525 lines
142 KiB
Plaintext
525 lines
142 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "8d75dc24-b8d2-45f8-830b-48e45f794a31",
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"metadata": {},
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"source": [
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"# Visualize circuits"
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]
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},
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{
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"cell_type": "markdown",
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"id": "7ea76afb-aecf-4ea2-b173-5d5e7e7d68ec",
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"metadata": {
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"tags": [
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"version-info"
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]
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},
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"source": [
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"<details>\n",
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"<summary><b>Package versions</b></summary>\n",
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"\n",
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"The code on this page was developed using the following requirements.\n",
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"We recommend using these versions or newer.\n",
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"\n",
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"```\n",
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"qiskit[all]~=1.2.4\n",
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"qiskit-aer~=0.15.1\n",
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"qiskit-ibm-runtime~=0.31.0\n",
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"qiskit-serverless~=0.17.1\n",
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"qiskit-ibm-catalog~=0.1\n",
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"```\n",
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"</details>"
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]
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},
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{
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"cell_type": "markdown",
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"id": "bd5865b3-adfa-4c87-be55-51f693335184",
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"metadata": {},
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"source": [
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"It's often useful to see the circuits you're creating. Use the following options to display Qiskit circuits."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "488c8d7d-5615-40ec-bf84-f1fa94832fce",
