system_identification.ipynb

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            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        }
      }
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/codecrap/qctrl-qchack-Estonia/blob/main/system_identification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7QZonn7b4N_L"
      },
      "source": [
        "!pip install qctrl qctrl-visualizer qctrl-open-controls"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hibVPw1k7c6G"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import math\n",
        "from pprint import pprint\n",
        "from itertools import product\n",
        "\n",
        "from qctrlvisualizer import get_qctrl_style, plot_controls\n",
        "from qctrl import Qctrl\n",
        "\n",
        "qctrl = Qctrl()\n",
        "plt.style.use(get_qctrl_style())"
      ],
      "execution_count": 601,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xzqVmyFu4Oxs"
      },
      "source": [
        "# Definitions\n",
        "---"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7igMu1XO73Ms"
      },
      "source": [
        "# Define standard matrices\n",
        "sigma_x = np.array([[0, 1], [1, 0]], dtype=np.complex)\n",
        "sigma_y = np.array([[0, -1j], [1j, 0]], dtype=np.complex)\n",
        "sigma_z = np.array([[1, 0], [0, -1]], dtype=np.complex)\n",
        "\n",
        "total_duration = 50  # ns\n",
        "shot_count = 1024\n",
        "\n",
        "# Define parameters of the Gaussian pulses to probe the filter\n",
        "max_width = total_duration / 4.0\n",
        "mean_values = np.linspace(max_width, total_duration - max_width, 6)\n",
        "width_values = np.linspace(max_width / 8.0, max_width / 2.0, 4)\n",
        "\n",
        "# Define sampled times\n",
        "segments = 255\n",
        "t_values = np.linspace(0.0, total_duration, segments + 1)\n",
        "\n",
        "# Define Gaussian pulse, normalized to generate pi-pulses\n",
        "# pi * gauss_normalized(..) = \n",
        "def gauss_pulse_operation(t, mean, width):\n",
        "  return qctrl.operations.exp(-0.5 * ((t - mean) / width) ** 2.0) * qctrl.operations.sqrt(0.5 * np.pi / width ** 2.0)\n",
        "\n",
        "def gaussian(t, amp, mean, width):\n",
        "  return amp * np.exp(-0.5 * ((t - mean) / width) ** 2.0) #* np.sqrt(0.5 / np.pi / width ** 2.0)\n",
        "\n",
        "def estimate_probability_of_one(measurements):\n",
        "  size = len(measurements)\n",
        "  probability = np.mean(measurements)\n",
        "  standard_error = np.std(measurements) / np.sqrt(size)\n",
        "  return (probability, standard_error)\n",
        "\n",
        "def estimate_probabilities(M):\n",
        "  P = []\n",
        "  for m in M:\n",
        "    size = len(m)\n",
        "    p0 = m.count(0) / size\n",
        "    p1 = m.count(1) / size\n",
        "    p2 = m.count(2) / size\n",
        "    P.append([p0,p1,p2])\n",
        "  return P"
      ],
      "execution_count": 602,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n15COcRGMP3b",
        "outputId": "82056343-150b-4a66-9140-39d60842d294"
      },
      "source": [
        "print(list(zip(mean_values, width_values)))"
      ],
      "execution_count": 603,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[(12.5, 1.5625), (17.5, 3.125), (22.5, 4.6875), (27.5, 6.25)]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J-X8yacGjYzQ"
      },
      "source": [
        "# Single amplitude sweep for fixed $(\\mu, \\sigma)$\n",
        "---\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JruPiuf8jZCw"
      },
      "source": [
        "amplitudes = np.linspace(0.001,1,100)\n",
        "controls = []\n",
        "for amp in amplitudes:\n",
        "  I = np.array([gaussian(t, amp, mean_values[2], width_values[2]) for t in t_values])\n",
        "  Q = np.zeros(segments+1) # amp/2*np.ones(segments+1) #\n",
        "  # I = 0.5*np.random.random(segments+1)\n",
        "  # Q = 0.5*np.random.random(segments+1)\n",
        "  values = I + 1j*Q\n",
        "  controls.append({\"duration\": total_duration, \"values\": values}) #, \"mean\": mean, \"width\": width})\n"
      ],
      "execution_count": 604,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "id": "VNVrFcM3mL5C",
        "outputId": "d010f6af-1ad9-455f-80e6-d3b1eafcbddf"
      },
      "source": [
        "plt.plot(controls[-1][\"values\"]/(2*np.pi)) #np.array([gaussian_pulse(1, t, mean_values[2], width_values[2]) for t in t_values]))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/numpy/core/_asarray.py:83: ComplexWarning: Casting complex values to real discards the imaginary part\n",
            "  return array(a, dtype, copy=False, order=order)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fb0ed9f0950>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 178
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "t3nzQ1r-kleU",
        "outputId": "99c575d5-9ab0-4bc4-90e6-75f607a2305b"
      },
      "source": [
        "plot_controls(\n",
        "    figure=plt.figure(),\n",
        "    controls={\n",
        "        \"$\\Omega$\": [\n",
        "            {\"duration\": total_duration / segments, \"value\": value} for value in controls[50][\"values\"]\n",
        "        ]\n",
        "    },\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51,
          "referenced_widgets": [
            "cedf6886cd0d4f6e9cd6d89b9e7e99cb",
            "8116b7a68b3b447789ea3a82adf92287",
            "d31af996d1904e57ba9b0d6b9c2c099b",
            "419a884a1e204bd4b0c1f97a6f5b4dd4",
            "9e31a86d95094d5b8c5d7316a8e1d5b2",
            "1aae9900002143aebd22142f0dae0ca8",
            "821f74b219f74c749b002d2518ec124b",
            "4c4b233bcb214b2fb157080be86ff74f"
          ]
        },
        "id": "CTXf22Takz92",
        "outputId": "5d12c7fb-80cb-420c-dd07-729f3f221b47"
      },
      "source": [
        "experiment_results = qctrl.functions.calculate_qchack_measurements(\n",
        "    controls=controls,\n",
        "    shot_count=shot_count,\n",
        ")"
      ],
      "execution_count": 605,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "cedf6886cd0d4f6e9cd6d89b9e7e99cb",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Your task calculate_qchack_measurements has started.\n",
            "Your task calculate_qchack_measurements has completed in 56s.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vi-15S-7qbrX",
        "outputId": "43a7f055-99cc-46ba-fc67-ff4ae9fa5fb5"
      },
      "source": [
        "measurement_results = experiment_results.measurements\n",
        "print(len(measurement_results))\n",
        "probabilities, errors = [], []\n",
        "for result in measurement_results:\n",
        "  p,e = estimate_probability_of_one(result)\n",
        "  probabilities.append(p)\n",
        "  errors.append(e)\n"
      ],
      "execution_count": 606,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zQ1oXJrl3ozG",
        "outputId": "ca13339f-78fb-4657-cff8-33eac683c3cc"
      },
      "source": [
        "print(np.array(measurement_results).shape)\n",
        "print(measurement_results[-1].count(2))\n",
        "print(np.max(probabilities))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(100, 1024)\n",
            "0\n",
            "0.87890625\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "8o6ltuqzqvyG",
        "outputId": "e50f8cea-4a5d-45ad-feeb-abc02bc5dabf"
      },
      "source": [
        "plt.errorbar(\n",
        "        amplitudes,\n",
        "        probabilities,\n",
        "        yerr=errors,\n",
        "        fmt=\"s\",\n",
        "    )"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<ErrorbarContainer object of 3 artists>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 581
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "nCVQsqhqHDfx",
        "outputId": "ac7391e6-667d-42d5-c907-a86c49edea12"
      },
      "source": [
        "P = np.array(estimate_probabilities(measurement_results))\n",
        "# print(P[-40,:])\n",
        "plt.plot(amplitudes, P[:,0], 's', amplitudes, P[:,1], 'd', amplitudes, P[:,2], 'o')\n",
