notebook_5.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d48aa6f3",
   "metadata": {},
   "source": [
    "$\n",
    "\\newcommand{\\R}{\\mathbb{R}}\n",
    "\\newcommand{\\C}{\\mathbb{C}}\n",
    "\\newcommand{\\i}{\\mathrm{i}}\n",
    "%\n",
    "\\newcommand{\\HP}{\\mathcal{H}_P}\n",
    "\\newcommand{\\HM}{\\mathcal{H}_M}\n",
    "\\newcommand{\\UP}{\\mathcal{U}_P}\n",
    "\\newcommand{\\UM}{\\mathcal{U}_M}\n",
    "%\n",
    "\\newcommand{\\RX}{\\mathrm{RX}}\n",
    "\\newcommand{\\RZ}{\\mathrm{RZ}}\n",
    "\\newcommand{\\RZZ}{\\mathrm{RZZ}}\n",
    "\\newcommand{\\CX}{\\mathrm{CX}}\n",
    "\\newcommand{\\CNOT}{\\mathrm{CNOT}}\n",
    "%\n",
    "\\newcommand{\\ket}[1]{\\left\\vert #1 \\right\\rangle}\n",
    "\\newcommand{\\bra}[1]{\\left\\langle #1 \\right\\vert}\n",
    "\\newcommand{\\braket}[1]{\\left\\langle #1 \\right\\rangle}\n",
    "\\newcommand{\\pmat}[1]{\\begin{pmatrix}#1\\end{pmatrix}}\n",
    "$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "important-helping",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "***This notebook is part of the project [SEQUOIA](https://websites.fraunhofer.de/sequoia/) of the [Competence Center Quantum Computing Baden-Württemberg](https://www.iaf.fraunhofer.de/en/networkers/KQC.html).***\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9854cd14",
   "metadata": {},
   "source": [
    "<img src=\"images/sequoia_logo.png\" />\n",
    "\n",
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "acute-firewall",
   "metadata": {},
   "source": [
    "# SEQUOIA DEMONSTRATOR  <a class=\"tocSkip\"/>\n",
    "\n",
    "## Energy Use Case: Optimization of Charging Schedules for Electric Cars  <a class=\"tocSkip\"/>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "670908af",
   "metadata": {},
   "source": [
    "Authors: Andreas Sturm, Niclas Schillo"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "011036ea",
   "metadata": {},
   "source": [
    "---\n",
    "# Bonus Notebook: Improving QAOA with CVaR <a class=\"tocSkip\">"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a669c07",
   "metadata": {},
   "source": [
    "In Notebook 2 we saw that the variational parameters $\\vec{\\beta}$ and $\\vec{\\gamma}$ of QAOA are determined with a classical optimizer such that they minimize the expectation value $e(\\vec{\\beta}, \\vec{\\gamma})$:\n",
    "\n",
    "$$\n",
    "(\\vec{\\beta}^\\ast, \\vec{\\gamma}^\\ast) = \\mathrm{argmin}_{\\vec{\\beta}, \\vec{\\gamma}} e(\\vec{\\beta}, \\vec{\\gamma}) \\ ,\n",
    "\\qquad\n",
    "e(\\vec{\\beta}, \\vec{\\gamma})\n",
    "=\n",
    "\\langle \\psi_\\mathrm{QAOA}(\\vec{\\beta}, \\vec{\\gamma}) | \\HP |\\psi_\\mathrm{QAOA}(\\vec{\\beta}, \\vec{\\gamma}) \\rangle\n",
    "\\ .\n",
    "$$\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6758171",
   "metadata": {},
   "source": [
    "Then, in Notebook 3 we saw that this (usually) does not yield a quantum state \n",
    "\n",
    "$$\n",
    "|\\psi_\\mathrm{QAOA}(\\vec{\\beta}^\\ast, \\vec{\\gamma}^\\ast) \\rangle\n",
    "= \\sum_{\\vec{b} \\in \\{0, 1\\}^n}\n",
    "\\lambda_{b} |b\\rangle \\ ,\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "284e65b1",
   "metadata": {},
   "source": [
    "where the probability $|\\lambda_{b^\\ast}|^2$ of the exact solution bit string $b^\\ast$ is the highest among all probabilities in $|\\psi_\\mathrm{QAOA}(\\vec{\\beta}^\\ast, \\vec{\\gamma}^\\ast) \\rangle$:\n",
    "\n",
    "$$\n",
    "|\\lambda_{b^\\ast}|^2 \\not = \\max_{\\vec{b} \\in \\{0, 1\\}^n} |\\lambda_{b}|^2,\n",
    "\\qquad\n",
    "\\lambda_{b^\\ast} = \\mathrm{argmin}_{\\vec{b} \\in \\{0, 1\\}^n} f_3(\\vec{b}) \\ .\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "971176b5",
   "metadata": {},
   "source": [
    "If one is interested in this exact solution (and not an approximate solution) it can be beneficial to use the **Conditional Value at Risk (CVaR) expectation value** instead of the usual expectation value."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f66cd758",
   "metadata": {},
   "source": [
    "In this notebook we present how the CVaR expecation can be integrated in our codes and present some results comparing standard and CVaR QAOA."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7545f30e",
   "metadata": {},
   "source": [
    "Remark: CVaR QAOA was proposed in [1]."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da6e0c52",
   "metadata": {},
   "source": [
