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    "# Learn to estimate parameters of a single-qubit Hamiltonian\n",
    "**Performing system identification with Boulder Opal**\n",
    "\n",
    "In this tutorial, you will use Boulder Opal to obtain estimates of parameters present in a single-qubit Hamiltonian, using data from a simulated experiment.\n",
    "\n",
    "You will achieve this by running an optimization where you define your model of the system as a graph, and minimize the difference between the data from the simulated experiment and the predictions from your model.\n",
    "This allows you to find estimates of the parameters that most closely reproduce the simulated experimental data.\n",
    "After that, you will analyze the precision of your parameter estimates and compare them with the correct values."
   ]
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   "source": [
    "## Estimate parameters of a single-qubit Hamiltonian\n",
    "\n",
    "Your task will be to estimate the values of the parameters $\\Omega_x$, $\\Omega_y$, and $\\Omega_z$ from the following single-qubit Hamiltonian:\n",
    "\n",
    "$$ H = \\frac{1}{2} \\left( \\Omega_x \\sigma_x + \\Omega_y \\sigma_y + \\Omega_z \\sigma_z \\right), $$\n",
    "\n",
    "where the Pauli matrices are\n",
    "\n",
    "$$ \\sigma_x = \\left( \\begin{array}{cc} 0 & 1 \\\\ 1 & 0 \\end{array} \\right), \\, \\sigma_y = \\left( \\begin{array}{cc} 0 & -i \\\\ i & 0 \\end{array} \\right), \\, \\sigma_z = \\left( \\begin{array}{cc} 1 & 0 \\\\ 0 & -1 \\end{array} \\right). $$\n",
    "\n",
    "To perform this estimation, you will not have access to the Hamiltonian directly, but only to the results of the experiment.\n",
    "In each experiment setup, you can choose the initial state, let it evolve for a time that you select, and then measure an observable.\n",
    "As the free evolution of the state depends on the Hamiltonian, measuring an observable for different time intervals gives you information about the parameters that you want to determine.\n",
    "\n",
    "![estimate-parameters-of-a-single-qubit-hamiltonian.svg](attachment:estimate-parameters-of-a-single-qubit-hamiltonian.svg)\n",
    "\n",
    "To complete the system identification, you will create a graph that models the system whose parameters you want to estimate are optimizable variables.\n",
    "Next, you will run an optimization that minimizes the difference between the predictions of your model and the experimental data.\n",
    "With this, the optimizer will obtain estimates of the parameters that match the experimental data as closely as possible.\n",
    "\n",
    "To discover the model of a real hardware system, you would attempt to characterize it using measured experimental data.\n",
    "However, for the purposes of this tutorial, you will use the `run_experiments` function below to simulate the system that you are going to analyze.\n",
    "You can think of it as a black-box function that interacts with the qubit that you want to characterize.\n",
    "\n",
    "To use it, you just need to provide a batch of initial states (`initial_states`), observables (`observables`), and wait times (`wait_times`) for the experimental setups that you want to run.\n",
    "The function will then simulate all the experiments in the batch by preparing the qubit in the initial state, letting it evolve for a wait time, and then measuring the observable that you requested.\n",
    "The values returned are the expectation values for the observable, with an error added to represent the uncertainty inherent to calculating an observable from a finite set of measurements.\n",
    "More specifically, the observables will be Pauli matrices, and the associated errors in the measured expectation values will have Gaussian probability distributions with a standard deviation of 0.02."