        "plt.xlabel(\"Pulse Amplitude (a.u.)\")\n",
        "plt.ylabel(\"State population probability estimate\")\n",
        "plt.savefig(\"amplitude.png\", dpi=256)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f_jYqNpyvsmR"
      },
      "source": [
        "# Amplitude calibration\n",
        "---"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 833,
          "referenced_widgets": [
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        "id": "7A6kxwLsvu5u",
        "outputId": "a5fe8bf1-7343-4bfd-fa20-5e49bf5c3f84"
      },
      "source": [
        "amplitudes = np.linspace(0.001,1,100)\n",
        "max_amps = []\n",
        "\n",
        "for width,mean in product(width_values, mean_values):\n",
        "  controls = []\n",
        "  for amp in amplitudes:\n",
        "    I = np.array([gaussian(t, amp, mean, width) for t in t_values])\n",
        "    Q = np.zeros(segments+1) # amp/2*np.ones(segments+1) #\n",
        "    values = I + 1j*Q\n",
        "    controls.append({\"duration\": total_duration, \"values\": values})\n",
        "\n",
        "  experiment_results = qctrl.functions.calculate_qchack_measurements(controls=controls, shot_count=shot_count)\n",
        "  measurement_results = experiment_results.measurements\n",
        "  probabilities, errors = [], []\n",
        "  for result in measurement_results:\n",
        "    p,e = estimate_probability_of_one(result)\n",
        "    probabilities.append(p)\n",
        "    errors.append(e)\n",
        "  \n",
        "  max_amps.append( amplitudes[probabilities == np.max(probabilities)] )\n",
        "\n"
      ],
      "execution_count": 607,
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Your task calculate_qchack_measurements has started.\n",
            "Your task calculate_qchack_measurements has completed in 8s.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "8723182c400c4475b25f916367bc1d09",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Your task calculate_qchack_measurements has started.\n",
            "Your task calculate_qchack_measurements has completed in 8s.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "3938753935114d5b9d512cb0c72cf916",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Your task calculate_qchack_measurements has started.\n",
            "Your task calculate_qchack_measurements has completed in 8s.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "155f3d997d784c65a1fb785fed3836d1",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Your task calculate_qchack_measurements has started.\n",
            "Your task calculate_qchack_measurements has completed in 8s.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "8bf5b9df57e64e8a8978b655353ba038",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Your task calculate_qchack_measurements has started.\n",
            "Your task calculate_qchack_measurements has completed in 23s.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "kRbF5PaGzo65",
        "outputId": "2ee2ebae-1826-4e0f-b794-576462e0fd6c"
      },
      "source": [
        "print(np.array(max_amps).flatten())\n",
        "print([a[0] for a in max_amps])\n",
        "plt.plot([a[0] for a in max_amps])\n",
        "max_amps_list = [a[0] for a in max_amps]"
      ],
      "execution_count": 608,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[array([1.]) array([0.98990909]) array([1.]) array([0.97981818])\n",
            " array([1.]) array([0.98990909]) array([0.98990909]) array([1.])\n",
            " array([0.98990909]) array([0.97981818]) array([1.]) array([0.95963636])\n",
            " array([0.80827273, 0.87890909]) array([0.889]) array([0.83854545])\n",
            " array([0.80827273, 0.89909091]) array([0.83854545]) array([0.87890909])\n",
            " array([0.63672727, 0.64681818]) array([0.64681818]) array([0.62663636])\n",
            " array([0.60645455]) array([0.65690909]) array([0.63672727])]\n",
            "[1.0, 0.989909090909091, 1.0, 0.9798181818181818, 1.0, 0.989909090909091, 0.989909090909091, 1.0, 0.989909090909091, 0.9798181818181818, 1.0, 0.9596363636363637, 0.8082727272727274, 0.889, 0.8385454545454546, 0.8082727272727274, 0.8385454545454546, 0.878909090909091, 0.6367272727272728, 0.6468181818181818, 0.6266363636363637, 0.6064545454545455, 0.6569090909090909, 0.6367272727272728]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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I9osm6MuAgRG3B4TvO8i0jJ2EevRoqp4DXGJaRqWm6mXA+GaPfbcT7RUi7noPcZKWKdsKiu4rmqBfAQzVVH0IoYCfDEyNPEFT9d5AuWkZQeBWQjNwAJYC90YMwH4vfFyIbsPhUCg+UTYhEd1XmwVH0zL8wHRCob0BWGBaxjpN1Wdpqj4xfNp4YKOm6p8D/YB7wo8tB+4m9GaxApgVvk+IbqW41MWu9XJ1rOielGTbPWfrrnJ7sAzGiiTz1qP1vHhrLQ9s7k1un84PyAb8Ns/fVMO4azI5ZrTU/UXnbdy2d9WwQX1HtXRMphAIEYWmmTdlMVoKYdMyH8usRubrNQSDydXZEqlHgl6IKBTHeM2bjxd6ANi+xs8niz0xeU4hWiNBL0QU8vo6yC5SYrIUQjBo88liD6d+P52jhjn5x911BPzSqxddR4JeiCgoihJaCiEGPfrNH/mo3hPk9EvcTLw9m92fB1g+vzEGrRSiZRL0QkSpuNTFzg2BTtfUVy/24MqAk89LZ8TEDI4e6eKV++rwebp3r95bb7Pg1zXs3yqzk5KNBL0QUSoudeGptanY3vE1b2zbZvUiD6XnpuPOdaAoCpPuyKb8qyAfPNMQw9bG3+K7a3nnsQY+XSJjDslGgl6IKB2cedOJOv22VX4qdgQZOSnj4H0nnpvO0HFpvPpAPd767tmr37LCx9uPhd6oKspk8bdkI0EvRJSa1rzpTJ3+44UenGlw8vmHgl5RFCbekU31niDvPlHf6XbGm89jY/2smoL+DvL7O6jcKaWbZCNBL0SUMvMcFB3t6PBcetu2+XhRIyd8K53swq//1ztubDrDv5fO0ofqaajqXj3i135Xx67PAkx9JJe+xzqp3Nm92t8TSNAL0Q7FJ7o6PMVyx6d+DmwNMnJiRovHJ96eTX2FzZtm9+nV71jr47UH6xkzxc1J52VQWOKgQnr0SUeCXoh2KBnuYvfnAfze9tfSP17oweGEUy9sOeiPHpHGaRdn8NacBmr2JX+vOOC3sW6sIbtQ4dL7cgAo6O+kamdQrvZNMhL0QrRDcamLoB/2bGpfr9W2bT5e6GHoOWnk9G79v91Fv8nGW2+z1KjrbFO73Jt/qOer1X4mG7nk9Ar9TAUlDvxeqD0gQZ9MJOiFaIfi4aGZN+0dkN25IcDeTQFO+4H7iOcdNczFmVPcvPenBirKkrcEsvtzP6/cW8eIiRlf+5kKi0MD1pVJ3PaeSIJeiHboN9SJw0m76/SrF3lQFDj1++ltnnvhrdnYQVhyf3L26oNBm+durCE9S2GykfO1YwUloUiRKZbJRYJeiHZIy1DoN9TZ7h796oWNHDc2jfx+zjbP7TXIyTnXZrLMamTvl8m32cl7cxv48iMfl96Xc9jPUxgOeplimVwk6IVop+JSV7umWO7+3M/O9QFGTGp5ELYlE27JwpkG/7wvuXr1B7YFWHRXHaXfTefMqYeXoXL7OHC4kCmWSSaqzcE1VZ8APAI4gXmmZcxudvxo4BmgIHzODNMylmiqPpjQrlQbw6d+ZFrGT2LUdiESoni4i1Uve2isDeLOabuvtHpRaEmA1qZVtiS/n5Nv/SSLNx6u53u6n5LSqP6rdinbtnlOqwYFrngkF0VRDjvH4VTIP8ohpZsk0+ZvqabqTmAOcD5QCkzRVL202WkzCW0xOJLQnrKPRRz70rSMEeE/EvKi22sK3V0boitPrF7kYcgZLgpL2i7bRPrezVlk5Cr84+7adrexK3xoNfLZOz4unpVN0cDWf5bCYrk6NtlEU7oZDWwyLWOzaRleYD4wqdk5NpAX/jof2Bm7JgqRXJqWQohmzZt9WwJsX+Nvc7ZNS7KLHHz3pizWvOJl60pfux8fS5W7Arx4Wy1Dx6VxznWZRzy3oMQpPfokE03QlwDbI27vCN8X6S7gSk3VdwBLAC3i2BBN1Vdrqv6epurndKaxQiSDXoOdpGfBzg1tB/3qRaF15ke2oz4f6ds/yySnl8KiWYnr1du2zfxf1OD32Fz5aC4Ox+Elm0hNPfpk24+6J4vVYOwU4GnTMgYAFwCWpuoOYBdwdLikowPPa6qe1/zBmqpP01R9pabqK2urk+NjqhCtcTgU+ke5FMLqRR6OHumi16D2lW2auHMdTPhlNp+942Pjv7wdeo7OWvWyhzX/9HLRzBz6Htf2WEFBiRNvPdRXStAni2iCvgwYGHF7QPi+SNcBCwBMy/gQcAO9TcvwmJZxIHz/KuBL4Pjm38C0jLmmZYwyLWNUTl5O88NCJJ2S0rZ3myrfEWDrSj+ndbA33+QbP86koNjB4v+rjXsvuXZ/kL/+soZBp7v49o1HLtk0OTTFUso3ySKaoF8BDNVUfYim6umEBlsXNzvnK+BcAE3VTyQU9Ps0Ve8THsxFU/VjgKHA5lg1XohEKS51UbPPpvoIa9I0bfrd0bJNkzS3wgW/zmbzcj//fS2+vfoFv66hocpGfSwPp+vIJZsmBU1Xx8qAbNJoM+hNy/AD04GlhKZKLjAtY52m6rM0VZ8YPu1/ges1VV8DvABcY1qGDXwD+FRT9U+AF4GfmJZR3hU/iBDxFM1SCB8v9FBykiuqckdbxqpu+hzjZNGsurgtGLb2VQ8rFniYcEt2u6Z3FhTL1bHJRkm2AZOtu8rtwf2LEt0MIY6oak+AGccd4Ef35/Dtn2Udfnx3gFuPP8CFt2Vz4YzsmHzP5Qsaeeq6aq59Ko8zLm3/LJ72qK8McveYcrILFWb8qwhXenS9eQC/1+am3vu4YEYW379NSrHxsnHb3lXDBvUd1dIxuTJWiA7I6+sgp5fSao/+k394sG047QedK9tEGnVpBgNOdvFXvYa9m7puaYSAz2beNdVU7wly5Zy8doU8gCtdIbevXDSVTCTohegARVEoHu5qdS79xws9HHW8k/4nxO6KVodDYdpzeSgOePSSqi5Zs962bV74RQ0b3vJyxR9yGXx6Woeep7DEIYOxSUSCXogOKi51sWtD4LCaec2+IF+872NkDHvzTfoc4+KnCwqo3Bngj5dX4m2Iben1td/X88EzjZz/qyzGXhXdLJuWFBQ7knqZ5Z5Ggl6IDiopdeGpsyn/6us91zWveLCDsS3bRDpmdBrXzMtj60o/T19fHbPB2eULGlk8q47RkzO4aGbnxhUKS5xU7pIefbKQoBeig4rDM1Gal29WL/bQ5xgnJSd13UJkp01y88N7cli9yMPLv+n8RYafv+/F+mk1x5+TxpWP5rW4YFl7FBQ7aKi0aayVsE8GEvRCdFD/E0PzxSMHZOvKg3z2rpeRkzI6HZZtOXd6JuNvyOStRxt494mObyi+6zM/j0+povcQJ9P+kk9aRufbfWguvQR9MpCgF6KDMvMcFB3t+FrQf7rEQ9Df+YukoqEoCj+6P4dTLkhnwa9q+XSJp93PUb03yJxLK0lLV5j+UgHZhbGJBLk6NrlI0AvRCc2XQli9yEPRQAeDTovP+vEOp8K1T+Zz9AgXf/6fKrZ9HP0ql546m8cuq6RmX5Cf/S2/w+vxtOTQRVMyIJsMJOiF6ITiUhe7Pw/g99o0VAfZ8LaXkRO7vmwTKSNb4acL8snp7eCxH1VxYFvb4RoM2Dx5bRVfrfZz7ZP5DDqtY9MoWyOlm+QiQS9EJxQPdxH0w55NAda+5sXvhZEdWHu+s/L7OZn+UgE+r82jl1RSV9F6wNq2zd9m1PLpEi+XPZDDqRfGvsyUnqmQXaTIRVNJQoJeiE4oaVrzZp2f1Qs95Pd3MGR0Yrb963+Ci5+8kM++zQHmXlGFz9PytMu3H2vg3ccbwoO5hy/fECuFJU5Z2CxJSNAL0Qn9hjpxuGDLCh/r3vAwcmJGmxtzdKXjz07nqj/m8fm/fTw3vfqwZY1XL2rkpVtrGTkpgx/e07Xr0BQUy9WxyUKCXohOcKUr9BvqZJnViK8xPrNt2jL6cjcT78hm+XwP//ht3cH7Ny/38dSPqxk8ysU1f8rr8jckuTo2eSR+a3khurnQUggecnorHDc2toOaHTXhl1ns3xrg1Qfq6T3IydCz0/jjZZUUFDv56V8LSM/s+k8dhSVOavfb+Bpt0tyJ+5QjJOiF6LSS4S5WvRQu2ziTI9AURWHqw7lUlAX5y001FBQ7sINw44v55PaJzwf5pimWlbuC9BkSu6mbov2kdCNEJx09ItRfOv2S+M+2ORJnmsL1z+bR/0QX1XuD/PSv+fQbGr++XWFJeIqllG8STnr0QnRS6XfSue2DQgaekhxlm0iZeQ7+d2kBNfuC9D02vv/dD140JQOyCRfVv7ym6hOARwAnMM+0jNnNjh8NPAMUhM+ZYVrGkvCxWwltHh4AbjItY2nsmi9E4imKkpQh3yQzz0FmXvw/vB9aBkF69InW5r9+eHPvOcD5QCkwRVP10manzSS0l+xIQpuHPxZ+bGn49nBgAvBY02bhQojU5s514M5TZIplEojmbX40sMm0jM2mZXiB+cCkZufYQF7463xgZ/jrScB80zI8pmVsATaFn08I0QMU9JctBZNBNKWbEmB7xO0dwJhm59wFvK6pugZkA9+JeOxHzR5b0vwbaKo+DZgGcMPMm0E2BxciJYS2FJTSTaLFanRmCvC0aRkPaqp+FmBpqn5StA82LWMuMBdg667y2O6NJoRImIJiJzs3eBPdjB4vmtJNGTAw4vaA8H2RrgMWAJiW8SHgBnpH+VghRIoqLHFQvTtIwCf9t0SKpke/AhiqqfoQQiE9GZja7JyvgHOBpzVVP5FQ0O8DFgPPa6puAMXAUGB5jNouhEhyBcVObBuq9gQpGiDzMBKlzR69aRl+YDqwFNhAaHbNOk3VZ2mqPjF82v8C12uqvgZ4AbjGtAzbtIx1hHr664HXgBtNy5CCnRA9hOw0lRyU5qvbJdrWXeX2YBmMFSIl7Fjr456xFVxv5XFaAtbp70k2btu7atigvqNaOiZLIAghukzTTlMyxTKxJOiFEF0mu0ghzS3r3SSaBL0QossoikJBiVPWu0kwCXohRJcqLHZQKaWbhJKgF0J0qYISB5W7pHSTSBL0QoguVVjspHJnkGAwuWb49SQS9EKILlVQ7CDgg9r9EvSJIkEvhOhSh6ZYSvkmUSTohRBd6uDVsTIgmzAS9EKILlVQ0rSloPToE0WCXgjRpXL7OHC4pEefSBL0Qogu5XAoFBQ7qNwlQZ8oEvRCiC5XWOyUwdgEkqAXQnS5Ark6NqEk6IUQXa6g2EHFzgDJtix6TyFBL4TocoUlTnwNUF8hQZ8IUW0Orqn6BOARwAnMMy1jdrPjDwHfCt/MAvqallEQPhYA1oaPfWVaxkSEED3KoSmWQbKLpH8Zb20GvabqTmAO8F1gB7BCU/XFpmWsbzrHtIxfRJyvASMjnqLBtIwRsWuyEKK7KQxfHVtZFmDASVH1L0UMRfPWOhrYZFrGZtMyvMB8YNIRzp9CaN9YIYQADvXoZe/YxIjmrbUE2B5xewcwpqUTNVUfBAwB3o64262p+krAD8w2LWNhC4+bBkwDuGHmzSB7xgqRUvL7OVAccnVsosT6M9Rk4EXTMiL/NQeZllGmqfoxwNuaqq81LePLyAeZljEXmAuhzcFj3CYhRII50xTy+soUy0SJpnRTBgyMuD0gfF9LJtOsbGNaRln4783Au3y9fi+E6CEKShyypWCCRNOjXwEM1VR9CKGAnwxMbX6SpuonAIXAhxH3FQL1pmV4NFXvDYwDHohFw4UQ3UthsZM9X/gT3Yweqc0evWkZfmA6sBTYACwwLWOdpuqzNFWPnCo5GZhvWkZk6eVEYKWm6muAdwjV6NcjhOhxpEefOEqyXam2dVe5PVgGY4VIOa8/VMff76jjoZ29cefKXPpY27ht76phg/qOaumYvNpCiLgoKAnPpZdefdxJ0Ash4qKg+NDVsSK+JOiFEHFRWNy0paDMpY83CXohRFwc3CRcevRxJ0EvhIiLNLdCTi9FevQJIEEvhIibghInFXJ1bNxJ0Ash4qZQ9o5NCAl6IUTcFJQ4pXSTABL0Qoi4KSh2UHvAxteYXBdqpjoJeiFE3BycYinLFceVBL0QIm6aro6VKZbxJUEvhIibQxdNSdDHkwS9ECJuDi6DIAOycSVBL4SIG3eug8x8RaZYxlnA4asAAA6pSURBVJkEvRAirgqKZUvBeJOgF0LEVUGxUzYJj7OoNgfXVH0C8AjgBOaZljG72fGHgG+Fb2YBfU3LKAgfuxqYGT72W9MynolFw4UQ3VNhsYOd62RLwXhqs0evqboTmAOcD5QCUzRVL408x7SMX5iWMcK0jBGACbwcfmwRcCcwBhgN3BneR1YI0UMVlDio3hMk4JOLpuIlmtLNaGCTaRmbTcvwAvOBSUc4fwrwQvjr84A3TMsoNy2jAngDmNCZBgshurfCYie2DVW7pU4fL9GUbkqA7RG3dxDqoR9GU/VBwBDg7SM8tqSFx00DpgHcMPNmkD1jhUhZBSVNV8cGKRroTHBreoaoavTtMBl40bSMdo20mJYxF5gLoc3BY9wmIUQSKTx4dWwASEtsY3qIaEo3ZcDAiNsDwve1ZDKHyjbtfawQogcokKtj4y6aHv0KYKim6kMIhfRkYGrzkzRVPwEoBD6MuHspcG/EAOz3gFs71WIhRLeWVaCQlilXx8ZTmz160zL8wHRCob0BWGBaxjpN1Wdpqj4x4tTJwHzTMuyIx5YDdxN6s1gBzArfJ4TooRRFobDYSaUsbBY3im0nV0l8665ye7AMxgqR0h66sAK/B255U2Zbx8rGbXtXDRvUd1RLx+TKWCFE3BWWOGVN+jiSoBdCxF1BeO/YYDC5KgqpSoJeCBF3hSUOgn6o2Sd1+niQoBdCxF1BcWguvUyxjA8JeiFE3B3agESCPh4k6IUQcdd0dawMyMaHBL0QIu5yeis406RHHy8S9EKIuHM4lNDMG+nRx4UEvRAiIQrk6ti4kaAXQiREYYmDCgn6uJCgF0IkREF/B5VlAZJtGZZUJEEvhEiIghInvkaoky0oupwEvRAiIQoP7jQlA7JdTYJeCJEQTVfHyhTLridBL4RIiMKIvWNTTX1lkIA/eUpSEvRCiITI6+dAcaRe6ab2QJA7Tj3A/eMr2LclOX62qDYH11R9AvAI4ATmmZYxu4VzLgPuAmxgjWkZU8P3B4C14dO+Mi1jYvPHCiF6HqdLIf8oR8qVbt54uJ76CptgMMB955Rz9eN5nPr9jIS2qc0evabqTmAOcD5QCkzRVL202TlDCe0FO860jOHAzRGHG0zLGBH+IyEvhDgo1a6Ordod4J0n6jnj8gxu+3cRfY5x8viUKl6eWZvQUk40pZvRwCbTMjabluEF5gOTmp1zPTDHtIwKANMy9sa2mUKIVFRQ7EypHv2rv6sn4IPv35pN78FOfvl6Id/4cSZvPFLPwxdWUrU7MW9q0ZRuSoDtEbd3AGOanXM8gKbqHxAq79xlWsZr4WNuTdVXAn5gtmkZC5t/A03VpwHTAG6YeTPInrFC9AiFJQ4+e6f9QR/w2yiO0Jo5yWL/1gDvP9XAuKvc9DkmFK1pboUpD+Vy7Jlp/OWmau4dV8G1T+Ux7BvpcW1brAZjXcBQYDwwBfiTpuoF4WODTMsYBUwFHtZU/djmDzYtY65pGaNMyxiVk5cToyYJIZJdQbGDxhqbhurown7nBj8v3lrDjKH7uW3YAT5/39vFLYzeP++rQ3HABb/OPuzY6MvdzHiviKwChUcuquS139fFdRvFaIK+DBgYcXtA+L5IO4DFpmX4TMvYAnxOKPgxLaMs/Pdm4F1gZCfbLIRIEYfWpW896Buqgvz7yQbu/1Y5d48u590nGjhubDruXIWHL6xkqRHf0GzJrs/8/Gd+I+OnZR68PqC5/ie4+PV7hZz+wwwW/V8df7ysirry+JStoindrACGaqo+hFDATybUO4+0kFBP/ilN1XsTKuVs1lS9EKg3LcMTvn8c8EDMWi+E6Naadpqq3Bmk/wmH7g8Gbb5438cyq4HVizz4GqC41Mmls3MYfZmb3D4OGmuCPDe9hoV31vHlRz6ufiKP7MLEzBh/5Z460rMUztMP781Hcuc4uPbJPI49q4EXZ9Ry7znlTLPyGXRaWpe2r81XxbQMPzAdWApsABaYlrFOU/VZmqo3zaJZChzQVH098A5wi2kZB4ATgZWaqq8J3z/btIz1XfGDCCG6n6YefUVZaJCyfEeAJQ/UceeIAzx8YSVrX/Vy1hWZzHivkJkfFXHujVnk9gnFljvXwXVP53HZ73JY/6aX+84pZ9tqX9x/hq8+8fHxQg/nTs8kp3fbbzSKojB+Wha/fL0QbPj9dyt4b159ly7upiTbynFbd5Xbg2UwVogeweexuan3Pk6ekE7ADxve8mLbMGx8GmPVTEZclEF6ZtsDrpuX+5h3dRU1e4Nc9rtczv4fN4oSn4HaRy+pZOsKH3ev7UVmfvs+UdQeCPL0tGrWve5l9OUZTHk4F3dOxz6VbNy2d9WwQX1HtXQsqgumhBCiK6RlKOT1c7D2NS9FAx1cMCOLM6dm0ntwy3Xu1hwzOo3b/l3EU9dX8/zPa/jyIx9THsolI7trw/6LD7yse93LxbOy2x3yADm9HPzsb/ksfbCef/y2jj1fBPjVO4Uxn00kQS+ESKgfP5OH3wvDvpnWqYDL6e3gxpfyee139bxyTx1ffeJj2nP5HHV818ScbdssnlVHXj8H42/I6vDzOBwK59+SzZAz0qg9EOySKaOy1o0QIqGGjkvnxG+lxyTgHA6FC36djbawgJq9QWZ/s4KVLzXGoJWH2/CWl03LfFzw6yzSszrf9hPGpzPqEncMWnY4CXohRMo58dvp3PZBESWlTv58TTV/vaUGvzd245G2bbPw/+roNcjBuKszY/a8XUWCXgiRkgpLnOivFXLujZm8+3gDD55XQfn22CxBsHqxh+2f+Lnw1mxc6clzdW5rJOiFECnLmaZw6excrn8uj90bA9w7rpx1b3g69ZzBgM0/7q7jqGFOxkzumlJLrEnQCyFS3mmT3Mx4r5CCYgdzLqniH/fUEgx0rJSz/K+N7N4Y4KKZ2Ticyd+bBwl6IUQP0W+oi1+9XcSZU90smV2PeXEl1fvatwSB32vzyr11DBzhYsTExK4x3x4S9EKIHiM9S+Gqx/NQ5+Ty5Yc+7ju7nC8/jH5htA+ebeDAtiCT7shOqpUz2yJBL4ToccZelcktbxWS5lYwLqjkrUfbXoLAW2/z6v31HHtWGqXfie8yw50lQS+E6JEGnpLGjPcKOXlCOi/eWsvcK6tpqGq9lPPu3HqqdgeZdGd23JZXiBUJeiFEj5VV4OCG5/O55J4cPv2nh/u+WcGOtYcvjNZQFeR1o57S76YzdFz36s2DBL0QoodTFIXv3JTFL5YU4K2zeeDbFSyzGr52zltz6qmrsJl4+5GXIU5WEvRCCAEcNzZ0Ne0xY9KwflaD9bNqvA02tfuDvGk2MHJSBoNGdu268V1FFjUTQoiwvL4OblpUwCv31vHqA/V89Ymf4lIn3nqbi2Z2z948SNALIcTXOJwKE2/P4ZjRaTx1fTU71vo5c6qb/id037iMquWaqk8AHgGcwDzTMma3cM5lwF2ADawxLWNq+P6rgZnh035rWsYzMWi3EEJ0qZPOy+C294t4+7F6vvvzji9DnAzarNFrqu4E5gDnA6XAFE3VS5udMxS4FRhnWsZw4Obw/UXAncAYYDRwZ3gfWSGESHq9jnbyo9m5FPRv30YoySaawdjRwCbTMjabluEF5gOTmp1zPTDHtIwKANMy9obvPw94w7SM8vCxN4AJsWm6EEKIaERTuikBtkfc3kGohx7peABN1T8gVN65y7SM11p5bEnzb6Cp+jRgGsANM28G2TNWCCFiJlajCy5gKDAeGAD8S1P1k6N9sGkZc4G5ENocPEZtEkIIQXSlmzJgYMTtAeH7Iu0AFpuW4TMtYwvwOaHgj+axQgghulA0PfoVwFBN1YcQCunJwNRm5ywEpgBPaarem1ApZzPwJXBvxADs9wgN2gohhIiTNnv0pmX4genAUmADsMC0jHWaqs/SVH1i+LSlwAFN1dcD7wC3mJZxwLSMcuBuQm8WK4BZ4fuEEELEidLW0pzxtnVXuT1YBmOFEKJdNm7bu2rYoL6jWjoma90IIUSKS7prej1e//6N2/Zu6+jj62tqe2fl5uyPZZu6I3kdQuR1CJHXISTFX4dBrR6xbTul/ky/8hcrE92GZPgjr4O8DvI6yOvQ9EdKN0IIkeIk6IUQIsWlYtDPTXQDkoS8DiHyOoTI6xDSI1+HpJteKYQQIrZSsUcvhBAiggS9EEKkuKSbR99R0eyC1RNoqr4VqAECgN+0jBavlEtFmqo/CXwf2Gtaxknh+4qAvwKDga3AZU37JqSqVl6HuwjtG7EvfNptpmUsSUwL40NT9YHAs0A/QjvfzTUt45Ge+DuREj36aHbB6mG+ZVrGiJ4U8mFPc/jGNjOAt0zLGAq8Fb6d6p6m5Q1+Hgr/XoxI9ZAP8wP/a1pGKXAmcGM4F3rc70RKBD3R7YIlUpxpGf8Cmi+aNwlo2qf4GeAHcW1UArTyOvQ4pmXsMi3j4/DXNYQWZSyhB/5OpErpJppdsHoKG3hdU3UbeCK8qUtP1s+0jF3hr3cT+hjfU03XVP0qYCWhnm5Klysiaao+GBgJ/Ice+DuRKj16ccjZpmWcRqiMdaOm6t9IdIOShWkZNqE3wp7oj8CxwAhgF/BgYpsTP5qq5wAvATebllEdeayn/E6kStDLTlZhpmWUhf/eC/ydUFmrJ9ujqXp/gPDfe9s4PyWZlrHHtIyAaRlB4E/0kN8LTdXTCIX8X0zLeDl8d4/7nUiVoD+4C5am6umEdsFanOA2xZ2m6tmaquc2fU1oR6//JrZVCbcYuDr89dXAogS2JWGagi3sYnrA74Wm6grwZ2CDaRlGxKEe9zuRMlfGaqp+AfAwoemVT5qWcU+CmxR3mqofQ6gXD6Hxl+d70uugqfoLhDao7w3sAe4ktM3lAuBoYBuhqXQpPVDZyuswnlDZxiY0pfCGiDp1StJU/Wzg38BaIBi++zZCdfoe9TuRMkEvhBCiZalSuhFCCNEKCXohhEhxEvRCCJHiJOiFECLFSdALIUSKk6AXQogUJ0EvhBAp7v8BUSDeZq0MjoQAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nStVTg8Yka5N"
      },
      "source": [
        "# Filter optimization\n",
        "---"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4HRJvYLiH686",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "43102ef8-5f9f-4130-98d5-0b300a2b6778"
      },
      "source": [
        "controls = []\n",
        "for amp, (width,mean) in zip(max_amps_list, product(width_values, mean_values)):\n",
        "  print(amp,width,mean)\n",
        "  I = np.array([gaussian_pulse(t, amp, mean, width) for t in t_values])\n",
        "  Q = np.zeros(segments+1)\n",
        "  values = I + 1j*Q\n",
        "  controls.append({\"duration\": total_duration, \"values\": values}) #, \"mean\": mean, \"width\": width})\n",