    "# Example for this Notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7da86995",
   "metadata": {},
   "source": [
    "For this notebook we use the same example as in Notebook 2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ac829103",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ[\"QISKIT_SETTINGS\"] = \"qiskitrc\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0d888743",
   "metadata": {},
   "outputs": [],
   "source": [
    "from codes_notebook_1 import generate_example\n",
    "charging_unit, car_green, qcio, converter, qubo, number_binary_variables, qubo_minimization_result = generate_example()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a1d4f9df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "minimum value: f_3(b_min) =  5.999999999999993\n"
     ]
    }
   ],
   "source": [
    "f_3_min = qubo_minimization_result.fval\n",
    "print(\"minimum value: f_3(b_min) = \", f_3_min)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "529dc395",
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit.circuit.library import QAOAAnsatz\n",
    "\n",
    "ising, ising_offset = qubo.to_ising()\n",
    "\n",
    "qaoa_reps = 2\n",
    "qaoa_ansatz = QAOAAnsatz(cost_operator=ising, reps=qaoa_reps, name='qaoa')\n",
    "qaoa_ansatz.measure_active()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0c48fc61",
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit_aer import AerSimulator\n",
    "\n",
    "number_of_shots = 10000\n",
    "aer_simulator = AerSimulator(method=\"statevector\", shots=number_of_shots)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77f7bb38",
   "metadata": {},
   "source": [
    "## Standard Expectation Value"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d377873",
   "metadata": {},
   "source": [
    "We already presented in Notebook 2 how the standard expectation function can be implemented (and also commented on the naming \"energy_evaluation\"). We repeat it here so that we can later compare it with CVaR."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c5431258",
   "metadata": {},
   "outputs": [],
   "source": [
    "from typing import List\n",
    "import numpy as np\n",
    "from qiskit.opflow import OperatorStateFn\n",
    "\n",
    "observable = OperatorStateFn(ising, is_measurement=True)\n",
    "\n",
    "def energy_evaluation(parameter_values: List[float]):\n",
    "    \"parameter_values is expected to be of the form [beta_0, beta_1, ..., gamma_0, gamma_1, ...]\"\n",
    "    parameter_bindings = dict(zip(qaoa_ansatz.parameters, parameter_values))\n",
    "\n",
    "    qaoa_with_parameters = qaoa_ansatz.bind_parameters(parameter_bindings)\n",
    "    qaoa_with_parameters_decomposed = qaoa_with_parameters.decompose(reps=3)\n",
    "\n",
    "    result = aer_simulator.run(qaoa_with_parameters_decomposed).result()\n",
    "    counts = result.get_counts()\n",
    "    sqrt_probabilities = {bitstring: np.sqrt(count/number_of_shots) for bitstring, count in counts.items()}\n",
    "    \n",
    "    expectation = np.real(observable.eval(sqrt_probabilities)) + ising_offset\n",
    "    \n",
    "    return expectation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e5439b93",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Standard expectation value e([1.23, 2.31, 3.21, 4.32]) = 38.81870000000001\n"
     ]
    }
   ],
   "source": [
    "print(f\"Standard expectation value e([1.23, 2.31, 3.21, 4.32]) = {energy_evaluation([1.23, 2.31, 3.21, 4.32])}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b407af98",
   "metadata": {},
   "source": [
    "# Conditional Value at Risk (CVaR) Expectation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93c9af9e",
   "metadata": {},
   "source": [
    "In many optimization problems the smallest possible value of the cost function is of central intereset. Therefore, it seems reasonable to use the best observed sample as the cost function in QAOA. However, this leads to an unsmooth optimization surface and hence to problems in the classical optimization. As a remedy the optimization surface can be smoothed by not only using the best observed sample but instead averaging over a subset of the best observed samples. Exactly this is the basis of the CVaR method, and using it should lead to faster convergence of the classical optimizer and to better QAOA solutions.\n",
    "\n",