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"metadata": {},
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"outputs": [],
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"source": [
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"from qiskit import QuantumCircuit"
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]
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},
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{
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"cell_type": "markdown",
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"id": "8bbddcca-300c-4f20-98dd-a0723cdef026",
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"metadata": {},
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"source": [
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"## Draw a quantum circuit\n",
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"\n",
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"The `QuantumCircuit` class supports drawing circuits through the `draw()` method, or by printing the circuit object. By default, both render an ASCII art version of the circuit diagram.\n",
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"\n",
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"Note that `print` returns `None` but has the side effect of printing the diagram, whereas `QuantumCircuit.draw` returns the diagram with no side effects. Since Jupyter notebooks display the output of the last line of each cell, they appear to have the same effect."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "547f07e8-7891-433c-ac53-3186196b3aa7",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Build a quantum circuit\n",
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"circuit = QuantumCircuit(3, 3)\n",
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"circuit.x(1)\n",
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"circuit.h(range(3))\n",
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"circuit.cx(0, 1)\n",
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"circuit.measure(range(3), range(3));"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "d69ae2df-c31c-414f-bba2-4c4a1012de41",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" ┌───┐ ┌─┐ \n",
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"q_0: ┤ H ├───────■──┤M├───\n",
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" ├───┤┌───┐┌─┴─┐└╥┘┌─┐\n",
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"q_1: ┤ X ├┤ H ├┤ X ├─╫─┤M├\n",
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" ├───┤└┬─┬┘└───┘ ║ └╥┘\n",
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"q_2: ┤ H ├─┤M├───────╫──╫─\n",
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" └───┘ └╥┘ ║ ║ \n",
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"c: 3/═══════╩════════╩══╩═\n",
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" 2 0 1 \n"
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]
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}
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],
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"source": [
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"print(circuit)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "04c91fd9-50a1-4ff3-84e4-0b51a6b1541a",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<pre style=\"word-wrap: normal;white-space: pre;background: #fff0;line-height: 1.1;font-family: "Courier New",Courier,monospace\"> ┌───┐ ┌─┐ \n",
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"q_0: ┤ H ├───────■──┤M├───\n",
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" ├───┤┌───┐┌─┴─┐└╥┘┌─┐\n",
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"q_1: ┤ X ├┤ H ├┤ X ├─╫─┤M├\n",
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" ├───┤└┬─┬┘└───┘ ║ └╥┘\n",
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"q_2: ┤ H ├─┤M├───────╫──╫─\n",
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" └───┘ └╥┘ ║ ║ \n",
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"c: 3/═══════╩════════╩══╩═\n",
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" 2 0 1 </pre>"
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],
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"text/plain": [
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" ┌───┐ ┌─┐ \n",
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"q_0: ┤ H ├───────■──┤M├───\n",
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" ├───┤┌───┐┌─┴─┐└╥┘┌─┐\n",
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"q_1: ┤ X ├┤ H ├┤ X ├─╫─┤M├\n",
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" ├───┤└┬─┬┘└───┘ ║ └╥┘\n",
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"q_2: ┤ H ├─┤M├───────╫──╫─\n",
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" └───┘ └╥┘ ║ ║ \n",
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"c: 3/═══════╩════════╩══╩═\n",
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" 2 0 1 "
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"circuit.draw()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "2159af06-93b8-441d-b8aa-c29324d26c6e",
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"metadata": {},
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"source": [
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"### Alternative renderers\n",
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"\n",
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"A text output is useful for quickly seeing the output while developing a circuit, but it doesn't provide the most flexibility. There are two alternative output renderers for the quantum circuit. One uses [Matplotlib](https://matplotlib.org/) and the other uses [LaTeX](https://www.latex-project.org/). The LaTeX renderer requires the [qcircuit package](https://github.com/CQuIC/qcircuit). Select these renderers by setting the \"output\" argument to the strings `mpl` and `latex`.\n",
