv/t+Vf76G8WoOxRUTcmh1uHnzwQYKCgjj99NNZtmwZy5Ytw2q1ep5fvnw5Z599dosubrVaGTVqFOnp9V9oDoeD9PR0JkyY0Oz3cTgcrZqpJW3EHW4cQ4kObaJ1r0/deK6dCjfNdd7QLvSID+dwRQ0vfbfP6HJERPxGs3cwTEhI4Msvv/SMd7FYLA2ef+2114iMjGxxAfPnz2fmzJmMHj2asWPHsnjxYsrLy5k1axYAM2bMICUlxdMys2jRIkaPHk2fPn2orq7mgw8+4D//+Q9PPfVUi68tbaA0F/K24MDE146TmBXWVLg5w3WfvQGqSyFEq+8eT5DFzNwz+nL76xt5+ovdXDmuBxEh2pRURKTF/xLGxMQ0erxTp06tKmD69Onk5+dz7733kpOTw4gRI1i5cqVnkHFmZmaD7R/Ky8v5wx/+wP79+wkLC2PgwIG8+OKLTJ8+vVXXFx/LdM16O2jtzeGqaKKa2hE8uivEdIfiTNe4G3fYkWP6zcgUlny+k32HKvj3mn3MmdyyrmERkUDUrL2lAon2lmpjH90Fa57k04hfc/2h3/LYpcO5eFS3xs9943rY9BpM/hNMvqNt62zH3szYz/xXfyQ2PJjVt59BVFNdfyIi7ZjX95YSabUD6wDYbO4PQHRT3VIAqeNc91nf+rqqgHL+8K70ToygqKKG57/ea3Q5IiKGU7gR37HXwMH1AKy3u7pLopvqloIjws334NAmrM0VZDFz0xTXqsXLVu/Wujci0uEp3Ijv5G6G2ioIjeWn6kTgOC03SSeBNRJspZC/rY2KDAy/HtaV/kmRlFTVslyrFotIB6dwI75TtyCfs9toiqscwHHCjdkCXYa7Hmdv9HV1AcViNnFzmqvrb/lXezhcbjO4IhER4yjciO/s/x6A2i6jsNnrws2xuqUAkoe57rN/9GVlAemck5IZ3CWa0upanvx8p9HliIgYRuFGfKcuoJTHDwXAbIII63HCjaflRuGmpcxmE3ecOxBw7TmVVVhhcEUiIsZQuBHfqKmCgp8BOBzt6i6JCg3GbDYd+3XucJOzERwOX1YYkE7rl8CkvvHY7A4e/2SH0eWIiBhC4UZ8I38rOO0QFkeh2T2YuBlrRib0h6BQsJXBYQ2MbSmTycSd5wwC4O0NB9hysNjgikRE2p7CjfhG7hbXfdIQSuo2dWxyX6kjWYJcs6bAtRWDtNjQbjGcP7wrTic88qFmnYlIx6NwI76Rs9l1nzyUkrp1V5oVbkDjbrzgtrMHEGwxsfrnAr76ucDockRE2pTCjfhGbl24SRpCSVVdy01zuqWgfsZUziYfFNYxdI8P58pxPQB4ZOVWHI4OtcuKiHRwCjfifU5nfTBJHuJpuWn2nkedB7vu89SlciJuOLMvkSFBbD5Qwrs/HjS6HBGRNqNwI95XcgCqisAcBIkDKalqYbdUZ9d0ZkoPQmWRT0rsCOIjQzy7hD/y4TYqbLUGVyQi0jYUbsT73ONtEvpDUAgllS3slgqNgegU12Ntw3BCrjulF93iwsgpqeKpVbuMLkdEpE0o3Ij3ecbbuGY9lba05Qags2s6M3k/ebOyDic02MLd57n+LJ/+crcW9hORDkHhRryvbvE+El3dS/UDilsTbtRyc6KmnpTMxD7x2God/Pn9rUaXIyLicwo34n0FdSvjJrhWJq6fCt7MbimARLXceIvJZGLhtJOwmE2s3JLDNzs1NVxEApvCjXiX01nfcpPQD6B+QHGrWm7U0uANA5KjuGpcdwDuf+8nau3a2kJEApfCjXhXWS7YSsFkhk69AeoHFLdkzE3iANd9RQGU5Xu7yg7plrP6ExcezPbcUl76LtPockREfEbhRrzL3SUV1xOCQoAjW25a0C1ljXC9B7j2qZITFhtuZf7ZrtD42MfbyS+tNrgiERHfULgR7/rFeJuqGju2WlcXSIu6peCIcTcKN97y27HdGZISTUlVLQ+9r/FMIhKYFG7Eu5oYb2MyQaS1BS03AIn9G76nnDCL2cSii4ZhNsE7Gw7y5Q51+YlI4FG4Ee86aqaUa7xNVEgQZrOpZe+V4A43O7xVneDaNXzmxJ4A3PPOZqpq7MYWJCLiZQo34l2elpu6cNOamVJu7nBzaKc3KpMj3Hr2AJKjQ9l3qIInP9Ofr4gEFoUb8R5bORRnuR4ftcZNK8JNfF/XfckBqC71RoVSJzIkiPvOd60g/fSXu/g5V3++IhI4FG7Ee9wtLOHxEN4JqF+dOKolC/i5hXeC8ISG7y1eM/WkJNIGJVFjd/KntzbhcDiNLklExCsUbsR7ftElBUe03LSmW+rI99KgYq8zmUzcf8FJhFstfL/3MC+t1do3IhIYFG7Ee9wDf93dSUBxXbiJbXW4qXsvhRufSIkN449TXWvfLPpgqzbWFJGAoHAj3tNIy01RhQ2A2PATbbnRjClfmTmhJ2N7daLCZuf21zeqe0pE2j2FG/GeRsLN4Yq6lptwa+veU91SPmc2m/jbJcMIC7awZvchXvpun9EliYicEIUb8Q6HAw41XMAPoMgTblrbclP3XoW7wKH1WHylR3wEd547EICHP9hG5iF1T4lI+6VwI95RnAW1VWCxQmyP+sOVdd1SYa1suYnt4XrP2qr6aebiE1eP78H43p2orLHzx9d/VPeUiLRbfhFulixZQs+ePQkNDWXcuHGsXbu2yXOXLVvGqaeeSlxcHHFxcaSlpR3zfGkj7m6jTn3AUj/t291yE9falhuzxfWeR15DfMJsNvHXi4cTbrXw3Z5CXliz1+iSRERaxfBws2LFCubPn8/ChQvJyMhg+PDhTJ06lby8vEbPX7VqFVdccQWff/45a9asITU1lbPPPpsDBw60ceXSgGfbhX4NDrvH3MS0Ntwc+Z4KNz7XPT6cBXXdU4s+3Mb2HC3uJyLtj+Hh5vHHH2f27NnMmjWLwYMHs3TpUsLDw1m+fHmj57/00kv84Q9/YMSIEQwcOJB//etfOBwO0tPT27hyaeAXe0oBOJ3O+m6p1g4ohiPCjWZMtYWrxvfgjAGJ2God3Pjf9dp7SkTaHUPDjc1mY926daSlpXmOmc1m0tLSWLNmTbPeo6KigpqaGjp16tTo89XV1ZSUlDS4iQ80MlOqwmanxu4at9Hqbqkj31MtN23CZDLxt0uHkxAZwvbcUhZ9sNXokkREWsTQcFNQUIDdbicpKanB8aSkJHJycpr1HnfccQddu3ZtEJCOtGjRImJiYjy31NTUE65bGuFpualfwO9w3Ro3VouZsGBL699bLTdtLiEyhEcvHQbAC2v28dm2XIMrEhFpPsO7pU7EI488wiuvvMJbb71FaGhoo+csWLCA4uJizy0rSzNuvK6yCMrrxkjFHz0NPCY8GJPJ1Pr3d79neZ7