    "In order to specify the fraction of the best-observed samples, which are used to compute the objective function, the real number $\\alpha \\in [0, 1]$ is introduced. If $\\alpha = 1$, CVaR is equivalent to a standard expectation value. If $\\alpha = 0$, only the best-observed sample is used."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2ce67ba",
   "metadata": {},
   "source": [
    "In the next code cells we implement the CVaR expectation with qiskit's class `CVaRExpectation`, see [link](https://qiskit.org/documentation/stubs/qiskit.opflow.expectations.CVaRExpectation.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "51e82e08",
   "metadata": {},
   "outputs": [],
   "source": [
    "cvar_alpha = 0.08"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5c02c086",
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit.opflow import CircuitStateFn, DictStateFn\n",
    "from qiskit.opflow.expectations import CVaRExpectation\n",
    "\n",
    "qaoa_ansatz_wo_measurement = qaoa_ansatz.copy()\n",
    "qaoa_ansatz_wo_measurement.remove_final_measurements()\n",
    "\n",
    "expectation_operator = observable @ CircuitStateFn(qaoa_ansatz_wo_measurement)\n",
    "\n",
    "cvar_expectation_converter = CVaRExpectation(alpha=cvar_alpha)\n",
    "expectation_operator_cvar = cvar_expectation_converter.convert(expectation_operator)\n",
    "measurement_cvar = expectation_operator_cvar[0]\n",
    "\n",
    "def energy_evaluation_cvar(parameter_values: List[float]):\n",
    "    \"parameter_values is expected to be of the form [beta_0, beta_1, ..., gamma_0, gamma_1, ...]\"\n",
    "    parameter_bindings = dict(zip(qaoa_ansatz.parameters, parameter_values))\n",
    "\n",
    "    qaoa_with_parameters = qaoa_ansatz.bind_parameters(parameter_bindings)\n",
    "    qaoa_with_parameters_decomposed = qaoa_with_parameters.decompose(reps=3)\n",
    "\n",
    "    result = aer_simulator.run(qaoa_with_parameters_decomposed).result()\n",
    "    counts = result.get_counts()\n",
    "    sqrt_probabilities = {bitstring: np.sqrt(count/number_of_shots) for bitstring, count in counts.items()}\n",
    "    sqrt_probabilities_dict_state = DictStateFn(sqrt_probabilities)\n",
    "    \n",
    "    expectation = np.real(measurement_cvar.eval(sqrt_probabilities_dict_state)) + ising_offset\n",
    "    \n",
    "    return expectation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e8a7e6b4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CVaR expectation value e_cvar([1.23, 2.31, 3.21, 4.32]) = 9.024500000000003\n"
     ]
    }
   ],
   "source": [
    "print(f\"CVaR expectation value e_cvar([1.23, 2.31, 3.21, 4.32]) = {energy_evaluation_cvar([1.23, 2.31, 3.21, 4.32])}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d11be276",
   "metadata": {},
   "source": [
    "Next, we use a classical optimizer to determine the variational parameters $\\vec{\\beta}$ and $\\vec{\\gamma}$. This follows the same steps as in Notebook 2."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6add9b4",
   "metadata": {},
   "source": [
    "# Classical Optimization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "447b2816",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Run this cell only once if you want changing random numbers in the cell below\n",
    "seed = 199\n",
    "random_generator = np.random.default_rng(seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "28a0564c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit.algorithms.optimizers import COBYLA\n",
    "\n",
    "cobyla_optimizer = COBYLA()\n",
    "\n",
    "betas_initial_guess = np.pi*random_generator.random(qaoa_reps)\n",
    "gammas_initial_guess = 2*np.pi*random_generator.random(qaoa_reps)\n",
    "parameter_values_initial_guess = [*betas_initial_guess, *gammas_initial_guess]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "52122404",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Standard:\n",
      "  optimized parameters: [2.10537365 2.9363235  0.45141433 1.85876465]\n",
      "  expectation value: 18.86292000000001\n",
      "  number function evaluations: 47\n"
     ]
    }
   ],
   "source": [
    "result_optimization = cobyla_optimizer.minimize(fun=energy_evaluation, x0=parameter_values_initial_guess)\n",
    "\n",
    "parameter_values_optimized = result_optimization.x\n",
    "energy_optimized = result_optimization.fun\n",
    "number_function_evaluations = result_optimization.nfev # Number of evalutions of energy_evaluation\n",
    "\n",
    "print(\"Standard:\")\n",
    "print(\"  optimized parameters:\", parameter_values_optimized)\n",
    "print(\"  expectation value:\", energy_optimized)\n",
    "print(\"  number function evaluations:\", number_function_evaluations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c4645b6e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CVaR, alpha=0.08:\n",
      "  optimized parameters: [1.96361184 2.81734039 0.32297973 1.9298287 ]\n",
      "  cvar expectation value: 6.000000000000011\n",
      "  number function evaluations: 30\n"
     ]
    }
   ],
   "source": [
    "result_optimization_cvar = cobyla_optimizer.minimize(fun=energy_evaluation_cvar, x0=parameter_values_initial_guess)\n",
    "\n",
    "parameter_values_optimized_cvar = result_optimization_cvar.x\n",
    "energy_optimized_cvar = result_optimization_cvar.fun\n",
    "number_function_evaluations_cvar = result_optimization_cvar.nfev # Number of evalutions of energy_evaluation_cvar\n",
    "\n",
    "print(f\"CVaR, alpha={cvar_alpha}:\")\n",
    "print(\"  optimized parameters:\", parameter_values_optimized_cvar)\n",
    "print(\"  cvar expectation value:\", energy_optimized_cvar)\n",
    "print(\"  number function evaluations:\", number_function_evaluations_cvar)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebc320eb",
   "metadata": {},
   "source": [
    "# Postprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eecd2ff5",
   "metadata": {},
   "source": [
    "As in Notebook 3 we collect our result in a pandas dataframe, postprocess it and then visualize it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ab32f310",
   "metadata": {},
   "outputs": [],
   "source": [
    "parameter_bindings = dict(zip(qaoa_ansatz.parameters, parameter_values_optimized))\n",
    "\n",
    "qaoa_with_parameters = qaoa_ansatz.bind_parameters(parameter_bindings)\n",
    "qaoa_with_parameters_decomposed = qaoa_with_parameters.decompose(reps=3)\n",
    "\n",
    "result = aer_simulator.run(qaoa_with_parameters_decomposed).result()\n",
    "counts = result.get_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c935abd1",
   "metadata": {},
   "outputs": [],
   "source": [
    "parameter_bindings = dict(zip(qaoa_ansatz.parameters, parameter_values_optimized_cvar))\n",
    "\n",
    "qaoa_with_parameters = qaoa_ansatz.bind_parameters(parameter_bindings)\n",
    "qaoa_with_parameters_decomposed = qaoa_with_parameters.decompose(reps=3)\n",
    "\n",
    "result = aer_simulator.run(qaoa_with_parameters_decomposed).result()\n",
    "counts_cvar = result.get_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7c21f671",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bit_string</th>\n",
       "      <th>count</th>\n",
       "      <th>probability</th>\n",
       "      <th>count_cvar</th>\n",
       "      <th>probability_cvar</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>00001010</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>83.0</td>\n",
       "      <td>0.0083</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11101000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.0018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>00111101</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0004</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0014</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11101111</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0.0051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>00101000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0007</td>\n",
       "      <td>86.0</td>\n",
       "      <td>0.0086</td>\n",
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       "      <th>...</th>\n",
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       "      <th>251</th>\n",
       "      <td>01101110</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>252</th>\n",
       "      <td>00101011</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>53.0</td>\n",
       "      <td>0.0053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>253</th>\n",
       "      <td>10111010</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>254</th>\n",
       "      <td>00101110</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.0041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255</th>\n",
       "      <td>01001010</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0012</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>256 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    bit_string  count  probability  count_cvar  probability_cvar\n",