        "# pprint(controls)"
      ],
      "execution_count": 609,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1.0 1.5625 12.5\n",
            "0.989909090909091 1.5625 17.5\n",
            "1.0 1.5625 22.5\n",
            "0.9798181818181818 1.5625 27.5\n",
            "1.0 1.5625 32.5\n",
            "0.989909090909091 1.5625 37.5\n",
            "0.989909090909091 3.125 12.5\n",
            "1.0 3.125 17.5\n",
            "0.989909090909091 3.125 22.5\n",
            "0.9798181818181818 3.125 27.5\n",
            "1.0 3.125 32.5\n",
            "0.9596363636363637 3.125 37.5\n",
            "0.8082727272727274 4.6875 12.5\n",
            "0.889 4.6875 17.5\n",
            "0.8385454545454546 4.6875 22.5\n",
            "0.8082727272727274 4.6875 27.5\n",
            "0.8385454545454546 4.6875 32.5\n",
            "0.878909090909091 4.6875 37.5\n",
            "0.6367272727272728 6.25 12.5\n",
            "0.6468181818181818 6.25 17.5\n",
            "0.6266363636363637 6.25 22.5\n",
            "0.6064545454545455 6.25 27.5\n",
            "0.6569090909090909 6.25 32.5\n",
            "0.6367272727272728 6.25 37.5\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g-OvAwzi1VrB",
        "outputId": "03d7646c-b180-483d-94b7-b7686c70e10a"
      },
      "source": [
        "print(len(controls[-1][\"values\"]))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "256\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UJNLSmiTPHA4",
        "outputId": "91dc7aa0-d88e-44d6-f8d3-6a700620159c"
      },
      "source": [
        "wm_arr = np.array([a[0] for a in list(product(width_values, mean_values))])\n",
        "print(wm_arr.shape)\n",
        "shape = (len(width_values), len(mean_values))\n",
        "print(wm_arr.reshape(shape))"
      ],
      "execution_count": 610,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(24,)\n",
            "[[1.5625 1.5625 1.5625 1.5625 1.5625 1.5625]\n",
            " [3.125  3.125  3.125  3.125  3.125  3.125 ]\n",
            " [4.6875 4.6875 4.6875 4.6875 4.6875 4.6875]\n",
            " [6.25   6.25   6.25   6.25   6.25   6.25  ]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XMq_aI2MOG66",
        "outputId": "6c39372c-2130-4cf3-be60-9fcc9c879f0d"
      },
      "source": [
        "list(zip(max_amps_list, product(width_values, mean_values)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(1.0, (1.5625, 12.5)),\n",
              " (1.0, (1.5625, 17.5)),\n",
              " (0.989909090909091, (1.5625, 22.5)),\n",
              " (1.0, (1.5625, 27.5)),\n",
              " (0.9697272727272728, (1.5625, 32.5)),\n",
              " (0.989909090909091, (1.5625, 37.5)),\n",
              " (0.9798181818181818, (3.125, 12.5)),\n",
              " (0.9798181818181818, (3.125, 17.5)),\n",
              " (0.989909090909091, (3.125, 22.5)),\n",
              " (1.0, (3.125, 27.5)),\n",
              " (1.0, (3.125, 32.5)),\n",
              " (0.9697272727272728, (3.125, 37.5)),\n",
              " (0.8284545454545454, (4.6875, 12.5)),\n",
              " (0.778, (4.6875, 17.5)),\n",
              " (0.7477272727272728, (4.6875, 22.5)),\n",
              " (0.8486363636363636, (4.6875, 27.5)),\n",
              " (0.8587272727272728, (4.6875, 32.5)),\n",
              " (0.7477272727272728, (4.6875, 37.5)),\n",
              " (0.6266363636363637, (6.25, 12.5)),\n",
              " (0.5963636363636364, (6.25, 17.5)),\n",
              " (0.6266363636363637, (6.25, 22.5)),\n",
              " (0.6064545454545455, (6.25, 27.5)),\n",
              " (0.6064545454545455, (6.25, 32.5)),\n",
              " (0.6367272727272728, (6.25, 37.5))]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 548
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "CTCZPlDUMzBo",
        "outputId": "5be0b745-ec43-4308-a04e-99f918a22c6c"
      },
      "source": [
        "plot_controls(\n",
        "    figure=plt.figure(),\n",
        "    controls={\n",
        "        \"$\\Omega$\": [\n",
        "            {\"duration\": total_duration / segments, \"value\": value} for value in controls[10][\"values\"]\n",
        "        ]\n",
        "    },\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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t0jrca1z+3i38IbEX787oP8+WlrFxu9rOwpd2nLcE4QPF7344LNjqbwzy/rdLK05ZtE5NIQnXIu0kfBdwpu8FVxPuRvQD4E7rzIsDLLoBDH3FLOvM3wxXZBlY+0zfvNCCeR2xnWzX4ytaHGmV1uFeI5mAOxbChufCpDV31vS4XW1n4csqToXL37OFd11RrH+7tOKURWtp9hO2zlwHXOd7wXjgLYQrWF3ie8FvCRPyj6wzeyzyWvV4ub+YNe9sfS+YTLimc2ui/4DJXgjRXCxZPn2P/zibBW1zODrIZCkQ68wOwsfCt/leMIZw7eV3AZ8HFlV1rzxOnkg4D/xwZL8KuJ8aj5d9L/gMcCHh7kyV5zk1V9gSQjQPzVQRXQtVSo8OUk/CvhccRLhl4ULgeeBO4BLgQutMdQKOHy9H6zqfZZ35ZWQfBZw5wKXOAhZYZ/6c7k9QDGbv01bTbtRXtDjSKq2NXmPVHeGylHMPCV/waJ+65/aAA9lZ+LKKU+HHZ7awYEWx/u3SilMmrVmQdnX0UcDNhO8HPwBMJ9wQYREwxTpT8y7V94KHrTOvqjr2W+vMITX632OdeWNq4lNCy1YKkR3J5Smh3EtUDoVbL36eP/0qnPvWUpblZqSqoy8G3muduS9x7BbfCz4FfGOQc5/3veBD1pl/A/C94IOEuxfV4jzfC75D+Lg6XuIk71eU0uLRJ7vi9oJ5HbGdbNfjK1ocaZXWRq6x6o7YTcfCsIBx7qw954QHsrPwZRnnsFN7+FNim8Oi/NulFacsWktTmAXMqUrAAFhnvul7wapBzv0Y4KLE2ks4N9zfK0oV3hl9vZI954SbIgkLIWrTrMVYA6FtDpuTtJPwuP4O+l4wETgJ+M9aJ1pnfg8c6XtBW2R31+ob8W5gvnXmhQa1CiFKxGhdPapjIXQ9ErZVKd18pJ2E7/a94BzrzEWVA74XTAGuI7xr/exAJ/tecCzhq034XnCHdebOAbo/AewcwF8XvhesBroJ76p7rDNH+l6wD7AcmA+sJtyMYlNa1xyIKZPG17Qb9RUtjrRKaz3XqCSgfReFOyWNln1v/zoqT/33xeHc8A/PGMOCH+b/b5dWnDJpzYK0C7PagR8DLYSFWdOA44CrgbdaZw4b4NwvED5+viE6dApwjXXmX2r0/yZwMLCCPeeEB5t7rnX91cCRyfeYfS/4Z2CjdeZi3wuWAi+xzpw9UBwVZgmRPs2yX/BwUJFWuRmRwizrzGbfC15PmHgPIXxF6ULrzB98LxgweQEe8LrKY2jfC74O/BfQbxIm3CziccL3iSukvavSicCbovY1hKuADfZzpMKarr73IDs72mM72a7HV7Q40iqt9VyjkoD3XTSGZ7c8z8zpU3h2y/MAe7QHs7PwjVSc6veGR9tnIM84nR3tZEXq7wlbZ3oJ74Z/XHX8K4OcOiY5D2yd6Y4W+qh1nY8NS+je9AJ3+F7QC3zLOrMMmG2dWRf51xPucTwiPP/Cjpp2o76ixZFWaa3nGgBHfaqX7dHavtsTa/wm24PZWfhGKk6F/oq0RsNnoAhx0iaTFbMa5AHfC74LfDuyPwE8WN3J94IjrDMP1Qrie8EEYH/rzB/qvP5fWWfW+l7QAdzpe8Ee51tneqME3d81FwOLAU479yzQ42ghUmHFEhUjJVGRVvNRpCTsA39PuD1hL/BT4B/76XdOVOx1PfBLwp2bJgILCB+DHw98DqgrCVtn1kbfu3wvuAU4Cnja94I51pl1vhfMAbpqnLsMWAbhnHA91xVC1CaZaJptv+BGODqqEF8ZvTu8YoleWSo7qRZmNYLvBZ+uOlR5BN0L/RdaRStzLSacr+0kXNTjYeAW4MohvN5UHW8KMDZ6BD6FcKnNC4BjgA2Jwqx9rDNfHCiWCrOESAcVY9VGRVrlY6RWzGqEy4CHCJNo9Rxwv38hWGfuJ9zcIS1mE67sBeGYXG+d+YnvBQ8AN/lecCrwJOESnCPC5u6+15/b2ybFdrJdj69ocaRVWge7RiUBV9aHTs7NTZk0PraT7cHsLHx5xEkWaV3yrg2ctWJGU34GihKnvW3v9bzToghJ+OOEryYtIqxAvn6k3sWtYJ15Aji0n+MbCO+GR5ynN/bdzLe3TYrtZLseX9HiSKu01vJVzwMfsXg3AJu39v3nOGXS+NhOtgezs/DlFacyP/zoXeGCgc30GShanCyT8NjMIg8R68zV0U5K7wNmAf/te8FNvhf0u3GDEKK50Tzw0Dg6sYLYkrauUbuiWNnJPQlXsM78H/A14FLCud6jchUkhBhxkolkyfLpeyQasTfJP1JULV1Ocn8cHb0LfCzhBg6LgJuA10ZJedQyd9b0mnajvqLFkVZprfZVEknnoeH9wYxpk2Nfsl2PL604I3GNeuOcEC0d9N0PhxvO/eSsVhbcUu7PQNHjpE3uSRhYA/yZcGnLCwiLsSb5XnAwgHXmd/lJy4+JVevJJu1GfUWLI63SmmxfflLfSkXHnz0FgHGtLfGxZLseX1pxRuIajcbZ7/BW/vSrHv7w07BiuqyfgTLESZsiJOGdwEzg84Tv9yYrpHuBl+chKm8eX7shbi+Y1xHbyXY9vqLFkVZprfZV9gruWAjrN3Yzd9Z01kdFMsl2tT2Qr56+eV9jOHFOWDo9rpauXk2rTJ+BosYp037CdWOdmZ+3BiFEflRXQ2seuDGqV9PSQh7lIPckLIQYvVQnYFVDN07lj5dfXxk+mlahVjlQEi4o06dOrGk36itaHGmV1kqimPOqMbzr3Gls6t62R9/JE8f1267Hl1ackbhGGnFOWDo5fjR9qz+WBSuL/Rko0+c1C3JftrLZ0LKVQgwNLUuZHcllLQEWvW08p9+c3XZ8YnCKvGyl6IfV6zbG7flz9ontZLseX9HiSOvo1frVdzzDE//R98f/vouga1M3HS9po2tTX7FR0m7Ul1ackbhGmlpfc9pudlwG61eF/lV37OCr73iG9367pRCfgTJ9Xlev20iWN1ZKwgWlzHueFuka0lqsOJeftHmPBNyxEI76FOzsCZemrHyvkLQb9aUVZySukabWoz4VtitzxE/8R/17MY/2z2t/dtoUZsUsIUTzsmIJXLKob1OGjoVaEWukOGHplLitpS2Lh+6EC0pLy9iadqO+osWR1tGh9Qef3rMCevZCeN3p4XIAY8fuuXHaQHajvrTilFnr7IXw9CPhv8OStnBb9P3fCF+8Nf/PUtE+r4PFSRsVZqWMCrOE6CNZfLXf4a173JWJkaW6WKuCirZGBhVmlYxnN2+N2zPbp8Z2sl2Pr2hxpLV5tS4/tSdOvBVeesgY3uCHdxTPPR9uxzdtyqS4PZjdqC+tOM2gNRz/8UybMokfXPgcf/5teAO26o4dfO3EZ/GumTgqP69D8c1sn0pWKAkXlA1b+t6XnNk+NbaT7Xp8RYsjrc2l9coPbKu5OETHQjhycS/d23YwbcokureFCTrZHsxu1JdWnGbTeuTiMAFXirb+92e7+fuXbQO2xUteNvPntV6fknCO+F5wHOH2ii3Ad6wzF+csSYhC0LfaVddevo6F4QpOc2dNZ+0zW0ZcmxgaJyydws0XbImXu4Tw3/SSRVD5d02uQy3SR0l4AHwvaAEuB95KuNvTA74XrBytOzuJ0UftRKvE2ywkK9Qrd8ZJqpMyhIn57NtGRl+zo8KsAfC94HXA+daZYyP7HADrzEW1zkmjMKtSvShE0ek8tIV3fmkqO3b2/cc9flxrbCfb9fiKFme0av3hl7ey5je7GO1c0T38XZRUmNUYc4GnEvYa4LXVnXwvWAwsBjj1nM/waMYvdwsxkgz8OHIXsIUJVUcn1GjX4ytanNGo9T3XsRfVm26MBh59MrsbIyXhFLDOLAOWQTp3wld07/mPvmBeR2wn2/X4ihZHWqVVWsupNblPcdG1phUnjf2EayVyrZg1MGuBlyXszuiYEEIIMWx0JzwwDwAH+l6wP2HyPQX4QL6ShBBCNAsqzBoE3wuOBy4hfEXpKuvMhQP1f/TJrmeAJ4d73W3dW2dObpv67HDjNBsal9pobGqjsamNxqZ/MhiXeQvmdcza62hvb6++Cvh1xoc++2DeGor4pXHR2GhsNDbNNC6aExZCCCFyQklYCCGEyAkl4eKyLG8BBUXjUhuNTW00NrXR2PTPiIyLCrOEEEKInNCdsBBCCJETSsJCCCFETigJCyGEEDmhJCyEEELkhJKwEEIIkRNKwkIIIUROKAkLIYQQOaEkLIQQQuSEkrAQQgiRE0rCQgghRE4oCQshhBA5oSQshBBC5ISSsBBCCJETSsJCCCFETigJCyGEEDmhJCyEEELkhJKwEEIIkRNKwkIIIUROKAkLIYQQOaEkLIQQQuSEkrAQQgiRE0rCQgghRE4oCQshhBA5oSQshBBC5ISSsBBCCJETSsJCCCFETigJCyGEEDmhJCyEEELkhJKwEEIIkRNKwkIIIUROKAkLIYQQOaEkLIQQQuSEkrAQQgiRE0rCQgghRE4oCQshhBA5oSQshBBC5ISSsBBCCJETSsJCCCFETrTmLaDZ+ONTz/SOa20ZdpyeXbvjdmvL2NhOtuvxFS2OtEqrtEprWbS2tgz/fnX7jp5nF8zrmFV9vOnvhH0vOM73gkd9L3jM94Kl/fgn+F6wPPL/0veC+dHx+b4XvOB7wa+jr28O5XrjWluYP2efYX/t2rU7/krajfqKFkdapVVapbUsWtP4Px14sr+c0dR3wr4XtACXA28F1gAP+F6w0jrzu0S3U4FN1pkDfC84BfgK8L7I97h15rARFR0xZdL4mnajvqLFkVZplVZpLZPWLBjT29ub6QXyxPeC1wHnW2eOjexzAKwzFyX63B71udf3glZgPTALmAf8yDqzqJ5rrl63sTf6q0cIIYQA4NEnux5aMK/jyOrjTX0nDMwFnkrYa4DX1upjnenxvWALMCPy7e97wa+A54BzrTO/6O8ivhcsBhYDnHbuWZBCEl7TtTlud3a0x3ayXY+vaHGkVVqlVVrLorWzo52saPYkPBzWAftZZzb4XnAEsML3goXWmeeqO1pnlgHLILwTTuPiz7+wo6bdqK9ocaRVWqVVWsukNQuavTBrLfCyhN0ZHeu3T/Q4ejqwwTqz3TqzAcA68xDwOPDKzBULIYQYNTT7nfADwIG+F+xPmGxPAT5Q1Wcl8BHgXuA9wM+sM72+F8wCNlpndvle8HLgQOCJkZMuhBCi2WnqwiwA3wuOBy4BWoCrrDMX+l5wAfCgdWal7wUTAQccDmwETrHOPOF7wUnABcBOYDdwnnXmh4NdT4VZQgghqqlVmNX0SXikSSsJb+5+IW63t02K7WS7Hl/R4kirtEqrtJZFa3vbJIbLaK2OLi1Pb+yO2+1tk2I72a7HV7Q40iqt0iqtZdGaRhKuRbMXZgkhhBCFRUlYCCGEyAnNCadMWnPCW7dtj9tTJ0+I7WS7Hl/R4kirtEqrtJZF69TJExgumhMuGRPHt9a0G/UVLY60Squ0SmuZtGaBknBBeXzthri9YF5HbCfb9fiKFkdapVVapbUsWhfM6yArNCcshBBC5ISSsBBCCJETehxdUKZPnVjTbtRXtDjSKq3SKq1l0poFqo5OGS1bKYQQohpVR5eM1es2xu35c/aJ7WS7Hl/R4kirtEqrtJZFa5Y3VkrCBWX7jp6adqO+osWRVmmVVmktk9YsUGGWEEIIkRO6Ey4oLS1ja9qN+ooWR1qlVVqltUxas0CFWSmjwiwhhBDVqDCrZDy7eWvcntk+NbaT7Xp8RYsjrdIqrdJaFq0z26eSFUrCBWXDlm1xe2b71NhOtuvxFS2OtEqrtEprWbRmmYRVmCWEEELkhJKwEEIIkRMqzEqZtAqzXty+M25PnDAutpPtenxFiyOt0iqt0loWrRMnjGO41CrM0p2wEEIIkROZFWb5XtABGGA/68wbfS84BHi9deabWV2zmXhy/aa4vWBeR2wn2/X4ihZHWqVVWqW1LFrLup/wt4H/BNoj+w/ApzO8nhBCCFEqskzCc6O73l0A1pkdwO6hnOh7QUuGuoQQQohCkOV7wnuseu17QTswplZn3wv+GvgkcAzQ4XvBTuC3wM3AMuvMplrnNiMzpk+uaTfqK1ocaTi5JioAABA/SURBVJVWaZXWMmnNgsyqo30v+CLwCsKk+k+Ej6Kvs85c2k/f24AXgRuBe4H1wETgIOBY4N3Al6wzP8lEbIpo2UohhBDVjPiyldaZf/a94IOEc8LHA1+3zvxbje6ft878rurYTuB+4H7fCy4G5mWltYg8tubZuH1A58zYTrbr8RUtjrRKq7RKa1m0HtA5k6zIdNlK68x1wHVD6FedgKv9O4HH0tJVBnbt2l3TbtRXtDjSKq3SKq1l0poFqSdh3wv+eSC/deaL/ZzzAFDzubh15qgUpAkhhBCFIos74ecbOOfz0fcTgL8ArozsjwGPpiGqbEwY31rTbtRXtDjSKq3SKq1l0poFhVq20veCXwJHW2d6I7sF+G/rzGuHGfc44FKgBfiOdebiKv8E4FrgCGAD8D7rzOrIdw5wKuGrVp+xztw+0LVUmCWEEKKaYRVm+V5wEPBFYCHhne6dwCXAD60zx9Q4p7/H0luAe60zP6txqX0Iq6JfiOwJ0bGGiRL55cBbgTXAA74XrKyahz4V2GSdOcD3glOArwDv873gYOAUwp/7pcBPfS94pXVm13A0DYX1G56L2/vOmBbbyXY9vqLFkVZplVZpLYvWfWdMIysGTcK+FxxF+K7uZYADpgMnA78Epgxw6mzgDcCKyD4ReAA42feCm6wzF/ZzznLgXt8Llkf2yYSvLQ2Ho4DHrDNPAPhecGOkJZmETwTOj9rfAy7zvWBMdPxG68x24P98L3gsinfvMDUNyJK2rqojL9Zo1+MrWhxplVZpldZyaL2iO8ckDFwMvNc6c1/i2C2+F3wK+MYA570UOKKyyIbvBRcQJvO/IkzgeyVh68y50SPpN0WHzrXO3DoEjQMxF3gqYa8Bqh9vx32sMz2+F2wBZkTH76s6d271BXwvWAwsBjj1nM/w6I6e6i5CCCFKyqNPVt8YpcdQkvCcqgQMgHXmm74XrBrgvLnJVa6sM5t9L5hjnen2vWB7rZOsMz8EfjgEXYXBOrMMWAbpzAlf0b3nP/qCeR2xnWzX4ytaHGmVVmmV1rJoTWMDh1qJfNDCLN8LHrPOHNDP8YnARdaZz9Y473vARuC70aGPALOADwD/ZZ3Za4La94LpwNnAYYRzwwBYZ948oMiB9b8OON86c2xknxPFvCjR5/aoz72+F7QSrtg1C1ia7JvsV+t6aRVm9fT0TTu3trbEdrJdj69ocaRVWqVVWsuitbV1+NsZDKcw627fC86pSlpTCBfheCfQbxIGPg78P8K5ZIC7CRPsLuDtNc65inCu9pXA30cxHhqCxoF4ADjQ94L9gbWEhVYfqOqzkvCPhHuB9wA/s870+l6wErje9wJD+Hj9QMJVvDLnxcQj7amtLbGdbNfjK1ocaZVWaZXWsmidmkISrsVQkvDngR/7XvBuwoQ2DTgOuBp4uNZJ1pnn6Hv/t5pnahw/wDpzku8FJ1pnbvC94PvAz4egsSbRHO8ZwO2EryhdZZ15JJqjftA6s5LwvWQXFV5tJEzURP1uIvzDoAc4fSQqowHWPrMlbi+Y1xHbyXY9vqLFkVZplVZpLYvWNB5H12LQJBzN5b6eMPEeQviK0oXWmT/4XnB2rfOix9UfJNzEIb5OfytmJajMFe/wvWAfYBPhY+FhYZ25Dbit6tj/S7RfBN5b49wL6aeITAghhBguQ3pPOFo848fRV/L4VwY47d+B8YSV0DULsar43yj5Xk9YlbyZ4T+OFkIIIQpJlutxHWCdOaieE6wzH4qaxveC+wl3YCr89oVZMHuftpp2o76ixZFWaZVWaS2T1izIcj/hW4FTrDPdQ+zfAjxgnXl1JoJGCC1bKYQQoppa1dFZJuHrCddivp3EMiQDzQn7XnAP8LZojraUpJWEi/iuXJpxpFVapVVay6I1jcKs4byi1PA12XsHpDcO4Zx7oneMt1YOWmcGWplLCCGEKCWZJWHrzD8A+F4wh3BLwo8CY4ag5xEgOZdcnG2ehBBCiBTJJAlHq06dSLjYxtHRdY7tb/nLJNaZj2Whp4xMmTS+pt2or2hxpFVapVVay6Q1C1KfE/a94GvA+4HfEi7ocQvwO+vM/gOcc4R1puarSNF+v/tbZ/6QqtgMUGGWEEKIakZyTvg0wuUfL7LO/BzA94LBMv050VKY1xO+V/w04drRCwgXCTke+BxQ+CScFmu6Nsftzo722E626/EVLY60Squ0SmtZtHZ2tJMVWSThlxKuzfzVaOGNawe7jnXmPdG+xYuB84BOYBvhspi3AG8Y6qtOzcLzL+yoaTfqK1ocaZVWaZXWMmnNgtSTsHVmM+E+w9/wveAQwnnhidHrR9dZZ75V47z7GaHNEYQQQogiMDbL4NaZ31pnzgLmApawWEsIIYQQZLhYx2hFhVlCCCGqyWOxDjEMNne/ELfb2ybFdrJdj69ocaRVWqVVWsuitb1tElmhJFxQnt7YV4fW3jYptpPtenxFiyOt0iqt0loWrVkm4UznhIUQQghRGyVhIYQQIidUmJUyaRVmbd22PW5PnTwhtpPtenxFiyOt0iqt0loWrVMnT2C4qDCrZEwc31rTbtRXtDjSKq3SKq1l0poFSsIF5fG1G+L2gnkdsZ1s1+MrWhxplVZpldayaE1jP+FaaE5YCCGEyAklYSGEECIn9Di6oEyfOrGm3aivaHGkVVqlVVrLpDULVB2dMlq2UgghRDWqji4Zq9dtjNvz5+wT28l2Pb6ixZFWaZVWaS2L1ixvrJSEC8r2HT017UZ9RYsjrdIqrdJaJq1ZoMIsIYQQIid0J1xQWlrG1rQb9RUtjrRKq7RKa5m0ZoEKs1JGhVlCCCGqUWFWyXh289a4PbN9amwn2/X4ihZHWqVVWqW1LFpntk8lK5SEC8qGLdvi9sz2qbGdbNfjK1ocaZVWaZXWsmhVEm4Q3wv2AZYD84HVwMnWmU399PsIcG5k/pN15pro+N3AHOCFyPc260xXtqqFEEKMFpq9OnopcJd15kDgrsjegyhRnwe8FjgKOM/3gpckunzQOnNY9KUELIQQIjWaujDL94JHgTdZZ9b5XjAHuNs6s6Cqz/ujPqdF9reifjdEd8Kft848ONRrplWY9eL2nXF74oRxsZ1s1+MrWhxplVZpldayaJ04YRzDZbQWZs22zqyL2uuB2f30mQs8lbDXRMcqfNf3gl3AzYSPqvf6q8X3gsXAYoDTzj0LVB0thBBiCJQ+Cfte8FNg335cf5c0rDO9vhfUe9v/QevMWt8L2giTsAdcW93JOrMMWAbhnXCd1+iXJ9f3TV0vmNcR28l2Pb6ixZFWaZVWaS2L1iz3Ey59ErbOvKWWz/eCp30vmJN4HN3fnO5a4E0JuxO4O4q9Nvre7XvB9YRzxnslYSGEEKIRSp+EB2El8BHg4uj7D/rpczvw5UQx1tuAc3wvaAXarTPP+l4wDngH8NMR0CyEEGKU0OyFWTOAm4D9gCcJX1Ha6HvBkcCnrDOfiPp9HPhSdNqF1pnv+l4wBbgHGAe0ECbgwDqza6BrPvpk1zPRtYbFtu6tMye3TX12uHGaDY1LbTQ2tdHY1EZj0z8ZjMu8BfM6Zu11tLe3V18F/DrjQ599MG8NRfzSuGhsNDYam2Yal2Z/T1gIIYQoLErCQgghRE4oCReXZXkLKCgal9pobGqjsamNxqZ/RmRcmrowSwghhCgyuhMWQgghckJJWAghhMiJZl+so3T4XnAccCnhu8nfsc5cnLOk3PC94CrCRVK6rDOLomND2p6y2fG94GWEq7fNBnqBZdaZS0f7+PheMJHw/f4JhP+/fc86c57vBfsDNwIzgIcAzzqzIz+l+eF7QQvwILDWOvMOjU2I7wWrgW5gF9BjnTlyJH6fdCdcIKJfjsuBtwMHA+/3veDgfFXlytXAcVXHBt2ecpTQA3zOOnMwcDRwevRZGe3jsx14s3XmUOAw4DjfC44GvgJ8zTpzALAJODVHjXlzJvD7hK2x6eNvom1rK7sdZf77pCRcLI4CHrPOPBH9JXojcGLOmnLDOnMPsLHq8InANVH7GuBdIyqqIFhn1lln/idqdxP+pzqXUT4+1ple68zWyBwXffUCbwa+Fx0fdeNSwfeCTuAE4DuRPQaNzUBk/vukx9HFor9tFV+bk5aiMpTtKUcVvhfMBw4HfonGp/JE6SHgAMInS48Dm60zPVGX6u1KRxOXAF8E2iJ7BhqbCr3AHdFue9+KdsfL/PdJd8KitER7O4/qd+x8L5hKuM3mWdaZ55K+0To+1pld1pnDCHdEOwr4i5wlFQLfCyr1FQ/lraWg/JV15tWE04Gn+17wxqQzq98nJeFisRZ4WcLujI6JPp6OtqVkgO0pRwXR7l43A9dZZ74fHdb4RFhnNgM/B14HtEc7o8Ho/b36S+BvowKkGwkfQ1+KxgbYY+vaLuAWwj/gMv99UhIuFg8AB/pesL/vBeOBUwi3YxR9VLanhNrbUzY90VzelcDvrTMm4RrV4+N7wSzfC9qj9iTgrYTz5T8H3hN1G3XjAmCdOcc602mdmU/4f8vPrDMfRGOD7wVTfC9oq7QJt7RdxQj8PmlOuEBYZ3p8LziDcI/jFuAq68wjOcvKDd8LbgDeBMz0vWANcB7h3tA3+V5wKtH2lPkpzJW/BDzgYd8Lfh0d+xIanznANdG88FjgJuvMj3wv+B1wo+8F/wT8ivAPGBFyNhqb2cAtvhdAmBevt878xPeCB8j490nLVgohhBA5ocfRQgghRE4oCQshhBA5oSQshBBC5ISSsBBCCJETSsJCCCFETugVJSFGOb4X/JJw16HxwCsJ34+E8HWVp4BHrDPLM7z+SwnXLv4r68zuGn0WAv9inXl7VjqEyAO9oiSEAOI1qB+0zswc4eteAdxjnblhkH4rgEutMz8fGWVCZI/uhIUQNfG94GrCxHyZ7wXnE67DPI3wjvkhwsVB/hWYB3zfOvOF6Lw5gAX2AyYBN1hnvtxP/InAe4HPRvZkwt1qFgI7gUetM5UFEm4APkG4wpMQTYHmhIUQ9XAE8H5gAWFCvphwwftDgI/4XnBg1O9a4OvWmaOic97ue8Fb+4n3GsLtO1+M7GOBadaZg6M9gU9L9L0XOCbtH0iIPNGdsBCiHm63zmwB8L3gt8BvrDPbge2+FzwKvML3gj8TLjc6K1oGEMKt8w4C7qyK1wk8nbB/Axzke8HlwN3ArQnfemC27wXjrDM7U/2phMgJJWEhRD28mGjv6sduJXzC1gu8ZgjJ8gVgYsWwzjwRFWEdQ3iH/WXfC14V3SlPBHYqAYtmQo+jhRCpYp3pBn4BLK0c873gZb4X7NtP94cJH21X+nUCu6wzKwjniWcB+0Tug4DfZqVbiDxQEhZCZMEHgYN9L3jY94KHgeVAe3Un68zjwGbfCyqJ+FXAvb4X/Aa4H7jIOvPnyHcs4f7JQjQNekVJCJErvhe8HzjaOnPmAH3GEyblY6wzG0ZMnBAZozthIUSuRO8H/9H3goH+P5oHfEkJWDQbuhMWQgghckJ3wkIIIUROKAkLIYQQOaEkLIQQQuSEkrAQQgiRE0rCQgghRE78f5bjnF8ykHQvAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 504x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51,
          "referenced_widgets": [
            "812a072c57824cc7817a403ded4a0f0e",
            "57794ce990dc46daaefe600ade0d2309",
            "b2fa30c35a1a4a5eab97deab770f26d3",
            "65c9e4aad22848bdbe910860bbfa42b5",
            "e37e3e68131e44f4b5157eafa58f1f82",
            "d888e438a1c145b2ab90e31b008f0103",