rWtImJg/ozHWn9ALgttc2kldSZXBFIiLNY2i4SUhIwGKxkJvb8LfC3NxckpOTj/naRx99lEceeYSPP/6YYcOGNXleSEgI0dHRDW7iZe5NLaO6QGj9n69764UT6pIC13tG1v19UNdUm7r9nAEM6hJNYbmNW1/T9HARaR8MDTdWq5VRo0Y1GAzsHhw8YcKEJl/317/+lQcffJCVK1cyevTotihVjqWJmVKeBfxau8bNkdzvfUjhpi2FBFl44ooRhAabWf1zAU98pt3ZRcT/Gd4tNX/+fJYtW8YLL7zA1q1bmTNnDuXl5cyaNQuAGTNmsGDBAs/5f/nLX7jnnntYvnw5PXv2JCcnh5ycHMrKyoz6CNLITCmoH3NzQtPA3TQd3DB9O0fx0IVDAVicvoPVP+cbXJGIyLEZHm6mT5/Oo48+yr333suIESPYsGEDK1eu9AwyzszMJDs723P+U089hc1m45JLLqFLly6e26OPPmrUR5BGZkqBF7uljnxvDSo2xCWjunHF2FScTrjplQ0cLKo0uiQRkSYFHf8U35s3bx7z5s1r9LlVq1Y1+Hnv3r2+L0hapsluKS+sceOmlhvDLZx2EpsOFLP5QAlzX85gxe8mYA0y/PcjEZGj6F8mOTH2Gijc7Xp8VLdU3WypMC+23BTuBnvtib+ftFhosIWnrhxFdGgQ6zOLeFjr34iIn1K4kRNzeC84aiE4AqK6Nniqfl8pL7TcRHeDoDBw1EDRvhN/P2mV1E7h/H36CACe/2Yv72zQtici4n8UbuTEuLuk4vuAueFfp/qtF7zQcmM2Q3zfhtcUQ0wZlMTcM1ybmd7++kZ+zCoytiARkV9QuJET08RgYqjvlor1RrcUaNyNH7n1rAGkDepMda2D2f/+gZxiLfAnIv5D4UZOzLHCTbmr5SYuwgvdUqBtGPyI2Wxi8eUj6Z8USV5pNb/7zw/aYFNE/IbCjZyYJmZKORxOzzo38V4LN9pA059EhgTxrxljiAsPZuP+Ym5/fSNOp1YwFhHjKdxI6zmdTS7gV1RZg3ulfq+33GiVYr/RPT6cf145iiCziXd/PMiSz7WCsYgYT+FGWq+8AKqKAJNrQPERDpVVAxAdGkSwxUt/zdwDiisOQfkh77ynnLAJfeK57/yTAHj04x28tX6/wRWJSEencCOt5261iU2F4LAGTx2qG2+TEBnivetZI1xTwkGtN37mqvE9mH2qawfx21/fyNc7CwyuSEQ6MoUbab2C7a77hAFHPVVYF246eatLyk0zpvzWgnMH8ethXaixO/l//1nHTwdLjC5JRDoohRtpvfy6lpvEo8PNIZ+FG+0x5a/MZhOPXTaccb06UVZdy6zn12oPKhExhMKNtJ6n5eboaeDuMTfxkWq56UhCgiw8M2M0/ZMiyS2pZubytZ4lAURE2orCjbTeMVpu3N1S8RFeHHMDmjHVDsSEBfP8rLEkRYfwc14ZM59bS2lVjdFliUgHonAjrVNdBiV1s2Iaa7nxdbdU4R6oVYuAv+oaG8aL142jU4SVjfuLue75H6i0aZE/EWkbCjfSOu6Wk/AECO901NOFZXUtN97ulorqAtZIcNrh8B7vvrd4Vb+kKP597ViiQoJYu7eQ//fiOqprFXBExPcUbqR1jtElBXCo3DXmxustNybTERtoqmvK3w1JieG5WWMIC7bw5Y58bvzvemrtDqPLEpEAp3AjrXOMwcTgw6ngR15TM6bahdE9O7FsxmisFjMfbcnl5hUbFHBExKcUbqR18uvCTSMtN659pVwDSL26iJ+b9phqd07pl8CSK08m2GLifxuzueG/66lRwBERH1G4kdZpYk8pgOLKGux1G0vFhfui5aauW0ozptqVswYnsfSqUVgtZj7cnMPclzKw1SrgiIj3KdxIy9lroHC363GjC/i5xttEhQZhDfLBX7Eju6W0C3W7MmVQEk/PGIU1yMzHP+Xyh5c0yFhEvE/hRlqucA84aiE4AqJTjnr6kHumlC/G2wB06gOYoKoYyvN9cw3xmTMGdOZfM0YTEmTm0615/O7f66iw1RpdlogEEIUbaTnPYOJ+rtlLv+Be4ybeF+NtAIJDIa6H63H+Nt9cQ3zqtP6JPHeNaxbVFzvyuepf31FUoXWLRMQ7FG6k5Y4xmBggr6QKgM5RPgo3AJ0H111M4aa9mtg3gRevH0dMWDAZmUVc9vQacoqrjC5LRAKAwo203DEGEwPklrrG3CRFh/quhs6DXPd5P/nuGuJzo3rE8drvJ5AUHcKO3DIufuobdueXGV2WiLRzCjfScu6uoCZbblzhJrFNWm4Ubtq7/klRvP77ifRKiOBAUSWXLF1DRuZho8sSkXZM4UZaxl5b3y3lDhi/kFfq6lrwbcuNO9xs1YypAJDaKZzXfj+BoSkxFJbbuOKZb/nfxoNGlyUi7ZTCjbRM4W6orYLgcIjr1egp7pYbn465ie8L5iCoLoGSA767jrSZhMgQXvndeNIGdaa61sG8l9ez5POdOBVeRaSFFG6kZfK2uO4TB4K58b8+uW3RchNkhfh+dTVt9d11pE1FhATx9NWjuXaSKzj/7aPt3P76Ri32JyItonAjLZNbN8Yl6aRGn66utVNUt/WCT1tuAJLquqZyt/j2OtKmLGYT904bzIMXnITZBK+t28+V//rW090pInI8CjfSMnnHDjf5dTOlrBYzseHBvq3FM2NKLTeB6OoJPXn2mjFEhQTx/d7DTHviK9bt00BjETk+hRtpGXcrSRODiXOPmCllamSBP6/SjKmAd8aAzrw9bxJ9O0eSW1LN5c+s4aXv9mkcjogck8KNNJ+tHA7vdT1usuXGPd7Gx11SUN9yk78dHNqfKFD1SYzk7bmTOHdIMjV2J3e9tZk739hEVY3+m4tI4xRupPnytgFOiOgMEQmNnpLrmSnlw8HEbrE9XbO27NX1G3lKQIoMCeKfV57M7ecMwGyCFT9kacE/EWmS4eFmyZIl9OzZk9DQUMaNG8fatWubPHfLli1cfPHF9OzZE5PJxOLFi9uuUIHcza77pMa7pODINW7aoOXGbHbN2gJ1TXUAJpOJP0zuy/OzxhIXHsyWgyX8+omveGPdfqNLExE/Y2i4WbFiBfPnz2fhwoVkZGQwfPhwpk6dSl5eXqPnV1RU0Lt3bx555BGSk5PbuFqpH0w8pMlTPC03vpwGfiR30MrZ3DbXE8Od1j+RD286jXG9OlFhs3Praz9yy4oNlFVrZ3ERcTE03Dz++OPMnj2bWbNmMXjwYJYuXUp4eDjLly9v9PwxY8bwt7/9jcsvv5yQkDZoGZCGjjOYGCCvtA0W8DtS8nDXfc7Gtrme+IXkmFBenj2e+Wf1x2yCt9Yf4Nf/WM3G/UVGlyYifsCwcGOz2Vi3