       "0     00001010    2.0       0.0002        83.0            0.0083\n",
       "1     11101000    1.0       0.0001        18.0            0.0018\n",
       "2     00111101    4.0       0.0004        14.0            0.0014\n",
       "3     11101111    2.0       0.0002        51.0            0.0051\n",
       "4     00101000    7.0       0.0007        86.0            0.0086\n",
       "..         ...    ...          ...         ...               ...\n",
       "251   01101110    0.0       0.0000        12.0            0.0012\n",
       "252   00101011    0.0       0.0000        53.0            0.0053\n",
       "253   10111010    0.0       0.0000        17.0            0.0017\n",
       "254   00101110    0.0       0.0000        41.0            0.0041\n",
       "255   01001010    0.0       0.0000        12.0            0.0012\n",
       "\n",
       "[256 rows x 5 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.DataFrame.from_dict(data={\n",
    "    \"bit_string\": counts.keys(),\n",
    "    \"count\": counts.values()})\n",
    "df[\"probability\"] = df[\"count\"]/number_of_shots\n",
    "\n",
    "df_cvar = pd.DataFrame.from_dict(data={\n",
    "    \"bit_string\": counts_cvar.keys(),\n",
    "    \"count_cvar\": counts_cvar.values()})\n",
    "df_cvar[\"probability_cvar\"] = df_cvar[\"count_cvar\"]/number_of_shots\n",
    "\n",
    "df = df.merge(df_cvar, how=\"outer\", on=\"bit_string\")\n",
    "df.fillna(0.0, inplace=True)\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9b699ffd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bit_string</th>\n",
       "      <th>bit_array</th>\n",
       "      <th>integer_array</th>\n",
       "      <th>count</th>\n",
       "      <th>probability</th>\n",
       "      <th>probability_cvar</th>\n",
       "      <th>cost</th>\n",
       "      <th>is_feasible</th>\n",
       "      <th>is_optimal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>00011001</td>\n",
       "      <td>[1, 0, 0, 1, 1, 0, 0, 0]</td>\n",
       "      <td>[1.0, 2.0, 1.0, 0.0]</td>\n",
       "      <td>156.0</td>\n",
       "      <td>0.0156</td>\n",
       "      <td>0.0321</td>\n",
       "      <td>6.0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>00010110</td>\n",
       "      <td>[0, 1, 1, 0, 1, 0, 0, 0]</td>\n",
       "      <td>[2.0, 1.0, 1.0, 0.0]</td>\n",
       "      <td>132.0</td>\n",
       "      <td>0.0132</td>\n",
       "      <td>0.0344</td>\n",
       "      <td>6.0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>00100101</td>\n",
       "      <td>[1, 0, 1, 0, 0, 1, 0, 0]</td>\n",
       "      <td>[1.0, 1.0, 2.0, 0.0]</td>\n",
       "      <td>136.0</td>\n",
       "      <td>0.0136</td>\n",
       "      <td>0.0343</td>\n",
       "      <td>6.0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>00010101</td>\n",
       "      <td>[1, 0, 1, 0, 1, 0, 0, 0]</td>\n",
       "      <td>[1.0, 1.0, 1.0, 0.0]</td>\n",
       "      <td>259.0</td>\n",
       "      <td>0.0259</td>\n",
       "      <td>0.0189</td>\n",
       "      <td>6.6</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>01010110</td>\n",
       "      <td>[0, 1, 1, 0, 1, 0, 1, 0]</td>\n",
       "      <td>[2.0, 1.0, 1.0, 1.0]</td>\n",
       "      <td>83.0</td>\n",
       "      <td>0.0083</td>\n",
       "      <td>0.0066</td>\n",
       "      <td>7.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>11101111</td>\n",
       "      <td>[1, 1, 1, 1, 0, 1, 1, 1]</td>\n",
       "      <td>[3.0, 3.0, 2.0, 3.0]</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0051</td>\n",
       "      <td>88.6</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>252</th>\n",
       "      <td>00111111</td>\n",
       "      <td>[1, 1, 1, 1, 1, 1, 0, 0]</td>\n",
       "      <td>[3.0, 3.0, 3.0, 0.0]</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0009</td>\n",
       "      <td>0.0172</td>\n",
       "      <td>117.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>253</th>\n",
       "      <td>01111111</td>\n",
       "      <td>[1, 1, 1, 1, 1, 1, 1, 0]</td>\n",
       "      <td>[3.0, 3.0, 3.0, 1.0]</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.0035</td>\n",
       "      <td>118.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>254</th>\n",
       "      <td>10111111</td>\n",
       "      <td>[1, 1, 1, 1, 1, 1, 0, 1]</td>\n",