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"\n",
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"<Admonition type=\"tip\">\n",
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" OSX users can get the required LaTeX packages through the [mactex package](https://www.tug.org/mactex/).\n",
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"</Admonition>"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "3f9c61c9-58f9-4315-a639-455fa2e58450",
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"metadata": {},
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"outputs": [
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{
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],
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"text/plain": [
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"<Figure size 538.33x367.889 with 1 Axes>"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# Matplotlib drawing\n",
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"circuit.draw(output=\"mpl\")"
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]
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},
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{
|
|
"cell_type": "code",
|
|
"execution_count": 6,
|
|
"id": "94948dab-57de-45f0-8dd7-5901ae69b70a",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/jpeg": 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Nb+qx3b+N9EuorC5ktrWG5jmmULtUyCPb3yfunoOK6aigAqjYaaLC4vphd3c/2ubzilxLvWLgDbGP4V4zj1Jq9RQAyaGK5gkgniSWGVSkkcihldSMEEHggjtWF/wgng//AKFTQ/8AwXQ//E10FFAHP/8ACCeD/wDoVND/APBdD/8AE1n2+haPonxD0v8AsnSrGw87Sr7zPslukW/EtpjO0DOMnr6muwrm9bGoWvirStUtNIu9Rgisru3lW1khVkaR7dlJ82RARiJuhPagCx4j8Uaf4ctc3FzCLqTCwQu+CzMdoLdwgPVugANcZqQX4ceKbDWxdmex1phBq0ecs0vUXSL6DOGA4AI9q6//AISHVP8AoTNc/wC/1l/8kUf8JDqn/Qma5/3+sv8A5IoA27a5gvLdLi1njnhcZWSJgyt9CODXEeC/BfhW68C+Hri48NaNNPLpls8kklhEzOxiUkklckk85rc/4SHVP+hM1z/v9Zf/ACRVjwnY3GmeDdDsLyPy7q10+CGZNwO11jUMMjg4IPSgCv8A8IJ4P/6FTQ//AAXQ/wDxNH/CCeD/APoVND/8F0P/AMTXQUUAc34GghtfDclvbxRwwRanqCRxxqFVFF5MAABwABxiuXu30XUPjmFvTY3MC6D9nHnbXQT/AGn7nPG/BPHWtzR7vV9Etrqyk8K6rc/8TC8mSaCa02OklzJIpG6dW+646gVof8JDqn/Qma5/3+sv/kigDk7V9F07457bM2NtAdBNu3k7UQz/AGkfIccb8Dp14rpPF9hZ6nqHhazv7SC7tZNVffDPGJEbFnckZU8HBAP4VY/4SHVP+hM1z/v9Zf8AyRVOefVdZ13QGbw3qVjBZXr3E091LbFQptp4wAI5mYktIvb1oAuf8IJ4P/6FTQ//AAXQ/wDxNH/CCeD/APoVND/8F0P/AMTXQUUAcvf/AA78JXmnXVqnhvR4HmieNZo7CINGSCNwIXgjOa5DUtG8KTfDvxBHJ4a0Wz1+w0q4+0RLZRLJFIsTYkQ7clSRuVx/MED1es7VtA0jXYhHqum2t4qghTNEGK564PUfhQByXiXw54RtrVtL07wzoT63eIY7WJLCLdGTx5rYXKouck+2ByQK2rf4f+EILaKE+GNGkMaBd8lhEWbAxknbya1dM0TS9FjePTNPtrRXOX8mMKXPqxHJ/Gr9AHP/APCCeD/+hU0P/wAF0P8A8TR/wgng/wD6FTQ//BdD/wDE10FFAHP/APCCeD/+hU0P/wAF0P8A8TWHrvgzwrDrHhhIvDWjIk2pukqrYRAOv2S4bDfLyNyqcHuAe1d5XP8AiH/kOeE/+wrJ/wCkV1QBh+E/BfhW50e4efw1o0rjU79Az2ETEKt3MqjlegUAAdgAK3P+EE8H/wDQqaH/AOC6H/4mjwb/AMgO5/7Cupf+ls1dBQBz/wDwgng//oVND/8ABdD/APE0f8IJ4P8A+hU0P/wXQ/8AxNdBRQBwfjTwX4VtfAviG4t/DWjQzxaZcvHJHYRKyMImIIIXIIPOa3LrVNT1K+m0/Q4kiWBtlxqNyhMcbd1jTjzGHc5Cj1JytHjv/knniX/sFXX/AKKaj/hO/B//AENeh/8Agxh/+KoAQeDtOn+bVZ7zVpT95r24YofpEuIx+C1gXPg7w5qviiPTrPRrS2tNOAnvZLVPJZ5WH7qLcmDwP3hwcj93610H/Cd+D/8Aoa9D/wDBjD/8VUUHjHwRbGUweJdAjM0hlkK38I3ucZY/NyeB+VACvpetaKPN0i/l1G3X72n6hLuYj/pnOfmB/wB/cD6r1rMu4dJ8W+LNBa/0qO5g/s3UG+zajahjFIstqpyrAgMPmGR68Eg1r/8ACd+D/wDoa9D/APBjD/8AFVn2+u6PrfxD0v8AsnVbG/8AJ0q+8z7JcJLszLaYztJxnBxn0NAGh/wgng//AKFTQ/8AwXQ//E0f8IJ4P/6FTQ//AAXQ/wDxNdBRQBz/APwgng//AKFTQ/8AwXQ//E0f8IJ4P/6FTQ//AAXQ/wDxNdBRQBgL4G8Iqcr4V0QEdxp8X/xNFb9FABRXFXnjW5Hh3VfEVhBBJptlI0MCSA77xlfYSrA4UF8qODnGeM1ty+JbKxjBvpGURyJBcXCRsYYpWwNpbsMsBnoM8kUAbVFFUbCTUnuL0X8FvFCs2LRopCxePA5YEcHOeBQBeooooAKw9Y1jUrXWbHS9L0+0u57m3nuGa6vGgVFiaJcArG5JJmHYdDW5XP3n/JQ9G/7BV/8A+jbSgA+2eMP+gFof/g5m/wDkWj7Z4w/6AWh/+Dmb/wCRao+P9c1bw7pNtf6XLaAvdw2zJcQM4/eOF3Ahl6ZpnjnWtb8NeH7a+srmzeY3MNvIJrZireY4XcAHGMZ6c0AaP2zxh/0AtD/8HM3/AMi1oaFqf9t+HtM1byfJ+3WkVz5W7ds3oG25wM4zjOBVyBZUgRZ5FklA+Z1TaD+GTj86w/An/JPPDX/YKtf/AEUtAHQVh+Ir/UrWXR7TS5bSGfUL027S3UDTKiiCaXIVXQk5iA69zW5XP+If+Q54T/7Csn/pFdUAH2Pxh/0HdD/8E03/AMlUfY/GH/Qd0P8A8E03/wAlV0FFAHP/AGPxh/0HdD/8E03/AMlVHFH4qnMgh8ReH5DE/lyBNIlOxsA4P+lcHBHHvWh4g1V9J0ppbeMS3szrBaQk/wCsmbhQfbuT2UE9qk0TSk0bSYbJZDLIuXmmYfNNKx3O592Yk/jQBT8O3+pXUusWmqS2k0+n3ot1ltYGhV1MEMuSrO5BzKR17Ctyuf8AD3/Ic8Wf9hWP/wBIrWugoAKKKKACiiigAooooAK5/wAQ/wDIc8J/9hWT/wBIrqugrn/EP/Ic8J/9hWT/ANIrqgA8G/8AIDuf+wrqX/pbNXQVz/g3/kB3P/YV1L/0tmroKACiiigAooooA5/x3/yTzxL/ANgq6/8ARTUWf/JQ9Z/7BVh/6Nu6PHf/ACTzxL/2Crr/ANFNRZ/8lD1n/sFWH/o27oA6CiiigAooooAKKKKACiiigDzDUNNGj/8ACKeChfWptW1TzihOJJIYi04D5PHzBF77ic8dK0PE+p6RcXVzoD2l4lu0yzXi22mzH7bINpCCRU2gEqoZyegx6kd01tA8gkaGNpBghioJ46c1LQAVRsLe/guL572/W6ilm32yCER+RHgDYSD83OTk+tXqKAGTRLcQSQuXCyKUJjcowBGOGUgg+4IIrC/4Q3S/+frXP/B7e/8Ax6ugooA5/wD4Q3S/+frXP/B7e/8Ax6s+30a10j4h6X9mlvpPN0q+3fa7+e5xiW0xjzXbb17YzxnoK7CuT13VtN0bx1olxqmoWljA2mXyLLdTLEpYy2hwCxAzgE49jQBnfFwK3hC1ViQG1S0BIYg/60dCORVP4o6Ta2fhW2mjlvCw1O0GJr6aRf8AWr/CzkfpW3qHiD4dauUOpav4WvSn3ftNzbybfpuJxUd1rPw0vYYobvUvCU8UIxEks9syoPYE8fhQB2VcH4L8J6dc+BfD073Osh5NMtnYR61eIoJiU8KsoCj2AAHatiDxn4JtYEgt/Evh+GJBhY47+FVUewDcVJ4E/wCSeeGv+wVa/wDopaAD/hDdL/5+tc/8Ht7/APHqy9Q0Cy0nxH4VuIJ9RdjqUiEXWp3FwoH2O5P3ZJGAPHXGevqa7Sud8TRxzav4WilRXjfVJFZGGQwNldZBHcUAN/tzUNbdk8OQw/ZVJVtUuwTCSOvlICDL9cqvoTTx4aup/mv/ABJq87nqIZEt0H0Eag4+pJ963440ijWONFRFAVVUYAA6ACiRPMiePcy7lI3KcEZ7g+tAHAaf4fudW1241Kx1/UorXTpGt7Fp3W5WSUZWZ8OCdufkGCD8r84Nbo12/wBGlSLxJBCtuzBU1O1BEGTwBIpJMWT3JZf9oHArb0+xt9M0+3sbSPy7e3jEca9cADHXufep5I0mieKVFeNwVZGGQwPUEdxQBxun6BZar4k8VT3E2pI66lGgFrqdxbrj7HbHlY3UE89cZ6egrU/4Q3S/+frXP/B7e/8Ax6k8OKF1rxWqgADVYwAO3+hWtdDQBz//AAhul/8AP1rn/g9vf/j1H/CG6X/z9a5/4Pb3/wCPV0FFAHP/APCG6X/z9a5/4Pb3/wCPUf8ACG6X/wA/Wuf+D29/+PV0FFAHP/8ACG6X/wA/Wuf+D29/+PUf8Ibpf/P1rn/g9vf/AI9XQUUAc/8A8Ibpf/P1rn/g9vf/AI9WHrvhPTotY8MItzrJEupujbtavGIH2S4b5SZcqcqORg4yOhIPeVz/AIh/5DnhP/sKyf8ApFdUAYfhPwnp0+j3Dvc6yCNTv0+TWrxBhbuZRwsoGcDk9Sck5JJrc/4Q3S/+frXP/B7e/wDx6jwb/wAgO5/7Cupf+ls1dBQBz/8Awhul/wDP1rn/AIPb3/49R/whul/8/Wuf+D29/wDj1dBRQBz/APwhul/8/Wuf+D29/wDj1H/CG6X/AM/Wuf8Ag9vf/j1dBRQBwfjTwnp1t4F8Qzpc6yXj0y5dRJrV46kiJjyrSkMPYgg96LXwnpzeOtWgNzrOxNMsnBGtXgbJlugct5uSPlGATgc4xk53PHf/ACTzxL/2Crr/ANFNRZ/8lD1n/sFWH/o27oAP+EN0v/n61z/we3v/AMeo/wCEN0v/AJ+tc/8AB7e//Hq6CigDn/8AhDdL/wCfrXP/AAe3v/x6j/hDdL/5+tc/8Ht7/wDHq6CigDn/APhDdL/5+tc/8Ht7/wDHqP8AhDdL/wCfrXP/AAe3v/x6ugooAwF8H6YpyLrW8++u3p/9q0Vv0UAFFFZ91ren2VxJBNcHzYkEkqRxtIY0OcM+0HaODycdD6UAaFFNjkSaNJI3V43AZWU5DA9CDVe11GyvprmK1uoppLWTyp1jcExvjO1vQ4NAFqiiigAorGaaW88WrBHI62+n23mShWIDyyHCA+u1Vc4P99TWzQAUUUUAFFFFABVPUtJ03WbdbfVNPtL6BXDrHdQrKoYAjIDAjOCefc1cooA5/wD4QTwf/wBCpof/AILof/iaw9d8F+FYdY8MJF4a0ZEm1N0lVbCIB1+yXDYb5eRuVTg9wD2rvK5HXre+t/F3h+7aUSaUdQ3ybzzbyG2mhUD/AGXaRRjs3ru4AKPhPwX4VudHuHn8NaNK41O/QM9hExCrdzKo5XoFAAHYACtz/hBPB/8A0Kmh/wDguh/+JrO8IxahJcXSOTBY2WpX5AVgftMklzK45H8Cq446785xs57GgCnpuk6bo1u1vpen2ljAzl2jtYViUsQBkhQBnAHPsKuUUUAFFFFABRRRQAUUUUAFc/4h/wCQ54T/AOwrJ/6RXVdBXIa3Bf2/jHw9K0nm6W+otIWdubaT7LPGFHqrllx6Nn+8AAC/4N/5Adz/ANhXUv8A0tmroK47wdFqEst4ZWMFha6lqHlqrA/aXe6lbcSP4VDAAf3t2fuiuxoAKKKKACiiigDn/Hf/ACTzxL/2Crr/ANFNRZ/8lD1n/sFWH/o27qr8QbDVL7whqY0ljJObG4ha1J+WdJIypx/tjgj1wR/FkVJnu9Q8a3C6XLtttR0qzZr6NgfLhWS4JK9wzCRQp6fePO3BAO1ooooAKKKKACiiigAooooAK4XRYodN1fxdYeIFUjUr1riJ51ylzbPEqCNf7xXaylevI45Fd1RQBXsVjTT7ZIbc20SxKEgKhTEuBhcDgY6Yqrpl7Z3V1qUdraSwSQXHlzs9uYxK+0HcDj5xjAz7VpUUAQXqJJYXCSWv2tGiYNb4U+cMcphiFOenJA55rhX0nQY42d/hIFRQSzG10wAAd/8AXV6DWbrtjNqekS2EJUC5KxTEnH7ksBJj3KbgPc0AefaDoukyad9rm+FRnN5I1yh+y6dhY2OY1AaYFcJtyMDnJxzVi2h8K3k3lW/wsikk2eYFW20zLJnG4fvuVzxkcV6VjC4UAYHArmdK03UE8RQX8tnHaxDT/IuE3h1Eu5SBDjlU4fOcZ+TjigDG/sXQ/wDokX/kppn/AMeo/sXQ/wDokX/kppn/AMer0CigDz/+xdD/AOiRf+Smmf8Ax6j+xdD/AOiRf+Smmf8Ax6vQKKAPP/7F0P8A6JF/5KaZ/wDHqP7F0P8A6JF/5KaZ/wDHq9AooA8//sXQ/wDokX/kppn/AMeqC98OaJeWFxbD4USwGWNkE0NtpqvGSMblPncEda9Hqne6gLOSKJLW4uZpQzLHCFzhcZ5YhR1HfJ/A0AcJY+HNEs7C3tT8KJZzFGqGWa201ncgY3MfO5J61P8A2Lof/RIv/JTTP/j1dtpuo22raZbajZuXtrmNZY2IwSpGRkdqtUAef/2Lof8A0SL/AMlNM/8Aj1H9i6H/ANEi/wDJTTP/AI9XoFFAHn/9i6H/ANEi/wDJTTP/AI9R/Yuh/wDRIv8AyU0z/wCPV6BRQB5//Yuh/wDRIv8AyU0z/wCPUf2Lof8A0SL/AMlNM/8Aj1egUUAef/2Lof8A0SL/AMlNM/8Aj1H9i6H/ANEi/wDJTTP/AI9XoFFAHn/9i6H/ANEi/wDJTTP/AI9VfUPDei32n3FqnwqltnlQqs8NtpgeM9mH77qDzXpFU7vUYrS6t7XY8tzcB2jijxkquNzckDAyo6/xCgDhrXw9oVraQ24+E0soiRU3y2+ms7YGMsTPkk9zUv8AYuh/9Ei/8lNM/wDj1dxYXsGpWEF7avvgnQOhxg4PqOxqxQB5/wD2Lof/AESL/wAlNM/+PUf2Lof/AESL/wAlNM/+PV6BRQB5/wD2Lof/AESL/wAlNM/+PUf2Lof/AESL/wAlNM/+PV6BRQB5/wD2Lof/AESL/wAlNM/+PVQ0zwnoum/a93wvurr7RcNMPtEGmv5QPRFzPwo7V6bI/lxs4RnwM7V6n6VW0vUYdW0yC/gWRYpl3KsgAYc45wSKAOK/sXQ/+iRf+Smmf/HqP7F0P/okX/kppn/x6vQKKAPP/wCxdD/6JF/5KaZ/8eo/sXQ/+iRf+Smmf/Hq9AooA8//ALF0P/okX/kppn/x6j+xdD/6JF/5KaZ/8er0CigDgU0bRA4I+Euw/wB77LpvH5TUV31FABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAV5h8afGup+DtG006dFbSC8meOZZw/zKFzjKspGc84NFFAHaeDrx9R8F6JeyJFG9xYwylIkCIm5AdqgdFGcAegrboooAKKKKACiiigAooooAKKKKACub8SubHVNJ1OIA3Efn24Dfd2Om88euYU/WiigC94Yto7TwvpkUedv2ZGyTySw3H9Sa1qKKACiiigAooooAqancvZaXdXUYUvFEzqG6Egd65j4X6jNqXgi2eZUUxO0S7ARwMHnJ68miigDsqKKKACiiigAooooAKKKKAP/9k=",