bh1paWn1xZjNpKWlsWbNGq9dp7q6mpKSkgY3aQWnE7LrAkTy0CZPyy6qdJ0S00YtN13qwk32j21zPfEbFrOJG6f0Y8X/m0DXmFD2Hqrgon9+w+Mfb9eifyIdnGHhpqCgALvdTlJSUoPjSUlJ5OTkeO06ixYtIiYmxnNLTU312nt3KIW7oboYLCH1s5R+wel0sv+wK9x0iwtvm7qShwAmKM2G0ty2uab4lTE9O/HBTady3tAu2B1O/vHZTi5Y8jU/HdQvMiIdleEDin1twYIFFBcXe25ZWVlGl9Q+ZW9w3SedBJbGF+c7XFFDZd303K6xbdRyY42AhP6ux+qa6rBiw60sufJknvztSOLCg9maXcL5T37FP9J/psauVhyRjsawcJOQkIDFYiE3t+Fv27m5uV4dLBwSEkJ0dHSDm7TCwQ2u+64jmjxl/+EKwDXeJiTI4vua3NxdU+4apcP69bCufHzL6Zw9OIlah5PHP9nBhUu+1lgckQ7GsHBjtVoZNWoU6enpnmMOh4P09HQmTJhgVFnSFHfLTZcRTZ5S3yUV5vt6juQZd7Ohba8rfikxKoSnrx7F4ukjiAlzTRm/YMnXLHxnMyVVNUaXJyJtwNBuqfnz57Ns2TJeeOEFtm7dypw5cygvL2fWrFkAzJgxgwULFnjOt9lsbNiwgQ0bNmCz2Thw4AAbNmxg586dRn2EjsHprB+we4yWmwN14SalrcbbuHnCjbqlxMVkMnHhyBQ+nX86F47oitMJL6zZR9pjX/D+xmxt3yAS4IKMvPj06dPJz8/n3nvvJScnhxEjRrBy5UrPIOPMzEzM5vr8dfDgQUaOHOn5+dFHH+XRRx/l9NNPZ9WqVW1dfsdxeA9UFYPFComNDyaG+m6ptm+5Gea6L86EikII79S21xe/lRgVwuLLR3LJqFTueWczewrKmftyBqf3T+SeXw+mb+dIo0sUER8wNNwAzJs3j3nz5jX63C8DS8+ePfUblxHcY1mSToIga5OnHaibBp4S28bhJjQGOvV2zeg6kAH90o7/GulQTumXwIc3ncpTq3bx1KpdfLEjn68Xf8nVE3pw05R+xIY3/fdaRNqfgJ8tJV5wMMN1f4zxNmDgmBuAbmPqimh6+w7p2EKDLdxyVn8+uuU00ga5Bhw/9/VeJj+6in+v2UutZlWJBAyFGzm+rO9d96ljmzyl4Ro3BoQbd21ZCjdybL0SIvjXzNH857qx9E+KpKiihnvf2cI5/7ealZtz1DosEgAUbuTYaqvh4HrX49RxTZ5WUlnr2dsnJbaNBxQDdKsLNwfWgcPe9teXdufUfol8cOOpPHjhEOLCg9mZV8bvX1zHBUu+ZvXP+Qo5Iu2Ywo0cW/ZGsFdDeLxrXEsTsuoGE8dHWAmztuEaN26dB4M1EqpLIH9b219f2qUgi5mrx/dg1R/P4IYz+xJutbBxfzFXP7uWK5Z9y7p9hUaXKCKtoHAjx5b1res+dRyYTE2etrugHICeCRFtUdXRLEGQcrLrsbqmpIViwoK59ewBfHn7GVw7qRdWi5lvdxdy8VNrmPXcWoUckXZG4UaOLes71/0xuqQAduWVAdAn0aBwA/U1KtxIKyVEhnDvtMF8/sfJXD4mFYvZxOfb87n4qTVc/swadVeJtBMKN9I0p7M+KBwv3OS7wo2h64a4x924A5lIK6XEhvHIxcP4dP7pTB+dSrDFxLe7C7n62bVcuORrPtqSg8OhkCPirxRupGmHdkJZrmvxvmOsTAywK9/VLdUn0cBwkzoGMEHhLijJNq4OCRi9EiL4yyXD+OKPZzBrUk9Cg838uL+Y//efdaT9/Qv+8+0+Kmy1RpcpIr+gcCNN273KdZ86DoKbnt5tdzjZne/uljIw3ITF1W/FsOdL4+qQgNM1NoyF007iqzvOZO4ZfYgKCWJ3fjn3vL2Z8Q+n8/AHWz0rdIuI8RRupGl7vnDd9zr9mKcdLKqkutaB1WI2Zo2bI/Wuq9Vdu4gXJUSG8MepA1nzpyncN20wPePDKamq5Zkvd3PaXz9nzovr+GZXgcbliBhM4UYa57DDntWux70nH/PUnXWtNr0SIgiyGPxXyh3Edn/hGjMk4gORIUFcM6kXn906mWdnjuaUvgk4nPDh5hx+u+w7znh0FUu/2EVBWbXRpYp0SIbvLSV+KmcjVBVBSDR0HXnMUz0zpTobOFPKrfsE1xihkv2uvabi+xhdkQQws9nElEFJTBmUxI7cUl74Zi/vbDjI3kMVPPLhNh79aDtnn5TE5WO6M6lvAhZz08spiIj3KNxI43bXdev0mORaQ+YYdvnDeBs3a7hr1tS+r2D35wo30mb6J0Xx54uG8qdfDeL9jdm8vDaTDVlFfLAphw825ZAcHcoFI7py4cgUBnWJNrpckYCmbilp3I6Vrvs+Zx731C0HSwDXP+5+oW9dzTs+MrYO6ZAiQoK4bEwqb8+dxIc3ncrMCT2IDg0ip6SKp7/czbn/t5qpf/+Sp1bt4mBRpdHligQkk7ODjXwrKSkhJiaG4uJioqP121Ojygvg0X7gdMDNmyE2tclTq2vtDFn4ETV2J1/+8Qy6xxuwr9Qv5W2Ff44HSwjcvhtC/KBFSTq06lo7q7bn8/b6A6RvzcNWtwO5yQRjenbi3CHJnDMkmS4xBg/IF/FjLfn+VreUHG3HSlewSR52zGADsD2nlBq7k7jwYFI7+ck/zIkDIa4XHN4Du9Jh8AVGVyQdXEiQhaknJTP1pGSKK2r4cHM2b60/wHd7Cllbd7v/vZ8YkRrLOUOSOXdIMj3i/WAMm0g7pXAjR9v+oet+wK+Oe+qP+4sBGNotFtMx9p5qUyaTq/Zvl7g+i8KN+JGY8GAuH9udy8d250BRJR9uyuajLTn8sO8wG7KK2JBVxCMfbmNQl2jOGtSZyQM7M7xbrAYji7SAwo00ZCuHXZ+5Hg88frjZmFUEwLCUGB8W1QoD68LNjpVQa4Mgq9EViRwlJTaM60/tzfWn9iavpIqPfspl5eZsvt1dyNbsErZml/CPz3YSFx7M6f0TOWNgZ07rl0hchP4+ixyLwo00tO19qKmAuJ6ubqnj2FjXcjOsm5+Fm9TxEJnk2j5i56fNCmoiRuocHcrV43tw9fgeHC63kb4tj8+35/HljnwOV9Tw9oaDvL3hIGYTjEiN5bT+iUzqm8DwbrFYgzQ3RORICjfS0MYVrvthl7u6d46hwlbLz3mlAAxPjfVxYS1kCYKhl8KaJ+HHlxVupF2Ji7ByyahuXDKqGzV2Bxn7DvP59nxWbc9jW04pGZlFZGQWsfjTnwkLtjCmVycm9olnQu94hqTEqAtLOjyFG6lXmlvfJTXssuOe/sPewzic0DUmlKToUB8X1wrDr3CFm+0roaIQwjsZXZFIiwVbzIzrHc+43vHcee5ADhZVsmp7Pl/vKmDNrkMUltv4ckc+X+7IByAqNIhxvToxqkcnRvWIY1i3GEKDLQZ/CpG2pXAj9db/2zVLqtvYZi1+9/WuAgAm9k3wdWWtkzwEkoZC7ibY9DqM+53RFYmcsK6xYfx2XHd+O647DoeTHXmlfLPzEN/sOsR3uw9RWlXLp1vz+HRrHgDBFhMndY1hdI84RtXdOvvjLyMiXqRwIy72Gvh+uevxmOua9ZJvdh4CYGKfeF9VdeJOvho+vB3WPgNjrgezxiZI4DCbTQxMjmZgcjTXntKLWruDzQdL+H5PIev2HeaHfYcpKKv2zML611d7AOgWF8awbjEMSYlhWEosQ1KiiQ3XIGUJHAo34rLtf1B6ECIS4aSLjnt6bkkVmw64BhOf0s9PW24ARvwWPnsIDv3sGljc/2yjKxLxmSCLmRGpsYxIjWU24HQ6ySqsZF1mIT/sPcy6fYfZnlvK/sOV7D9cyQebcjyvTe0UVhd0YhiaEsOA5CgSo0KM+zAiJ0DhRsDhgC8fcz0eNQuCjv8P2mfbXE3ew1Nj6Rzlx03cIVFw8gzX2JuvF0O/s447UFokUJhMJrrHh9M9PpyLRnYDoLSqho37i9l0wHXbfKCYfYcqyCqsJKuwkvc3ZXteHx9hZUByFAOSoxiYHEX/JNctIkRfHeLf9DdUXK02uZvAGgXj5zTrJe9vdP0DePbgJF9W5h3j57i6pfZ97Row3XeK0RWJGCYqNJhJfROYdMRYueKKGjYfrA88Px0sYe+hcg6V2/hml2s8z5G6dwqnf1IkvRMj6Z0QQa+ECHonRpIQafWfxTylQ1O46ehqbfDZg67H4+c0a0ZRbkmVZzDxtGFdfVmdd8R0c423+fafkH4/9J4MZs0eEXGLCT868FTa7PycV8q2nFK2u2+5peSXVpNZWEFmYQXUDVp2iwoNondiJH2OCDzdO4WT2imMmLBgBR9pMwo3Hd2aJ6Fgh2uszYS5zXrJiu+zcDphVI84/9goszlOvRUy/gPZP8L3z2rmlMhxhFktDOsWy7BusQ2OHyqrZntuKbvyytiVX87ugnL2FJSx/3AlpVW1/JhVxI91K5cfKSo0iNQ4V9Bx3bsed+8UTre4cE1XF69SuOnI8rbCF391PT77zxAWe9yXVNfaeem7fQBcPb6HD4vzsogESFsIH9zmar3pf7ZrFWYRaZH4yBAmRoYwsU/DiQRVNXb2Hapgd34ZuwvK2Z3vCj2ZhZUUlFVTWlXLT9kl/JRd0vj7RlhJjgmlS0xo3X0YydH1PyfHhBJu1VeWNI/+pnRU1WXw2jVQWwl9pjRr0T6AV7/PIrekmqToEH41tItva/S20dfBptcg6zt4dSZc+xEE+/FgaJF2JDTY4hl8/EuVNjv7D1eQdbiCzEMVZB2uJKuw/r6supZD5TYOldvYcrDx8AMQExZMl5hQEqNCSIgMqbu3khB55M8hdIqwapXmDk7hpiOqtcGrV0P+Ntf+Sxc93awZREUVNv7+6c8AzD2jb/vbz8ZshoufhadPg+wN8Nbv4OLlrq0aRMRnwqwW+iVF0S/p6ODjdDopqqghu7iKnJJK131x1RH3rmMVNjvFlTUUV9awLaf0mNczm6BThNUTeOIjrMSGW4kNDybuiHvP4wgrEVaLxgQFEP2r3tG4W2x2fQbB4TD9JYhMPO7LnE4nd7+9mcJyG/06R3LF2O6+r9UXYlPh0ufgpUvhp3fAOQsuWgrWCKMrE+mQTCYTcRFW4iKsDO4a3eg5TqeT0upaT+jJL62moKyagtJq8svcj20UlFVTWGHD4YSCMhsFZbbjBiG3YIuJ2HArceHBnvuYsGAiQ4KJCg0iKjSI6FD34/pj7scaM+Rf/CLcLFmyhL/97W/k5OQwfPhwnnjiCcaOHdvk+a+99hr33HMPe/fupV+/fvzlL3/hV7/SxojHdWAdvDUHCrZDUBhM/w+kjjnuy5xOJ49/soP/bcwmyGzir5cMI9jSzlptjtR7Mlz6vKtrauu7cHgPXPgUJA81ujIRaYTJZCI6NJjo0GD6N9L6c6Rau4PCcltd6LGRX1pNYXk1hytqKKqooajCxuEKG0UVNRyusHG4ogZbrYMau5P80mryS6tbVaPVYm4QeCJDgogIsRBmDSLCaiHMaiHCGlR3byHcGkR4iIVw9+Mj7t3ntbvWcT9icjqdTiMLWLFiBTNmzGDp0qWMGzeOxYsX89prr7F9+3Y6d+581PnffPMNp512GosWLeLXv/41L7/8Mn/5y1/IyMhgyJAhx71eSUkJMTExFBcXEx3d+G8JASd3C3z9D9j0qmvvqMgkuPy/0G3UcV9aXFHDve9u5p0NBwF48MIh7Wsg8bHsWwMrroKKAjBZXFs1TJgHCf2MrkxE2ojT6aSyxl4XfhqGnpLKGkqraimt+uV9/eMyWy2++hYNtpgIDbIQEmwhJMhMaLCZkCALocFmQj3H6u/dj0Mafc71WmuQmWCLCavFTHDdzRpk8jwOtphdzwW5zrGYTX7TXdeS72/Dw824ceMYM2YMTz75JAAOh4PU1FRuuOEG7rzzzqPOnz59OuXl5fzvf//zHBs/fjwjRoxg6dKlx71ewIcbey2U7If87bDvG9iZ7lqgz23IJfCrvx1zPZuqGjtbs0v4aEsuL3+3j5KqWixmE/edf1LgBBu3koOw8k5XF5Vbl+HQbyqkjHK15kR10Z5UItIoh8NJma220RBUabNTYbNTYautu3c9LrfZqbTZKa+upbLGdX/k8zV2Q7+WGzCZqA88FtMRgegXP1vMBFlMBFnMBJtN9E2KZMG5g7xaS0u+vw3tlrLZbKxbt44FCxZ4jpnNZtLS0lizZk2jr1mzZg3z589vcGzq1Km8/fbbviz1+Iqy4IflgBNXjP/lPUcd35lXSnGFDRPu4wAOAExHnFv/vPsvvPu4A6u9klB7GSH2ckLtpcTYcrFgb1BarSmIbVET+aLz1ew3D8T54X6czv117+TE4XQtyX64ooaCsmr2HarA7qj/n2tAUhQPXHAS43r78QaZrRXdFS77tysIfv0P+Pkj11o42T/Wn2MOhugurrWArJGuLR2skWAJdi0GaA5ytfyYLWAy1983WzN/Kxo/ByKPbs0UEeOYzfVdZhDmlfe01Tpc4cdWS3Wtg6oaO1U1ds/jI++ra+xU1Tiorm38/pfn19jdN2ddd1zDn212R4NanE5XPbZaRxPVNq6ossYrfxatZWi4KSgowG63k5TUcAn/pKQktm3b1uhrcnJyGj0/Jyen0fOrq6uprq7vQy0paXqa4QkpzYavHm/RS/r6phKqnUFkOpPIcPTje+cAPrWfTFFlFOQBZDXrPeLCgxnfO54LRnTlrMHJgT+tssdE1628ALa9D5nfwsEM1wKHjhooynTdjDTsMoUbkQ7AGuRqGYkJD27zazudTmodTlfgqXVisx8ZgBzYap1HPHbUPe8KRrUOB7V2J7UOB50ijN101S8GFPvSokWLuP/++31/ocgkGDenbkq1qX5qdYOfG95vPlhCYXkNmEw4afp1DZ8z4TSZXL/rm0zUmMOpDorEFhRJtSWK0tAkyqydMZnMmEzQDZiFCZOpvn3AZKJBH6rJBFEhQcSGW+kUYaVPYiRJ0SF+08/apiISYNRM1w1c3Xyl2VByACoKwVYG1aWue3uNawyToxYcdnDa6+4dNL8TvgXNz2FxLf44IiItYTKZPN1NWI2upvUMDTcJCQlYLBZyc3MbHM/NzSU5ObnR1yQnJ7fo/AULFjToxiopKSE1NfUEK29EXA8495EWveT4w5/FcJYg1/TxWB/8nREREZ8wdJSk1Wpl1KhRpKene445HA7S09OZMGFCo6+ZMGFCg/MBPvnkkybPDwkJITo6usFNREREApfh3VLz589n5syZjB49mrFjx7J48WLKy8uZNWsWADNmzCAlJYVFixYBcNNNN3H66afz2GOPcd555/HKK6/www8/8Mwzzxj5MURERMRPGB5upk+fTn5+Pvfeey85OTmMGDGClStXegYNZ2ZmYj5iGu7EiRN5+eWXufvuu/nTn/5Ev379ePvtt5u1xo2IiIgEPsPXuWlrAb/OjYiISABqyfe3ViYTERGRgKJwIyIiIgFF4UZEREQCisKNiIiIBBSFGxEREQkoCjciIiISUBRuREREJKAo3IiIiEhAUbgRERGRgGL49gttzb0gc0lJicGViIiISHO5v7ebs7FChws3paWlAKSmphpciYiIiLRUaWkpMTExxzynw+0t5XA4OHjwIFFRUZhMJkNrKSkpITU1laysrA63z5U+e8f77B31c0PH/ewd9XODPrsvPrvT6aS0tJSuXbs22FC7MR2u5cZsNtOtWzejy2ggOjq6w/3ld9Nn73ifvaN+bui4n72jfm7QZ/f2Zz9ei42bBhSLiIhIQFG4ERERkYCicGOgkJAQFi5cSEhIiNGltDl99o732Tvq54aO+9k76ucGfXajP3uHG1AsIiIigU0tNyIiIhJQFG5EREQkoCjciIiISEBRuBEREZGAonDjR/785z8zceJEwsPDiY2NNbocn1myZAk9e/YkNDSUcePGsXbtWqNLahNffvkl06ZNo2vXrphMJt5++22jS2oTixYtYsyYMURFRdG5c2cuvPBCtm/fbnRZbeKpp55i2LBhnsXMJkyYwIcffmh0WW3ukUcewWQycfPNNxtdis/dd999mEymBreBAwcaXVabOHDgAFdddRXx8fGEhYUxdOhQfvjhB0NqUbjxIzabjUsvvZQ5c+YYXYrPrFixgvnz57Nw4UIyMjIYPnw4U6dOJS8vz+jSfK68vJzhw4ezZMkSo0tpU1988QVz587l22+/5ZNPPqGmpoazzz6b8vJyo0vzuW7duvHII4+wbt06fvjhB84880wuuOACtmzZYnRpbeb777/n6aefZtiwYUaX0mZOOukksrOzPbevvvrK6JJ87vDhw0yaNIng4GA+/PBDfvrpJx577DHi4uKMKcgpfue5555zxsTEGF2GT4wdO9Y5d+5cz892u93ZtWtX56JFiwysqu0BzrfeesvoMgyRl5fnBJxffPGF0aUYIi4uzvmvf/3L6DLaRGlpqbNfv37OTz75xHn66ac7b7rpJqNL8rmFCxc6hw8fbnQZbe6OO+5wnnLKKUaX4aGWG2kzNpuNdevWkZaW5jlmNptJS0tjzZo1BlYmbam4uBiATp06GVxJ27Lb7bzyyiuUl5czYcIEo8tpE3PnzuW8885r8P98R/Dzzz/TtWtXevfuzZVXXklmZqbRJfncu+++y+jRo7n00kvp3LkzI0eOZNmyZYbVo3AjbaagoAC73U5SUlKD40lJSeTk5BhUlbQlh8PBzTffzKRJkxgyZIjR5bSJTZs2ERkZSUhICL///e956623GDx4sNFl+dwrr7xCRkYGixYtMrqUNjVu3Dief/55Vq5cyVNPPcWePXs49dRTKS0tNbo0n9q9ezdPPfUU/fr146OPPmLOnDnceOONvPDCC4bU0+F2BW9rd955J3/5y1+Oec7WrVs7zIAz6djmzp3L5s2bO8QYBLcBAwawYcMGiouLef3115k5cyZffPFFQAecrKwsbrrpJj755BNCQ0ONLqdNnXvuuZ7Hw4YNY9y4cfTo0YNXX32V6667zsDKfMvhcDB69GgefvhhAEaOHMnmzZtZunQpM2fObPN6FG587NZbb+Waa6455jm9e/dum2IMlpCQgMViITc3t8Hx3NxckpOTDapK2sq8efP43//+x5dffkm3bt2MLqfNWK1W+vbtC8CoUaP4/vvv+b//+z+efvppgyvznXXr1pGXl8fJJ5/sOWa32/nyyy958sknqa6uxmKxGFhh24mNjaV///7s3LnT6FJ8qkuXLkcF9kGDBvHGG28YUo/CjY8lJiaSmJhodBl+wWq1MmrUKNLT07nwwgsBV9pPT09n3rx5xhYnPuN0Ornhhht46623WLVqFb169TK6JEM5HA6qq6uNLsOnpkyZwqZNmxocmzVrFgMHDuSOO+7oMMEGoKysjF27dnH11VcbXYpPTZo06aglHnbs2EGPHj0MqUfhxo9kZmZSWFhIZmYmdrudDRs2ANC3b18iIyONLc5L5s+fz8yZMxk9ejRjx45l8eLFlJeXM2vWLKNL87mysrIGv73t2bOHDRs20KlTJ7p3725gZb41d+5cXn75Zd555x2ioqI846tiYmIICwszuDrfWrBgAeeeey7du3entLSUl19+mVWrVvHRRx8ZXZpPRUVFHTWmKiIigvj4+IAfa3Xbbbcxbdo0evTowcGDB1m4cCEWi4UrrrjC6NJ86pZbbmHixIk8/PDDXHbZZaxdu5ZnnnmGZ555xpiCjJ6uJfVmzpzpBI66ff7550aX5lVPPPGEs3v37k6r1eocO3as89tvvzW6pDbx+eefN/rfd+bMmUaX5lONfWbA+dxzzxldms9de+21zh49ejitVqszMTHROWXKFOfHH39sdFmG6ChTwadPn+7s0qWL02q1OlNSUpzTp0937ty50+iy2sR7773nHDJkiDMkJMQ5cOBA5zPPPGNYLSan0+ls+0glIiIi4huaCi4iIiIBReFGREREAorCjYiIiAQUhRsREREJKAo3IiIiElAUbkRERCSgKNyIiIhIQFG4ERERkYCicCMiIiIBReFGRHxi8uTJ3HzzzUaX0WKHDh2ic+fO7N2794Tf6/LLL+exxx478aJEpEW0/YKItJjJZDrm8wsXLuTGG28kODiYqKioNqrq2G655Rb27dvHm2++eczz5s+fT2lpKcuWLeOjjz7inHPOOeb5H330EWeffXajz23evJnTTjuNPXv2EBMT0+raRaRltCu4iLRYdna25/GKFSu499572b59u+dYZGSk3+1kv3btWs4777xjnlNRUcGzzz7r2bX7tNNOa/BZhwwZwh/+8Af+8Ic/eI4lJiY2+X5DhgyhT58+vPjii8ydO/cEP4GINJe6pUSkxZKTkz23mJgYTCZTg2ORkZFHdUtNnjyZG264gZtvvpm4uDiSkpJYtmwZ5eXlzJo1i6ioKPr27cuHH37oeY3D4WDRokX06tWLsLAwhg8fzuuvv96iWm02G8HBwXzzzTfcddddmEwmxo8f3+i5H3zwASEhIZ7nw8LCPJ/Jbrdz6NAhTj311Aaf1WKxHPP606ZN45VXXmlRzSJyYhRuRKTNvPDCCyQkJLB27VpuuOEG5syZw6WXXsrEiRPJyMjg7LPP5uqrr6aiogKARYsW8e9//5ulS5eyZcsWbrnlFq666iq++OKLZl8zKCiIr7/+GoANGzaQnZ3NypUrGz139erVjBo1qtHn1q9fD8DJJ5/cko/M2LFjWbt2LdXV1S16nYi0nsKNiLSZ4cOHc/fdd9OvXz8WLFhAaGgoCQkJzJ49m379+nHvvfdy6NAhNm7cSHV1NQ8//DDLly9n6tSp9O7dm2uuuYarrrqKp59+utnXNJvNHDx4kPj4eIYPH05ycjKxsbGNnrtv3z66du3a6HMZGRmkpqYSHx/f4PhFF11EXFwcl1xySaOv69q1KzabjZycnGbXLCInRmNuRKTNDBs2zPPYYrEQHx/P0KFDPceSkpIAyMvLY+fOnVRUVHDWWWc1eA+bzcbIkSNbdN3169czfPjw455XWVlJaGhoo89lZGQ02mpz0003ce211/LCCy80+rqwsDAAT2uUiPiewo2ItJng4OAGP5tMpgbH3LOwHA4HZWVlALz//vukpKQ0eF1ISEiLrrthw4ZmhZuEhAQOHz7c6HMZGRlcf/31Rx2fPHkyq1atavI9CwsLgWMPPBYR71K4ERG/NHjwYEJCQsjMzOT0008/offatGkTF1988XHPGzlyJC+++OJRxwsKCsjKymrxeBtwTQfv1q0bCQkJLX6tiLSOwo2I+KWoqChuu+02brnlFhwOB6eccgrFxcV8/fXXREdHM3PmzGa/l8PhYPv27Rw8eJCIiIgm15yZOnUqCxYs4PDhw8TFxXmOZ2RkAC0fTAyuQcpNrYMjIr6hAcUi4rcefPBB7rnnHhYtWsSgQYM455xzeP/99+nVq5fnnOeff/64iwo+9NBDPP/886SkpPDQQw81ed7QoUM5+eSTefXVVxscX79+PUlJSU0ONm5KVVUVb7/9NrNnz27R60TkxGiFYhFp1xYuXMgXX3xxzHEvLfH+++/zxz/+kc2bN2M2N+/3v1WrVvHkk08etQbPU089xVtvvcXHH3/sldpEpHnULSUi7dqHH37Ik08+6bX3O++88/j55585cOAAqampxz0/LS2NH3/8kfLycrp168Zrr73GhAkTANcA6ieeeMJrtYlI86jlRkRERAKKxtyIiIhIQFG4ERERkYCicCMiIiIBReFGREREAorCjYiIiAQUhRsREREJKAo3IiIiElAUbkRERCSgKNyIiIhIQFG4ERERkYCicCMiIiIBReFGREREAsr/Bzy/slEr51tlAAAAAElFTkSuQmCC" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pulse = Pulse.gaussian({'width': 0.2, 'window': 6})\n", "source = Source.two_level(pulse=pulse)\n", "\n", "source.plot_lifetime(start = -1, end = 6, label='Lifetime')\n", "pulse.plot(scale=1/20).show() # We can scale down the pulse for better visualisation" ] }, { "cell_type": "markdown", "id": "65a09d02", "metadata": {}, "source": [ "ZPGenerator is designed to simulate sources of light that may rely on complicated pulse sequences. Building these pulse sequences can be done in an intuitive way using the Pulse class. We can simply initialise a Pulse object and then use the add() method to append new pulse objects to it. We can also add different kinds of pulses to the same sequence. Once we are done, we create a source that uses that pulse sequence." ] }, { "cell_type": "code", "execution_count": 53, "id": "e8c3d7bc", "metadata": { "ExecuteTime": { "end_time": "2024-02-09T08:46:13.945540Z", "start_time": "2024-02-09T08:46:13.758284Z" } }, "outputs": [ { "data": { "text/plain": "
", "image/png": 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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sequence = Pulse()\n", "sequence.add(Pulse.gaussian({'delay': 0}))\n", "sequence.add(Pulse.square({'delay': 1.1, 'width': 0.7, 'area': 2*np.pi}))\n", "sequence.add(Pulse.dirac({'delay': 2, 'area': np.pi/2}))\n", "\n", "source = Source.two_level(pulse=sequence)\n", "source.plot_lifetime(start = -1, end = 6, label='Lifetime')\n", "sequence.plot(scale=1/25).show() # notice that dirac pulses are not plotted!" ] }, { "cell_type": "markdown", "source": [ "## Custom shapes" ], "metadata": { "collapsed": false } }, { "cell_type": "markdown", "source": [ "In addition to the built-in Dirac, Gaussian, and square pulses, the Pulse class has the custom() class method to automate the construction of custom parameterised pulses. For example, we can make a quadratic pulse by giving it a function that takes two arguments t: float and args: dict. The interval for the pulse is specified using a list of two values or a function of args: dict that returns a list of two values. Finally, we must also specify any default parameters for the shape and gate, otherwise the initialisation will fail.\n", "\n", "To demonstrate, let's make a quadratic pulse function." ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 54, "outputs": [ { "data": { "text/plain": "
", "image/png": 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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "my_pulse = Pulse.custom(shape=lambda t, args: args['area'] * (1 - (t - args['delay'])**2),\n", " gate=lambda args: [args['delay'] - 1, args['delay'] + 1],\n", " parameters={'area': np.pi, 'delay': 0})\n", "my_pulse.plot(parameters={'delay': 1}, start=-1, end=3).show()" ], "metadata": { "collapsed": false, "ExecuteTime": { "end_time": "2024-02-09T08:46:14.030454Z", "start_time": "2024-02-09T08:46:13.962713Z" } } }, { "cell_type": "markdown", "source": [ "We can check to see that the shape has not been normalised correctly." ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 55, "outputs": [ { "data": { "text/plain": "1.3333333333333333" }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ "my_pulse.area(parameters={'area': 1})" ], "metadata": { "collapsed": false, "ExecuteTime": { "end_time": "2024-02-09T08:46:14.040064Z", "start_time": "2024-02-09T08:46:14.033012Z" } } }, { "cell_type": "markdown", "source": [ "Either we can ensure that the function we input is normalised, or we can use some options to automatically normalise the shape to a specified default value before building the pulse. Since we are scaling the function by the parameter 'area', we simply provide the default value for 'area' as the required norm." ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 56, "outputs": [ { "data": { "text/plain": "True" }, "execution_count": 56, "metadata": {}, "output_type": "execute_result" } ], "source": [ "my_pulse = Pulse.custom(shape=lambda t, args: args['area'] * (1 - (t - args['delay'])**2),\n", " gate=lambda args: [args['delay'] - 1, args['delay'] + 1],\n", " parameters={'area': np.pi, 'delay': 0},\n", " auto_normalise=True,\n", " norm=np.pi,\n", " name='Quadratic pulse')\n", "my_pulse.area({'area': 1}) == 1" ], "metadata": { "collapsed": false, "ExecuteTime": { "end_time": "2024-02-09T08:46:14.041693Z", "start_time": "2024-02-09T08:46:14.037839Z" } } }, { "cell_type": "code", "execution_count": 57, "outputs": [ { "data": { "text/plain": "