       "      <td>[3.0, 3.0, 3.0, 2.0]</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0062</td>\n",
       "      <td>121.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255</th>\n",
       "      <td>11111111</td>\n",
       "      <td>[1, 1, 1, 1, 1, 1, 1, 1]</td>\n",
       "      <td>[3.0, 3.0, 3.0, 3.0]</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>0.0042</td>\n",
       "      <td>126.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>256 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    bit_string                 bit_array         integer_array  count  \\\n",
       "0     00011001  [1, 0, 0, 1, 1, 0, 0, 0]  [1.0, 2.0, 1.0, 0.0]  156.0   \n",
       "1     00010110  [0, 1, 1, 0, 1, 0, 0, 0]  [2.0, 1.0, 1.0, 0.0]  132.0   \n",
       "2     00100101  [1, 0, 1, 0, 0, 1, 0, 0]  [1.0, 1.0, 2.0, 0.0]  136.0   \n",
       "3     00010101  [1, 0, 1, 0, 1, 0, 0, 0]  [1.0, 1.0, 1.0, 0.0]  259.0   \n",
       "4     01010110  [0, 1, 1, 0, 1, 0, 1, 0]  [2.0, 1.0, 1.0, 1.0]   83.0   \n",
       "..         ...                       ...                   ...    ...   \n",
       "251   11101111  [1, 1, 1, 1, 0, 1, 1, 1]  [3.0, 3.0, 2.0, 3.0]    2.0   \n",
       "252   00111111  [1, 1, 1, 1, 1, 1, 0, 0]  [3.0, 3.0, 3.0, 0.0]    9.0   \n",
       "253   01111111  [1, 1, 1, 1, 1, 1, 1, 0]  [3.0, 3.0, 3.0, 1.0]    1.0   \n",
       "254   10111111  [1, 1, 1, 1, 1, 1, 0, 1]  [3.0, 3.0, 3.0, 2.0]    2.0   \n",
       "255   11111111  [1, 1, 1, 1, 1, 1, 1, 1]  [3.0, 3.0, 3.0, 3.0]    3.0   \n",
       "\n",
       "     probability  probability_cvar   cost  is_feasible  is_optimal  \n",
       "0         0.0156            0.0321    6.0         True        True  \n",
       "1         0.0132            0.0344    6.0         True        True  \n",
       "2         0.0136            0.0343    6.0         True        True  \n",
       "3         0.0259            0.0189    6.6        False       False  \n",
       "4         0.0083            0.0066    7.0         True       False  \n",
       "..           ...               ...    ...          ...         ...  \n",
       "251       0.0002            0.0051   88.6        False       False  \n",
       "252       0.0009            0.0172  117.0        False       False  \n",
       "253       0.0001            0.0035  118.0        False       False  \n",
       "254       0.0002            0.0062  121.0        False       False  \n",
       "255       0.0003            0.0042  126.0        False       False  \n",
       "\n",
       "[256 rows x 9 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"bit_array\"] = df[\"bit_string\"].apply(\n",
    "    lambda bitstring: np.fromiter(bitstring, dtype=int)[::-1])\n",
    "\n",
    "df[\"integer_array\"] = df[\"bit_array\"].apply(\n",
    "    converter.interpret)\n",
    "\n",
    "df = df[\n",
    "    [\"bit_string\", \"bit_array\",\"integer_array\", \"count\", \"probability\", \"probability_cvar\"]]\n",
    "\n",
    "df[\"cost\"] = df[\"bit_array\"].apply(\n",
    "    qubo.objective.evaluate)\n",
    "\n",
    "df[\"is_feasible\"] = df[\"integer_array\"].apply(\n",
    "    qcio.is_feasible)\n",
    "\n",
    "df[\"is_optimal\"] = df[\"cost\"].apply(lambda x: np.isclose(x, f_3_min))\n",
    "\n",
    "df = df.sort_values(\"cost\", ascending=True)\n",
    "\n",
    "df.reset_index(inplace=True, drop=True)\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "46e7253a",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 6000x3600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import scienceplots\n",
    "plt.style.use(['science','ieee'])\n",
    "# Set the default figure size and font size\n",
    "plt.rcParams['figure.figsize'] = 10, 6\n",
    "plt.rcParams['font.size'] = 14\n",
    "plt.rcParams['text.usetex'] = True\n",
    "plt.rcParams['axes.prop_cycle'] = plt.cycler(color=[BLUE,RED])\n",
    "\n",
    "        \n",
    "df_plot = df.iloc[0:40, :]\n",
    "\n",
    "#use IEEE Colors\n",
    "BLUE = \"#0072BD\"\n",
    "RED = \"#D95319\"\n",
    "GREEN = \"#77AC30\"\n",
    "\n",
    "fig, ax =  plt.subplots()\n",
    "\n",
    "\n",
    "ax.bar(df_plot.index, df_plot[\"probability_cvar\"], color= RED, label=f\"CVaR, $\\\\alpha$={cvar_alpha}\")\n",
    "\n",
    "ax.set_xlabel(\"Bitstring\")\n",
    "ax.set_ylabel(\"Probability\")\n",
    "ax.set_xticks(df_plot.index)\n",
    "ax.set_xticklabels(df_plot[\"bit_string\"], rotation=90)\n",