            "30adee50b75344f7b2983f6a1bf37157",
            "cb6c08497d24494f980279c4e0890113"
          ]
        },
        "id": "RH-VVMR98cg7",
        "outputId": "7561f240-ed43-4a6a-adfa-efe74e811349"
      },
      "source": [
        "experiment_results = qctrl.functions.calculate_qchack_measurements(\n",
        "    controls=controls,\n",
        "    shot_count=shot_count,\n",
        ")"
      ],
      "execution_count": 611,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "812a072c57824cc7817a403ded4a0f0e",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Your task calculate_qchack_measurements has started.\n",
            "Your task calculate_qchack_measurements has completed in 7s.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q10wchIbY4YZ",
        "outputId": "e0d1913a-83c9-4fe3-ddcb-d20e5d908a97"
      },
      "source": [
        "measurement_results = experiment_results.measurements\n",
        "print(len(measurement_results))\n",
        "probabilities, errors = [], []\n",
        "for result in measurement_results:\n",
        "  p,e = estimate_probability_of_one(result)\n",
        "  probabilities.append(p)\n",
        "  errors.append(e)\n",
        "probabilities = np.array(probabilities).reshape(shape[::-1])\n",
        "errors = np.array(errors).reshape(shape[::-1])"
      ],
      "execution_count": 612,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "24\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qHV8fBVSLx0u",
        "outputId": "a2537f1a-10a3-4586-b134-c33e340029ac"
      },
      "source": [
        "[a for b in measurement_results for a in b].count(2)"
      ],
      "execution_count": 613,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 613
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "teBSfVw5_DWh",
        "outputId": "3d3bce63-3ebe-4a69-f581-43104fd3a19a"
      },
      "source": [
        "# Plot inputs\n",
        "fig, axs = plt.subplots(1, 4, figsize=(16, 4))\n",
        "axs[0].set_ylabel(\"Measured values, M (a.u.)\")\n",
        "for n in range(len(width_values)):\n",
        "    axs[n].set_title(f\"Pulses with width {width_values[n] * 1:.1f} (a.u.)\")\n",
        "    axs[n].set_xlabel(\"Pulse center (a.u.)\")\n",
        "    axs[n].errorbar(\n",
        "        mean_values,\n",
        "        probabilities[:,n],\n",
        "        yerr=errors[:,n],\n",
        "        fmt=\"s\",\n",
        "    )"
      ],
      "execution_count": 614,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x288 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0TlNGQG8uwe5"
      },
      "source": [
        "## Parameter extraction\n",
        "---"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51,
          "referenced_widgets": [
            "722b3603e9224e1f9208e356272f46b9",
            "dd3c1c33f2634cf6b333a8645fac0297",
            "93ce3e28d1cb42b49ab7f1fd2594cd69",
            "66e734da24704118a67394c02d926faf",
            "3c2e119b631641cc88c71fdaf8a6798f",
            "f915bc633fc84200ade5770b93e12e8d",
            "b7458ae498b24ab6909946e644a0b0f8",
            "2bb5d41bbd2043fa9bb5a379bda30705"
          ]
        },
        "id": "W-PGR2qr7l2R",
        "outputId": "735492c0-632c-42d5-ae29-78bbc3f8c752"
      },
      "source": [
        "with qctrl.create_graph() as graph:\n",
        "\n",
        "  # Parameters to estimate\n",
        "  mu = qctrl.operations.bounded_optimization_variable(\n",
        "      count=1, lower_bound=0.0, upper_bound=total_duration, name=\"mu\"\n",
        "  )\n",
        "  sigma = qctrl.operations.bounded_optimization_variable(\n",
        "      count=1, lower_bound=0.01*total_duration, upper_bound=total_duration, name=\"sigma\"\n",
        "  )\n",
        "  a = qctrl.operations.unbounded_optimization_variable(\n",
        "      count=1, initial_lower_bound=-1.0, initial_upper_bound=1.0, name=\"a\"\n",
        "  )\n",
        "  b = qctrl.operations.unbounded_optimization_variable(\n",
        "      count=1, initial_lower_bound=-1.0, initial_upper_bound=1.0, name=\"b\"\n",
        "  )\n",
        "\n",
        "  # Create Hamiltonian term\n",
        "  # Create a 2D batch of signals with all values of mean and width you want to simulate.\n",
        "  # This batch is preserved through the entire computation, which ends up being faster than\n",
        "  # looping through all values.\n",
        "  alpha = qctrl.operations.pwc_signal(\n",
        "      values=gauss_pulse_operation(\n",
        "          t_values[None, None, :],\n",
        "          mean_values[:, None, None],\n",
        "          width_values[None, :, None],\n",
        "      ),\n",
        "      duration=total_duration,\n",
        "      name=\"alpha\",\n",
        "  )\n",
        "\n",
        "  gauss_filter = qctrl.operations.gaussian_integral_function(std=sigma, offset=mu)\n",
        "  alpha_filtered = qctrl.operations.convolve_pwc(\n",
        "      pwc=alpha, kernel_integral=gauss_filter\n",
        "  )\n",
        "  shift = qctrl.operations.stf_operator(signal=alpha_filtered, operator=0.5 * sigma_x)\n",
        "\n",
        "  # Calculate |0> -> |1> transfer probability\n",
        "  calculated_populations = 1.0 - qctrl.operations.infidelity_stf(\n",
        "      sample_times=t_values,\n",
        "      hamiltonian=shift,\n",
        "      target_operator=qctrl.operations.target(operator=np.array([[0, 1], [0, 0]])),\n",
        "      name=\"infidelities\",\n",
        "  )\n",
        "\n",
        "  # Create measured points\n",
        "  calculated_points = a * calculated_populations + b\n",
        "  calculated_points.name = \"calculated_points\"\n",
        "\n",
        "  # Calculate cost\n",
        "  cost = qctrl.operations.sum(\n",
        "      (calculated_points - probabilities) ** 2.0\n",
        "      / (2.0 * errors ** 2.0),\n",
        "      name=\"cost\",\n",
        "  )\n",
        "\n",
        "  # Calculate Hessian\n",
        "  hessian = qctrl.operations.hessian_matrix(cost, [mu, sigma, a, b], name=\"hessian\")\n",
        "  \n",
        "  # Estimate the parameters\n",
        "  result = qctrl.functions.calculate_optimization(\n",
        "    cost_node_name=\"cost\",\n",
        "    output_node_names=[\"mu\", \"sigma\", \"a\", \"b\", \"hessian\"],\n",
        "    optimization_count=10,\n",
        "    graph=graph,\n",
        "  )\n",
        "\n",
        "  estimated_mu = result.output[\"mu\"][\"value\"][0]\n",
        "  estimated_sigma = result.output[\"sigma\"][\"value\"][0]\n",
        "  estimated_a = result.output[\"a\"][\"value\"][0]\n",
        "  estimated_b = result.output[\"b\"][\"value\"][0]\n",
        "\n",
        "  # Calculate 2-sigma uncertainties (error bars give 95% precision)\n",
        "  hessian = result.output[\"hessian\"][\"value\"]\n",
        "  uncertainties = 2.0 * np.sqrt(np.diag(np.linalg.inv(hessian)))\n",
        "  uncertainty_mu, uncertainty_sigma, uncertainty_a, uncertainty_b = uncertainties\n"
      ],
      "execution_count": 615,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "722b3603e9224e1f9208e356272f46b9",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Your task calculate_optimization has started.\n",
            "Your task calculate_optimization has completed in 24s.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FJUGAnw3WK4T"
      },
      "source": [
        "def population(pulse_widths, pulse_means, filter_sigma, filter_mu):\n",
        "    \"\"\"\n",
        "    Calculates the transfer probability between states |0> and |1> for a\n",
        "    batch of Gaussian pulses with given widths and means, while applying\n",
        "    a Gaussian filter to the pulses with a given cutoff frequency and offset.\n",
        "    \"\"\"\n",
        "\n",
        "    def gaussian_pulse(t, mean, width):\n",
        "        return np.exp(-0.5 * ((t - mean) / width) ** 2.0) * np.sqrt(\n",
        "            0.5 * np.pi / width ** 2.0\n",
        "        )\n",
        "\n",
        "    with qctrl.create_graph() as graph:\n",
        "        alpha = qctrl.operations.pwc_signal(\n",