   ]
  },
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   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
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   "source": [
    "# Correct values of the parameters that you want to determine.\n",
    "# The `run_experiments` function will use them to produce simulated experimental results.\n",
    "actual_parameters = [3.54e5, 7.91e5, -5e5]  # Hz\n",
    "parameter_names = [\"Ω_x\", \"Ω_y\", \"Ω_z\"]\n",
    "\n",
    "\n",
    "def run_experiments(initial_states, observables, wait_times):\n",
    "    \"\"\"\n",
    "    A black-box function representing a quantum system whose parameters\n",
    "    you want to determine.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    initial_states : np.ndarray\n",
    "        An array containing the initial states of each experiment setup.\n",
    "        Its shape must be ``(N, 2, 1)``, where ``N`` is the number of\n",
    "        experiment setups\n",
    "    observables : np.ndarray\n",
    "        An array containing the observables that you want to measure at\n",
    "        the end of each experiment setup. Its shape must be ``(N, 2, 2)``,\n",
    "        where ``N`` is the number of experiment setups.\n",
    "    wait_times : np.ndarray\n",
    "        The list of times when the experiment ends. Its shape must be\n",
    "        ``(T,)``, where ``T`` is the number of points in time where the\n",
    "        experiment ends.\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    np.ndarray\n",
    "        A batch of simulated experimental results corresponding to the\n",
    "        setups and times that you provided. It will have shape ``(N, T)``,\n",
    "        where ``N`` is the number of experimental setups and ``T`` is the\n",
    "        number of wait times.\n",
    "    \"\"\"\n",
    "\n",
    "    alpha_x, alpha_y, alpha_z = actual_parameters\n",
    "\n",
    "    graph = bo.Graph()\n",
    "\n",
    "    hamiltonian = graph.constant_pwc(\n",
    "        constant=(\n",
    "            alpha_x * graph.pauli_matrix(\"X\")\n",
    "            + alpha_y * graph.pauli_matrix(\"Y\")\n",
    "            + alpha_z * graph.pauli_matrix(\"Z\")\n",
    "        )\n",
    "        * 0.5,\n",
    "        duration=np.max(wait_times),\n",
    "    )\n",
    "    unitary = graph.time_evolution_operators_pwc(\n",
    "        hamiltonian=hamiltonian, sample_times=wait_times\n",
    "    )\n",
    "    final_states = unitary @ initial_states[:, None]\n",
    "\n",
    "    expectation_values = graph.real(\n",
    "        graph.expectation_value(final_states[..., 0], observables[:, None])\n",
    "    )\n",
    "\n",
    "    errors = graph.random.normal(\n",
    "        shape=expectation_values.shape, mean=0, standard_deviation=0.02\n",
    "    )\n",
    "    measurement_results = expectation_values + errors\n",
    "    measurement_results.name = \"measurement_results\"\n",
    "\n",
    "    result = bo.execute_graph(graph, \"measurement_results\")\n",
    "\n",
    "    return result[\"output\"][\"measurement_results\"][\"value\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Import libraries\n",
    "\n",
    "Before doing any calculation with Boulder Opal, you need to import the necessary packages.\n",
    "To learn more about installing Boulder Opal, see the [Get started](https://docs.q-ctrl.com/boulder-opal/toolkit/get-started-with-boulder-opal-toolkit) guide. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import packages.\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import qctrlvisualizer\n",
    "import boulderopal as bo\n",
    "\n",
    "# Apply Q-CTRL style to plots created in pyplot.\n",
    "plt.style.use(qctrlvisualizer.get_qctrl_style())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Obtain data from the simulated experiment\n",
    "\n",
    "To perform system identification, you want to compare the model predictions with the data that you obtain from the `run_experiments` function provided at the beginning of this tutorial.\n",
    "Use a set of initial states, observables, and wait times that correspond to the experiments that you'd be running.\n",
    "\n",
    "Specifically, you will be using three types of experiment setups:\n",
    "1. Prepare the system at state $| z+ \\rangle$ and measure $\\sigma_x$ after a period of free evolution.\n",
    "2. Prepare the system at state $| x+ \\rangle$ and measure $\\sigma_y$ after a period of free evolution.\n",
    "3. Prepare the system at state $| y+ \\rangle$ and measure $\\sigma_z$ after a period of free evolution.\n",
    "\n",
    "For each kind of experiment, the system is left to evolve for 40 values of time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your task (action_id=\"1828680\") is queued.\n",
      "Your task (action_id=\"1828680\") has started.\n",
      "Your task (action_id=\"1828680\") has completed.\n"
     ]
    }
   ],
   "source": [
    "# Define initial states of the experiments.\n",
    "state_zp = np.array([[1], [0]])  # |z+⟩, also known as |0⟩\n",