    "ax.legend()\n",
    "# add scatter markers above x labels to indicate the optimal solution and feasible solutions\n",
    "ax.scatter(df_plot[df_plot[\"is_optimal\"]].index, df_plot[df_plot[\"is_optimal\"]][\"probability_cvar\"], marker=\"*\", color=\"black\", s=100, label=\"optimal solution\")\n",
    "ax.scatter(df_plot[df_plot[\"is_feasible\"]].index, df_plot[df_plot[\"is_feasible\"]][\"probability_cvar\"], marker=\"o\",alpha=0.5 ,color=\"black\", s=100, label=\"feasible solution\")\n",
    "#remove overllaping markers\n",
    "\n",
    "ax.set_ylim(bottom=-0.001)\n",
    "ax.legend()\n",
    "\n",
    "\n",
    "#move the x axis a little bit up\n",
    "ax.set_ylim(bottom=-0.001)\n",
    "   \n",
    "ax.legend()\n",
    "plt.tight_layout()\n",
    "    \n",
    "            \n",
    "    \n",
    "    \n",
    "\n",
    "\n",
    "                \n",
    "\n",
    "\n",
    "\n",
    "plt.savefig(\"qaoa_cvar.pdf\", bbox_inches='tight')\n",
    "plt.show()\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "eeb5eab8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
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         "name": "cvar",
         "type": "bar",
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         "y": [
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        {
         "line": {
          "color": "black",
          "width": 2
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         "mode": "lines+markers",
         "name": "cost",
         "type": "scatter",
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         "xaxis": "x",
         "y": [
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    "fig.add_trace(go.Bar(\n",
    "    x=df_plot[\"bit_string\"],\n",
    "    y=df_plot[\"probability\"],\n",
    "    name=\"standard\",\n",
    "    marker_color=RED\n",
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    "#fig.add_trace(go.Bar(\n",
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    "fig.add_trace(go.Scatter(\n",
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    "    x=df_plot[\"bit_string\"][df_plot[\"is_feasible\"]==True],\n",
    "    y=[-0.001]*(len(df_plot[\"is_feasible\"]==True)),\n",
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    "    marker_size=10,\n",
    "    name=\"feasible bitstring\",\n",
    "    opacity=0.7\n",
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    "fig.update_xaxes(\n",
    "    title=r'$x$')\n",
    "fig.update_yaxes(\n",
    "    title=\"probability\")\n",
    "fig.update_layout(\n",
    "    title=\"Best 50 bit strings w.r.t. to cost function (sorted by cost, increasing from left to right)\",\n",
    "    title_font_size=13\n",
    ")\n",
    "\n",
    "fig.show()\n",
    "fig.write_image(\"qaoa_cvar.pdf\")"
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   "cell_type": "code",
   "execution_count": 42,
   "id": "e3708f36",
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       "                        })                };                });            </script>        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from plotly.subplots import make_subplots\n",
    "\n",
    "fig = make_subplots(specs=[[{\"secondary_y\": True}]])\n",
    "\n",
    "fig.add_trace(go.Bar(\n",
    "    x=df_plot[\"bit_string\"],\n",
    "    y=df_plot[\"probability\"],\n",
    "    name=\"standard\"\n",
    "))\n",
    "fig.add_trace(go.Bar(\n",
    "    x=df_plot[\"bit_string\"],\n",
    "    y=df_plot[\"probability_cvar\"],\n",
    "    name=\"cvar\"\n",
    "))\n",
    "\n",
    "fig.add_trace(go.Scatter(\n",
    "    x=df_plot[\"bit_string\"],\n",
    "    y=df_plot[\"cost\"],\n",
    "    mode=\"lines+markers\",\n",
    "    line_color=\"black\",\n",
    "    line_width=2,\n",
    "    name=\"cost\"),\n",
    "secondary_y=True\n",
    ")\n",
    "\n",
    "fig.update_xaxes(\n",
    "    title=\"bit string\")\n",
    "fig.update_yaxes(\n",
    "    title=\"probability\")\n",
    "fig.update_yaxes(\n",
    "    title=\"cost\",\n",
    "    secondary_y=True)\n",
    "fig.update_layout(\n",
    "    title=\"Best 50 bit strings w.r.t. to cost function\",\n",
    "    title_font_size=13\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d4cc068",
   "metadata": {},
   "source": [
    "# Further Examples"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28562f5b",
   "metadata": {},
   "source": [
    "## Convergence Classical Optimizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "9942cb23",