        "            gaussian_pulse(\n",
        "                t_values[None, None, :],\n",
        "                pulse_means[:, None, None],\n",
        "                pulse_widths[None, :, None],\n",
        "            ),\n",
        "            total_duration,\n",
        "        )\n",
        "\n",
        "        shift = qctrl.operations.stf_operator(\n",
        "            signal=qctrl.operations.convolve_pwc(\n",
        "                pwc=alpha,\n",
        "                kernel_integral=qctrl.operations.gaussian_integral_function(\n",
        "                    std=filter_sigma,\n",
        "                    offset=filter_mu,\n",
        "                ),\n",
        "            ),\n",
        "            operator=0.5 * sigma_x,\n",
        "        )\n",
        "\n",
        "        population = 1.0 - qctrl.operations.infidelity_stf(\n",
        "            sample_times=t_values,\n",
        "            hamiltonian=shift,\n",
        "            target_operator=qctrl.operations.target(np.array([[0, 1], [0, 0]])),\n",
        "        )\n",
        "        population.name = \"populations\"\n",
        "\n",
        "    graph_result = qctrl.functions.calculate_graph(\n",
        "        graph=graph,\n",
        "        output_node_names=[\"populations\"],\n",
        "    )\n",
        "    return graph_result.output[\"populations\"][\"value\"]\n"
      ],
      "execution_count": 616,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34,
          "referenced_widgets": [
            "25d1c0e1b3474b968daa40186a5deda9",
            "e8ef1fc9271c470ca09193661f404818",
            "ff467d260ab6477484dff8a1814a87d7",
            "e8d226519d05420a8ce46e8fe2d3b6b0",
            "207f1f9e7a1a4206921c27950db8cb82",
            "f97fe0d54d3e470cae72ad6bd087ba16",
            "df30ed16337146ad96157bfb710d50b6",
            "19c6dac96413464890675a7b5b8a96b7"
          ]
        },
        "id": "P79l4G0bU95s",
        "outputId": "0669ea7a-9eb8-4285-8f7f-e088b992e061"
      },
      "source": [
        "mean_range = np.linspace(0.1 * total_duration, 0.9 * total_duration, 30)\n",
        "\n",
        "calculated_curves = estimated_b + estimated_a * population(\n",
        "    pulse_widths=width_values,\n",
        "    pulse_means=mean_range,\n",
        "    filter_mu=estimated_mu,\n",
        "    filter_sigma=estimated_sigma,\n",
        ")\n",
        "\n",
        "# ideal_curves = actual_b + actual_a * population(\n",
        "#     pulse_widths=width_values,\n",
        "#     pulse_means=mean_range,\n",
        "#     filter_mu=actual_mu,\n",
        "#     filter_sigma=actual_sigma,\n",
        "# )\n"
      ],
      "execution_count": 617,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "25d1c0e1b3474b968daa40186a5deda9",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\rYour task calculate_graph has completed in 3s.\n",
            "\r"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "4bxpK5yYWe80",
        "outputId": "186ec866-ab15-4ab6-9053-0521a0b821cc"
      },
      "source": [
        "# Print parameter estimates\n",
        "print(f\"estimated sigma = ({estimated_sigma:.3f} ± {uncertainty_sigma:.3f}) au\")\n",
        "\n",
        "print(f\"estimated mu = ({estimated_mu:.3f} ± {uncertainty_mu:.3f}) au\")\n",
        "\n",
        "print(f\"estimated a = ({estimated_a:.3f} ± {uncertainty_a:.3f}) au\")\n",
        "\n",
        "print(f\"estimated b = ({estimated_b:.3f} ± {uncertainty_b:.3f}) au\")\n",
        "\n",
        "# Plot results\n",
        "fig, axs = plt.subplots(1, 4, figsize=(16, 4))\n",
        "axs[0].set_ylabel(\"Measurement values, M (a.u.)\")\n",
        "\n",
        "for n in range(len(width_values)):\n",
        "    axs[n].set_title(f\"Pulses with width {width_values[n]:.1f} au\")\n",
        "    axs[n].set_xlabel(\"Pulse center (au)\")\n",
        "    axs[n].errorbar(\n",
        "        mean_values,\n",
        "        probabilities[:,n],\n",
        "        yerr=2.0 * errors[:,n],\n",
        "        fmt=\"s\",\n",
        "        color=\"C0\",\n",
        "        label=\"Measured values\",\n",
        "    )\n",
        "\n",
        "    axs[n].plot(\n",
        "        mean_range,\n",
        "        calculated_curves[:,n],\n",
        "        \"--\",\n",
        "        label=\"Estimated values\",\n",
        "        color=\"C1\",\n",
        "    )\n",
        "    axs[n].legend(loc=4)\n",
        "\n",
        "plt.savefig(\"filter.png\", dpi=256)\n"
      ],
      "execution_count": 619,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "estimated sigma = (0.500 ± 0.409) au\n",
            "estimated mu = (31.705 ± 0.148) au\n",
            "estimated a = (-0.497 ± 0.016) au\n",
            "estimated b = (0.848 ± 0.006) au\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x288 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Iu_jsOQtEhKk"
      },
      "source": [
        "# Constant Hamiltonian term estimation\n",
        "---"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9GyE6ZoNfvN1"
      },
      "source": [
        "# range of wait times in the different experiments\n",
        "wait_times = np.linspace(1, 100, 20)\n",
        "\n",
        "# list of initial states\n",
        "initial_states = np.array(\n",
        "    [\n",
        "        [[1.0 + 0.0j], [0.0 + 0.0j]],\n",
        "        [[1.0 + 0.0j], [1.0 + 0.0j]] / np.sqrt(2.0),\n",
        "        [[1.0 + 0.0j], [0.0 + 1.0j]] / np.sqrt(2.0),\n",
        "    ]\n",
        ")\n",
        "initial_state_names = [\n",
        "    r\"$\\vert 0 \\rangle$\",\n",
        "    r\"$(\\vert 0 \\rangle + \\vert 1 \\rangle)/\\sqrt{2}$\",\n",
        "    r\"$(\\vert 0 \\rangle +i \\vert 1 \\rangle)/\\sqrt{2}$\",\n",
        "]\n",
        "\n",
        "# list of states whose population will be measured at the end\n",
        "projector_states = np.array(\n",
        "    [\n",
        "        [[1.0 + 0.0j], [0.0 + 1.0j]] / np.sqrt(2.0),\n",
        "        [[1.0 + 0.0j], [0.0 + 0.0j]],\n",
        "        [[1.0 + 0.0j], [1.0 + 0.0j]] / np.sqrt(2.0),\n",
        "    ]\n",
        ")\n",
        "projector_state_names = [\n",
        "    r\"$(\\vert 0 \\rangle +i \\vert 1 \\rangle)/\\sqrt{2}$\",\n",
        "    r\"$\\vert 0 \\rangle$\",\n",
        "    r\"$(\\vert 0 \\rangle + \\vert 1 \\rangle)/\\sqrt{2}$\",\n",
        "]"
      ],
      "execution_count": 621,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kkYMcI3qFJNw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        },
        "outputId": "27735894-90ff-4bba-a065-d15b8115ee49"
      },
      "source": [
        "# define Hamiltonian\n",
        "Hamiltonian = lambda Omegas: 0.5 * (\n",
        "    Omegas[0] * sigma_x + Omegas[1] * sigma_y + Omegas[2] * sigma_z\n",
        ")\n",
        "\n",
        "# define unitaries for Hamiltonian\n",
        "Ut = lambda t, Omegas: expm(-1.0j * t * Hamiltonian(Omegas))\n",
        "\n",
        "# define function to calculate the populations given Omegas and wait times\n",
        "# this function first calculates the unitaries for all wait times and then\n",
        "# calculates the projected populations for different initial states and projectors.\n",
        "# the return value is thefore in the shape of [len(projector_states), len(wait_times)]\n",
        "def get_populations(Omegas, wait_times):\n",
        "    unitaries = np.array([Ut(t, Omegas) for t in wait_times])\n",
        "    return np.array(\n",
        "        [\n",
        "            np.abs(\n",
        "                np.matmul(\n",
        "                    projector_state.conj().T, np.matmul(unitaries, initial_state)\n",
        "                ).flatten()\n",
        "            )\n",
        "            ** 2\n",
        "            for projector_state, initial_state in zip(projector_states, initial_states)\n",
        "        ]\n",
        "    )\n",
        "\n",
        "# calculate expected values\n",
        "# expectation_values = get_populations(actual_Omegas, wait_times)"
      ],