    "state_xp = np.array([[1], [1]]) / np.sqrt(2)  # |x+⟩, also known as |+⟩\n",
    "state_yp = np.array([[1], [1j]]) / np.sqrt(2)  # |y+⟩\n",
    "initial_states = np.array([state_zp, state_xp, state_yp])\n",
    "initial_states_names = [r\"$| z+ \\rangle$\", r\"$| x+ \\rangle$\", r\"$| y+ \\rangle$\"]\n",
    "\n",
    "# Define observables measured in the experiments.\n",
    "sigma_x = np.array([[0, 1], [1, 0]])\n",
    "sigma_y = np.array([[0, -1j], [1j, 0]])\n",
    "sigma_z = np.array([[1, 0], [0, -1]])\n",
    "observables = np.array([sigma_x, sigma_y, sigma_z])\n",
    "observables_names = [\n",
    "    r\"$\\langle\\sigma_x\\rangle$\",\n",
    "    r\"$\\langle\\sigma_y\\rangle$\",\n",
    "    r\"$\\langle\\sigma_z\\rangle$\",\n",
    "]\n",
    "\n",
    "# Define wait times of the experiments.\n",
    "duration = 10e-6  # s\n",
    "wait_times = np.linspace(0, duration, 40)\n",
    "\n",
    "black_box_data = run_experiments(initial_states, observables, wait_times)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can plot the data obtained from the `run_experiments` function to see how the values of the observables oscillate in time.\n",
    "The results are organized in a single array of shape `(3, 40)`, where the first dimension represents the three experiment setups, and the second dimension represents all the wait times."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 1440x360 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, len(black_box_data), figsize=(20, 5))\n",
    "fig.suptitle(\"Measured data from the experiments\")\n",
    "\n",
    "for index, axis in enumerate(axes):\n",
    "    axis.set_title(f\"Initial state {initial_states_names[index]}\")\n",
    "    axis.set_ylabel(observables_names[index], labelpad=0)\n",
    "    axis.plot(wait_times * 1e6, black_box_data[index], \"o\")\n",
    "    axis.set_ylim(-1.1, 1.1)\n",
    "    axis.set_xlabel(\"Wait time $t$ (µs)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Define your model and obtain estimates of the parameters\n",
    "\n",
    "The next step will be to create a graph that models the system.\n",
    "This graph will have optimization variables for the parameters that you want to determine.\n",
    "When you run an optimization on it, it will attempt to minimize the difference between the experimental data and your model, which will allow you to obtain good estimates for the parameters. You can learn more about graphs from our [Understanding graphs in Boulder Opal](https://docs.q-ctrl.com/boulder-opal/toolkit/design/calculate-with-graphs/get-an-introduction-to-graphs-in-boulder-opal) topic and [Get familiar with graphs](https://docs.q-ctrl.com/boulder-opal/toolkit/design/represent-time-varying-signals/learn-to-use-graphs-with-piecewise-constant-pulses) tutorial."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create the graph object\n",
    "\n",
    "Start by creating the graph object that will define the calculation.\n",
    "You can do this by creating a `boulderopal.Graph` instance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "graph = bo.Graph()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create optimizable variables\n",
    "\n",
    "The next step is to define optimizable variables for the parameters that you want to estimate: $\\Omega_x$, $\\Omega_y$, and $\\Omega_z$.\n",
    "You can do this using the node `graph.optimization_variable`, which takes a `count` parameter (which you can use to create a batch of variables at the same time) and an `upper_bound` and a `lower_bound`.\n",
    "In this case, assume that you know that the norm of the variables will be smaller than 1 MHz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "omega_fit = graph.optimization_variable(\n",
    "    count=3, lower_bound=-1e6, upper_bound=1e6, name=\"omega_fit\"\n",
    ")\n",
    "omega_x_fit = omega_fit[0]\n",
    "omega_y_fit = omega_fit[1]\n",
    "omega_z_fit = omega_fit[2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Construct the Hamiltonian for the model\n",
    "\n",
    "Now that you have defined the variables that you want to determine, the next step will be to use them to build a model whose predictions you can compare with the data from the black box.\n",
    "In this case, the model will contain the following Hamiltonian:\n",
    "$$ H = \\frac{1}{2} \\left( \\Omega_x \\sigma_x + \\Omega_y \\sigma_y + \\Omega_z \\sigma_z \\right). $$\n",
    "As this Hamiltonian doesn't change in time, you can define it using the operation `graph.constant_pwc`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define Hamiltonian of the model.\n",
    "hamiltonian = graph.constant_pwc(\n",
    "    constant=0.5\n",
    "    * (omega_x_fit * sigma_x + omega_y_fit * sigma_y + omega_z_fit * sigma_z),\n",
    "    duration=duration,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define the experiments for the model\n",
    "\n",
    "You will compare the predictions from this model with the data provided to you by the black box.\n",