   "metadata": {},
   "outputs": [],
   "source": [
    "var_parameters_list = []\n",
    "def callback(*args):\n",
    "    var_parameters_list.append(args)\n",
    "    \n",
    "var_parameters_cvar_list = []\n",
    "def callback_cvar(*args):\n",
    "    var_parameters_cvar_list.append(args)\n",
    "\n",
    "cobyla_optimizer = COBYLA(callback=callback)\n",
    "res = cobyla_optimizer.minimize(fun=energy_evaluation, x0=parameter_values_initial_guess)\n",
    "\n",
    "cobyla_optimizer = COBYLA(callback=callback_cvar)\n",
    "res_cvar = cobyla_optimizer.minimize(fun=energy_evaluation_cvar, x0=parameter_values_initial_guess)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "4ca1dcb1",
   "metadata": {},
   "outputs": [],
   "source": [
    "expectation_values = [energy_evaluation(var_parameters[0]) for var_parameters in var_parameters_list]\n",
    "expectation_values_cvar = [energy_evaluation_cvar(var_parameters[0]) for var_parameters in var_parameters_cvar_list] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "69bb6c94",
   "metadata": {
    "scrolled": false
   },
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       "                            \n",
       "var gd = document.getElementById('e8771a46-1de4-4635-8f24-fc74ad24d1ff');\n",
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       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
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       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
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      ]
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     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "fig = go.Figure()\n",
    "fig.add_trace(go.Scatter(\n",
    "    y=expectation_values,\n",
    "    name=\"expectation value\"))\n",
    "fig.add_trace(go.Scatter(\n",
    "    y=expectation_values_cvar,\n",
    "    name=\"cvar expectation value\"))\n",
    "fig.update_xaxes(\n",
    "    title=\"iteration\")\n",
    "fig.update_layout(\n",
    "    title=\"Convergence classical optimizer\",\n",
    "    title_font_size=12)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9661def",
   "metadata": {},
   "source": [
    "## Further Examples"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2e6d4aa",
   "metadata": {},
   "source": [
    "The following section examines the behavior for different $\\alpha$."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72cb8a05",
   "metadata": {},
   "source": [
    "For $\\alpha = 0$:\n",
    "\n",
    "<img src=\"images/cvar/alpha_0.svg\" width=\"1500\" />"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d97cfef7",
   "metadata": {},
   "source": [
    "For $\\alpha = 0.2$:\n",
    "\n",
    "<img src=\"images/cvar/alpha_0p2.svg\" width=\"1500\" />"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3e409aa",
   "metadata": {},
   "source": [
    "For $\\alpha = 0.4$:\n",
    "\n",
    "<img src=\"images/cvar/alpha_0p4.svg\" width=\"1500\" />"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d2ed762",
   "metadata": {},
   "source": [
    "For $\\alpha = 0.6$:\n",
    "\n",
    "<img src=\"images/cvar/alpha_0p6.svg\" width=\"1500\" />"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c630fdda",
   "metadata": {},
   "source": [
    "For $\\alpha = 0.8$:\n",
    "\n",
    "<img src=\"images/cvar/alpha_0p8.svg\" width=\"1500\" />"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40477e5b",
   "metadata": {},
   "source": [
    "For $\\alpha = 1$:\n",
    "\n",
    "<img src=\"images/cvar/alpha_1.svg\" width=\"1500\" />"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19e8cd3a",
   "metadata": {},
   "source": [
    "# Literature <a class=\"tocSkip\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d26da6c9",
   "metadata": {},
   "source": [
    "[1] *Barkoutsos, P. K., Nannicini, G., Robert, A., Tavernelli, I., and Woerner, S. 2020. Improving\n",
    "Variational Quantum Optimization using CVaR. Quantum 4, 256*, see [link](https://arxiv.org/abs/1907.04769)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a21ddee5",
   "metadata": {},
   "source": [
    "---\n",
    "Authors: <br>\n",
    "Andreas Sturm, andreas.sturm@iao.fraunhofer.de <br>\n",
    "Niclas Schillo, niclas.schillo@iao.fraunhofer.de\n",
    "\n",
    "(c) Fraunhofer IAO\n",
    "\n",
    "---"
   ]
  }
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