      "execution_count": 622,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-622-7ecc348a2fc9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     26\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     27\u001b[0m \u001b[0;31m# calculate expected values\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 28\u001b[0;31m \u001b[0mexpectation_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_populations\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mactual_Omegas\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwait_times\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'actual_Omegas' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pzH7BSCQduLh"
      },
      "source": [
        "with qctrl.create_graph() as graph:\n",
        "    # forbids frequencies whose half-periods are shorter than the smaller spacing between points\n",
        "    min_time_resolution = np.min(np.abs(np.diff(np.unique(np.pad(wait_times, (1, 0))))))\n",
        "\n",
        "    # Parameters to be estimated\n",
        "    Omegas = [\n",
        "        qctrl.operations.bounded_optimization_variable(\n",
        "            1,\n",
        "            lower_bound=-1.0 / min_time_resolution,\n",
        "            upper_bound=1.0 / min_time_resolution,\n",
        "            name=name,\n",
        "        )\n",
        "        for name in [\"omega_x\", \"omega_y\", \"omega_z\"]\n",
        "    ]\n",
        "\n",
        "    # Create coefficients of the Hamiltonian\n",
        "    # to calculate the infidelities at the intermediate times, you can create a batch of Hamiltonian\n",
        "    # coefficients with the duration set to 1 and signal value set to be scaled by the intermediate times\n",
        "    signals = [\n",
        "        qctrl.operations.pwc_signal(duration=1, values=omega * wait_times[:, None])\n",
        "        for omega in Omegas\n",
        "    ]\n",
        "\n",
        "    hamiltonian = qctrl.operations.pwc_sum(\n",
        "        [\n",
        "            qctrl.operations.pwc_operator(signal=signal, operator=operator)\n",
        "            for signal, operator in zip(\n",
        "                signals, [sigma_x / 2, sigma_y / 2, sigma_z / 2]\n",
        "            )\n",
        "        ]\n",
        "    )\n",
        "\n",
        "    # Calculate the projected populations\n",
        "    populations = [\n",
        "        1\n",
        "        - qctrl.operations.infidelity_pwc(\n",
        "            hamiltonian=hamiltonian,\n",
        "            target_operator=qctrl.operations.target(\n",
        "                operator=projector_state @ initial_state.T.conj()\n",
        "            ),\n",
        "        )\n",
        "        for projector_state, initial_state in zip(projector_states, initial_states)\n",
        "    ]\n",
        "\n",
        "    # Calculate cost\n",
        "    cost = qctrl.operations.sum(\n",
        "        [\n",
        "            qctrl.operations.sum(\n",
        "                (population - measurement_result) ** 2.0\n",
        "                / (2.0 * measurement_error ** 2.0)\n",
        "            )\n",
        "            for measurement_result, measurement_error, population in zip(\n",
        "                measurement_results, measurement_errors, populations\n",
        "            )\n",
        "        ],\n",
        "        name=\"cost\",\n",
        "    )\n",
        "\n",
        "    # Calculate Hessian\n",
        "    hessian = qctrl.operations.hessian_matrix(cost, Omegas, name=\"hessian\")\n",
        "\n",
        "# Estimate the parameters\n",
        "result = qctrl.functions.calculate_optimization(\n",
        "    cost_node_name=\"cost\",\n",
        "    output_node_names=[\"omega_x\", \"omega_y\", \"omega_z\", \"hessian\"],\n",
        "    graph=graph,\n",
        ")\n",
        "\n",
        "estimated_Omegas = [\n",
        "    result.output[\"omega_x\"][\"value\"][0],\n",
        "    result.output[\"omega_y\"][\"value\"][0],\n",
        "    result.output[\"omega_z\"][\"value\"][0],\n",
        "]\n",
        "\n",
        "# Calculate 2-sigma uncertainties (error bars give 95% precision)\n",
        "hessian = result.output[\"hessian\"][\"value\"]\n",
        "uncertainties = 2.0 * np.sqrt(np.diag(np.linalg.inv(hessian)))\n",
        "Omega_x_uncertainty, Omega_y_uncertainty, Omega_z_uncertainty = uncertainties\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HGmF46YhYJPB"
      },
      "source": [
        "# Pickle test\n",
        "---"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Pq0QwX8JFKvX",
        "outputId": "e29dfd0a-99fc-47fc-873d-7a7307f15ed5"
      },
      "source": [
        "NOT = np.array([ 0.31209867-0.19643832j,  0.14533233-0.0174159j,\n",
        "0.03519855+0.07436375j, -0.06475012-0.39443288j,\n",
        "-0.59260967+0.35004789j,  0.23800035+0.09241289j,\n",
        "0.17528884+0.0232753j ,  0.26635219-0.41861981j,\n",
        "-0.05800045+0.09432613j,  0.09115755+0.38281929j], dtype=np.complex)\n",
        "print(NOT)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[ 0.31209867-0.19643832j  0.14533233-0.0174159j   0.03519855+0.07436375j\n",
            " -0.06475012-0.39443288j -0.59260967+0.35004789j  0.23800035+0.09241289j\n",
            "  0.17528884+0.0232753j   0.26635219-0.41861981j -0.05800045+0.09432613j\n",
            "  0.09115755+0.38281929j]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "at9Q3o-ZT-Bb",
        "outputId": "64d88433-de0a-4449-898f-21b17b0c7076"
      },
      "source": [
        "!pip install jsonpickle"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting jsonpickle\n",
            "  Downloading https://files.pythonhosted.org/packages/bb/1a/f2db026d4d682303793559f1c2bb425ba3ec0d6fd7ac63397790443f2461/jsonpickle-2.0.0-py2.py3-none-any.whl\n",
            "Requirement already satisfied: importlib-metadata; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from jsonpickle) (3.8.1)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata; python_version < \"3.8\"->jsonpickle) (3.4.1)\n",
            "Requirement already satisfied: typing-extensions>=3.6.4; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from importlib-metadata; python_version < \"3.8\"->jsonpickle) (3.7.4.3)\n",
            "Installing collected packages: jsonpickle\n",
            "Successfully installed jsonpickle-2.0.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QTPXqX-7T5Dy",
        "outputId": "d8c30607-0b9a-4c99-b5f1-4aed1a8297db"
      },
      "source": [
        "import jsonpickle.ext.numpy as jsonpickle_numpy\n",
        "import jsonpickle\n",
        "import numpy as np\n",
        "\n",
        "jsonpickle_numpy.register_handlers()\n",
        "\n",
        "not_control = {\"duration\": 100, \"value\": NOT}\n",
        "json_not_control = jsonpickle.encode(not_control)\n",
        "with open(\"not_control.json\", \"w\") as file:\n",
        "    file.write(json_not_control)\n",
        "\n",
        "print(json_not_control)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{\"duration\": 100, \"value\": {\"py/object\": \"numpy.ndarray\", \"dtype\": \"complex128\", \"values\": [{\"py/object\": \"builtins.complex\", \"py/newargs\": {\"py/tuple\": [0.31209867, -0.19643832]}}, {\"py/object\": \"builtins.complex\", \"py/newargs\": {\"py/tuple\": [0.14533233, -0.0174159]}}, {\"py/object\": \"builtins.complex\", \"py/newargs\": {\"py/tuple\": [0.03519855, 0.07436375]}}, {\"py/object\": \"builtins.complex\", \"py/newargs\": {\"py/tuple\": [-0.06475012, -0.39443288]}}, {\"py/object\": \"builtins.complex\", \"py/newargs\": {\"py/tuple\": [-0.59260967, 0.35004789]}}, {\"py/object\": \"builtins.complex\", \"py/newargs\": {\"py/tuple\": [0.23800035, 0.09241289]}}, {\"py/object\": \"builtins.complex\", \"py/newargs\": {\"py/tuple\": [0.17528884, 0.0232753]}}, {\"py/object\": \"builtins.complex\", \"py/newargs\": {\"py/tuple\": [0.26635219, -0.41861981]}}, {\"py/object\": \"builtins.complex\", \"py/newargs\": {\"py/tuple\": [-0.05800045, 0.09432613]}}, {\"py/object\": \"builtins.complex\", \"py/newargs\": {\"py/tuple\": [0.09115755, 0.38281929]}}]}}\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}