", "image/png": 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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "my_pulse.plot(start=-10, end=10)\n", "Pulse.square(name='Square pulse').plot(parameters={'width': 1, 'delay': -5})\n", "Pulse.gaussian(name='Gaussian pulse').plot(parameters={'width': 1, 'delay': 5}).show()" ], "metadata": { "collapsed": false, "ExecuteTime": { "end_time": "2024-02-09T08:46:14.165514Z", "start_time": "2024-02-09T08:46:14.070121Z" } } }, { "cell_type": "markdown", "source": [ "Once we have defined our custom pulse, we can create our source and look at the lifetime and figures of merit." ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 58, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number | Probability\n", "0 | 0.04115\n", "1 | 0.84480\n", "2 | 0.11136\n", "3 | 0.00267\n", "4 | 0.00002\n", "\n", "Figure of Merit | Value\n", "Brightness | 0.9588\n", "Average photon number | 1.0756\n", "Intensity correlation | 0.2066\n", "\n", "Figure of Merit | Value\n", "First order number coherence | 0.2602\n", "Second order number coherence | 0.0261\n", "Hong-Ou-Mandel visibility | 0.6446\n", "Mean wavepacket overlap | 0.8514\n", "\n" ] }, { "data": { "text/plain": "
", "image/png": 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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "source = Source.two_level(pulse=my_pulse)\n", "source.display_quality()\n", "source.plot_lifetime(end = 10, label='Lifetime')\n", "my_pulse.plot(scale=1/10).show()" ], "metadata": { "collapsed": false, "ExecuteTime": { "end_time": "2024-02-09T08:46:14.504569Z", "start_time": "2024-02-09T08:46:14.166650Z" } } }, { "cell_type": "markdown", "source": [ "Looks like the quadratic pulse gives some bad multi-photon emission! To mitigate this, we can narrow the pulse by adding a 'width' parameter to our custom pulse. In this case, we have to be careful to normalise by the width parameter so that the integrated area remains constant as a function of the width." ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 59, "outputs": [], "source": [ "my_pulse = Pulse.custom(shape=lambda t, args: args['area'] / args['width'] *\n", " (1 - (t / args['width'] - args['delay'])**2),\n", " gate=lambda args: [args['delay'] - args['width'], args['delay'] + args['width']],\n", " parameters={'area': np.pi, 'delay': 0, 'width': 1},\n", " auto_normalise=True,\n", " norm=np.pi,\n", " name='pulse')" ], "metadata": { "collapsed": false, "ExecuteTime": { "end_time": "2024-02-09T08:46:14.516271Z", "start_time": "2024-02-09T08:46:14.505655Z" } } }, { "cell_type": "code", "execution_count": 60, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number | Probability\n", "0 | 0.00220\n", "1 | 0.96501\n", "2 | 0.03262\n", "3 | 0.00017\n", "4 | 0.00000\n", "\n", "Figure of Merit | Value\n", "Brightness | 0.9978\n", "Average photon number | 1.0308\n", "Intensity correlation | 0.0624\n", "\n", "Figure of Merit | Value\n", "First order number coherence | 0.0434\n", "Second order number coherence | 0.0007\n", "Hong-Ou-Mandel visibility | 0.8792\n", "Mean wavepacket overlap | 0.9416\n", "\n" ] }, { "data": { "text/plain": "
", "image/png": 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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "my_pulse.update_default_parameters(parameters={'width': 0.2})\n", "source = Source.two_level(pulse=my_pulse)\n", "source.display_quality()\n", "source.plot_lifetime(end = 5, label='Lifetime')\n", "my_pulse.plot(scale=1/25).show()" ], "metadata": { "collapsed": false, "ExecuteTime": { "end_time": "2024-02-09T08:46:14.831745Z", "start_time": "2024-02-09T08:46:14.510127Z" } } }, { "cell_type": "markdown", "source": [], "metadata": { "collapsed": false } } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.6" } }, "nbformat": 4, "nbformat_minor": 5 }