    "Do this by setting up in the model the same kind of experiments that you ran in the black box: prepare them in the same initial state, evolve them for the same wait times, and then measure the same observables.\n",
    "\n",
    "The operation `graph.time_evolution_operators_pwc` allows you to calculate the unitary time evolution operators for the modeled system.\n",
    "Use it to evolve the system from an initial state up to a certain set of sample times.\n",
    "Then, use the operations `graph.conjugate`, `graph.transpose`, and matrix products (`@`) to calculate the final expectation value.\n",
    "\n",
    "The results are organized in a batch of shape `(3, 40)`, where the first dimension represents the three experiment setups, and the second dimension represents all the wait times.\n",
    "To learn more about batching, see the topic [Batching and broadcasting in Boulder Opal](https://docs.q-ctrl.com/boulder-opal/toolkit/design/calculate-with-graphs/understand-batches-and-broadcasting-in-boulder-opal)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate the evolution according to the Hamiltonian.\n",
    "unitaries = graph.time_evolution_operators_pwc(\n",
    "    hamiltonian=hamiltonian, sample_times=wait_times\n",
    ")\n",
    "\n",
    "# Obtain expectation values at the requested times.\n",
    "final_states = unitaries @ initial_states[:, None]\n",
    "\n",
    "# Obtain expectation values.\n",
    "expectation_values = graph.real(\n",
    "    graph.expectation_value(final_states[..., 0], observables[:, None]),\n",
    "    name=\"expectation_values\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Construct the cost of the optimization\n",
    "\n",
    "Finally, you can define the cost that you want to minimize as the sum of the square differences between the predictions of your model and your experimental data, known as the [residual sum of squares (RSS)](https://en.wikipedia.org/wiki/Residual_sum_of_squares).\n",
    "In other words, if $\\{ x_i \\}$ is the set of input data and $\\{ \\tilde x_i \\}$ are the corresponding predictions from your model, the cost $C_\\textrm{RSS}$ will be:\n",
    "\n",
    "$$ C_\\text{RSS} = \\sum_i \\left( x_i - \\tilde x_i \\right)^2. $$\n",
    "\n",
    "Note that for a Gaussian probability of errors with constant standard deviation the RSS is proportional to the negative log likelihood, as described in the topic [Characterizing your hardware using system identification in Boulder Opal](https://docs.q-ctrl.com/boulder-opal/toolkit/design/characterize-hardware/explore-system-identification-techniques-for-quantum-hardware-characterization).\n",
    "In a situation like this, minimizing either the RSS or the negative log likelihood is equivalent.\n",
    "\n",
    "You can calculate this sum using the operations `graph.sum` on the [batches](https://docs.q-ctrl.com/boulder-opal/toolkit/design/calculate-with-graphs/understand-batches-and-broadcasting-in-boulder-opal) of data from the model and the experiment (`run_experiments`).\n",
    "In the ideal case where the model and the actual system match perfectly, this cost will be zero."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Obtain the residual sum of squares (RSS), to be used as cost.\n",
    "residuals = expectation_values - black_box_data\n",
    "rss = graph.sum(residuals**2, name=\"rss\")\n",
    "\n",
    "# Obtain the Hessian, to be used for statistical analysis later.\n",
    "fit_parameters = [omega_x_fit, omega_y_fit, omega_z_fit]\n",
    "hessian = graph.hessian(rss, fit_parameters, name=\"hessian\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Optimize the graph\n",
    "\n",
    "Now that the graph is ready, you can run the optimization with the function `boulderopal.run_optimization`.\n",
    "It will attempt to minimize the node whose `cost_node_name` you provide (in this case, the sum of the square of the differences between the model predictions and the input data)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your task (action_id=\"1828687\") is queued.\n",
      "Your task (action_id=\"1828687\") has started.\n",
      "Your task (action_id=\"1828687\") has completed.\n"
     ]
    }
   ],
   "source": [
    "result = bo.run_optimization(\n",
    "    graph=graph,\n",
    "    cost_node_name=\"rss\",\n",
    "    output_node_names=[\"expectation_values\", \"omega_fit\", \"hessian\"],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Analyze the results\n",
    "#### Compare parameters\n",
    "\n",
    "After the graph optimization has completed, all of the output data is stored in the dictionary returned by `boulderopal.run_optimization`.\n",
    "The values of the nodes that you have requested when optimizing the graph are stored in the dictionary `result[\"output\"]`.\n",
    "\n",
    "You can access the `\"omega_fit\"` key of this object to extract the estimated parameters that you obtained.\n",
    "As our experiment is simulated by the `run_experiments` function, you can directly compare the estimates with the correct values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t Estimated value\t Correct value\n",
      "Ω_x: \t 0.353 MHz \t\t 0.354 MHz\n",
      "Ω_y: \t 0.792 MHz \t\t 0.791 MHz\n",
      "Ω_z: \t -0.500 MHz \t\t -0.500 MHz\n"
     ]
    }
   ],
   "source": [
    "fit_values = result[\"output\"][\"omega_fit\"][\"value\"]\n",
    "\n",
    "print(\"\\t Estimated value\\t Correct value\")\n",
    "for name, fit, actual_value in zip(parameter_names, fit_values, actual_parameters):\n",
    "    print(f\"{name}: \\t {fit/1e6:.3f} MHz \\t\\t {actual_value/1e6:.3f} MHz\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Compare the expectation values\n",
    "\n",
    "The result output also carries the `expectation_values` that you requested during the optimization.\n",
    "You can plot them together with the expectation values obtained from the experiment, to see how close they are."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x360 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_data = result[\"output\"][\"expectation_values\"][\"value\"]\n",
    "\n",
    "fig, axes = plt.subplots(1, len(model_data), figsize=(20, 5))\n",
    "\n",
    "fig.suptitle(\"Measured data from the experiments and predictions from the model\")\n",
    "\n",
    "for index, axis in enumerate(axes):\n",
    "    axis.set_title(f\"Initial state {initial_states_names[index]}\")\n",
    "    axis.set_ylabel(observables_names[index], labelpad=0)\n",
    "    axis.plot(wait_times * 1e6, black_box_data[index], \"o\", label=\"Black-box data\")\n",
    "    axis.plot(wait_times * 1e6, model_data[index], \"--\", label=\"Model data\")\n",
    "    axis.set_ylim(-1.05, 1.05)\n",
    "    axis.legend()\n",
    "    axis.set_xlabel(\"Wait time $t$ (µs)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Estimate accuracy of the results\n",
    "\n",
    "Now that you have the estimates, you can use some statistical analysis to determine their accuracy.\n",
    "Following the topic [Characterizing your hardware using system identification in Boulder Opal](https://docs.q-ctrl.com/boulder-opal/toolkit/design/characterize-hardware/explore-system-identification-techniques-for-quantum-hardware-characterization), you can use the covariance matrix (which for a Gaussian probability distribution is proportional to the inverse of the Hessian matrix) to obtain the confidence region of your estimated parameters.\n",
    "\n",
    "You can easily calculate and represent a hyperellipse representing the confidence region by using the Q-CTRL Visualizer.\n",
    "Passing the Hessian, the RSS cost, and number of measurements to [`qctrlvisualizer.confidence_ellipse_matrix`](https://docs.q-ctrl.com/references/qctrl-visualizer/qctrlvisualizer/confidence_ellipse_matrix) will give you a representation the confidence region as a hyperellipse that you can visualize with [`qctrlvisualizer.plot_confidence_ellipses`](https://docs.q-ctrl.com/references/qctrl-visualizer/qctrlvisualizer/plot_confidence_ellipses)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x360 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot 95%-confidence ellipses.\n",
    "confidence_region = qctrlvisualizer.confidence_ellipse_matrix(\n",
    "    hessian=result[\"output\"][\"hessian\"][\"value\"],\n",
    "    cost=result[\"cost\"],\n",
    "    measurement_count=3 * len(wait_times),\n",
    "    confidence_fraction=0.95,\n",
    ")\n",
    "\n",
    "qctrlvisualizer.plot_confidence_ellipses(\n",
    "    confidence_region,\n",
    "    estimated_parameters=fit_values,\n",
    "    actual_parameters=actual_parameters,\n",
    "    parameter_names=[r\"$\\Omega_x$\", r\"$\\Omega_y$\", r\"$\\Omega_z$\"],\n",
    ")\n",
    "plt.suptitle(\"Estimated parameters (with 95% confidence region)\", y=1.05)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Congratulations!\n",
    "You have performed system identification for a single-qubit system.\n",
    "\n",
    "Our user guides offer more examples and problems to which you can apply system identification.\n",
    "In particular, you might be interested in reading about [performing Hamiltonian parameter estimation for large amounts of data using stochastic optimization](https://docs.q-ctrl.com/boulder-opal/toolkit/design/characterize-hardware/how-to-perform-parameter-estimation-with-a-large-amount-of-data) or [using system identification to characterize the bandwidth of a control line](https://docs.q-ctrl.com/boulder-opal/toolkit/design/characterize-hardware/how-to-characterize-a-transmission-line-using-a-qubit-as-a-probe).\n",
    "\n",
    "If you want to learn more about Boulder Opal and its capabilities, visit our [topics page](https://docs.q-ctrl.com), which includes a topic about [system identification](https://docs.q-ctrl.com/boulder-opal/toolkit/design/characterize-hardware/explore-system-identification-techniques-for-quantum-hardware-characterization)."
   ]
  }
 ],
 "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": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