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"image/png": "iVBORw0KGgoAAAANSUhEUgAAAVcAAACECAIAAACrjv96AAAfFUlEQVR4Ae2defwN1fvAi0/fkMhXlBChRJFSKkkihPqoUEIq5fejbNnK2mIrS4utPcpSkrxCZStFUijSgpJSKIoWRIv6vnW8xjEz99y5c2fuPXPvuX/c1zNneZ7nPGfmmXOe88zzHPnPP/8cYX5GAiFL4Kijjvrrr79CJnIIfU5Ozp9//nno2kBKCRxptIBSPqYyGAkceWRK77QUkwtGRunDkpM+0v4ph/FiMW8P//NhekZcApHUAqwtA1/C8PaI+FQa9o0EfEogj89+ppuRQLolsHnz5oULF6abi0ygb7RAJsxido7hjjvu+PDDD7Nz7MGOOqU2m6BYD8P2EwbOoMabAXgCF+9777137bXXrlu3rkCBAk75BE7OSSKTSsxaIJNmM1vGglWoZ8+ew4YNc1UB2SKF4MZ5yDq4a9euefPm5cuXr0GDBhjh0bKVKlVKlNAPP/xQrFixRHsF0n7q1KkLFizImzeveA/s37+/fv36rVq1mjFjxuzZszkCEOVYFmvXrt2uXbtAiBokaZHAtGnTfvvtt+uvvz4t1DOQKGqV35tvvsmzsXz58jVr1vTu3btr164jR44UVd7/+/Tpw5M2Z84c7138tWQaXDvy5Hfo0IHajh07Altt/v777x49elB+ww03iPMFq8oCYuG0GhggGQkEKN7ff/+9fPny2AUV/ARITkElY6oOrAV4Vd5yyy2rV68+6aSTuKxYsSKXK1asAE7oV7ZsWTAUL148oV4BNs6TJ8+ZZ54JwjPOOAPYwoxuqlKliihnsWCVGyCKEhg9ejSzXK9evSgyryfPOXv27OHNefvttwsVAJeUFCpU6Oyzzwbeu3fvM888s2/fvvz58zdv3lz9hP/fvz89x2m4UkuApdPEiROXLFly9NFHs5NirtXt01X7008/jRgxYtGiReliICPp5owbN27Lli0tW7a0hvf222/XqlVLvDNvvPFG1tLnn3/+d999l5ubO3PmzBIlSlgtDZAZEvj111958tkPiuE88cQTTZs2xZ6i4brpvvvua9GiReXKlTND8pqMIs8777xTsmRJdgGCIbY6ixcvrlOnDpevvfYaDz8qAJiHnxvlscceE83MfyZJoFevXpYKEON65ZVXHnroId3GuHHjxsmTJw8YMEA3xqLOT56PP/5YPOdiJFxi57/kkku4xOSOGcYaYbly5Xg/WJc6A5s2bfrg8N9XX32lM8Np5A29j8ndyYBrobNZSCUYcVn8f/3119u2bfv5558FFezWrExPOOGEkIhmLdqc448/vlSpUtb4Z82aVbBgwXPOOYeS9evXY/Czqjib/f77761LJ4D64KzhqquuYm/prJVLTjnlFCZYLgkW5qXBGkfG+cUXX8iXrjBGRNfy8Ao5v+R2Dw9/MphXrlyZSoH88ccfTBm7UdyBPv/8861btx5zzDHHHXcclil+mC1YjVLI+4mpPPXUU+MOLZXMC2bSNZtJ0uUZz8EiwDtfDINnmHUgRgHwUsKRjCxKztt++eUXhfT79ev35JNPPvvss23btlU0oyrJN7PMlSuhSy+9FHunXAVXOBTIJU6Yt6KzMBtKWPGhN20jvfzyy19//XVboe9LxZRxOIUlYvr06RUqVGDiunTpgqNKmTJlZKsENx67VBwEcBnGWQiN0KZNG06yMGPHYilrZzOWQBTlObgG8IZnrYX9nzU/syWMAvRBxPKbipk49thjFbg4jceXo27duoo2pkpDCQwcOPCmm26SGeOcNQXbb06jIf3JJ5/cdtttbOBOPvlkmQcZnjt3Lnfm448/DmO8jTBdPfXUU0OGDLn11lv79u2r0AUyEgPHlAAq0/rhMkS7ZcuWiRLssVdffbVViw7mkNa6TCMAk7Gojx07llr+bQ04BqP8/vvvt5VblwqcVpsMBh588EHcRhECv6JFi/JmDnawoJUR7ty5s3379hxO8zCz5JSrnDANWCY43YS++eYbtADrgueee87Wy0bOVmsubRI45FqD4N566y02Y9WrVwfmx5HM2rVr6SAugXnbC9j8Z5gE+D5PGH2WLl367bffhuovAAm8UVA63FGs6v/zn/+ohYmbEG5gTjeh0qVLswPF5w0PAja2lhFRjc3UOiVwmBbAHQAVwEcEot3pp5/OfoF92qpVq3i7Uo77gBOFViVCZ1may+ItVrnVwACFCxdGCDVr1sRDLDxp4ISGisFLhWfby0peuAkNHTo0FkvcsRxz8vUKnIdqco7FQCaU83jw4xj2mmuuwSPgsssuwwazfft2Uc4/LgNYDTkvsErSDiB3Jw+cULDDxN2FWjYywJTQbMeOHZ9++qn48oQPpTgKlUdn4XHFadVmCRCeEARmPk7B9sQRgHd5duvWDUOvl/ZjxozB8wUXOBqHNxAvnESuTebEF5g/fz7WZhYsmJGYBkLQVqtWjceebQ7vCrkcz7MrrriCG0X+iV5ySRbC4QlBYGYh0KhRI3byHmXLyQXOLCh0jz4CmBv5xIC1THgD8ch5tJpljhZIUu7mvkGA4QnBH2bODtj8861qopPrj1yiVDKmvdECB6fS3DcIIjwh+MOMwY/DadlxwOOD54+cR+SZ18xogYNzau4bBBGeEMLD7PpMppicKw8RKjzsjCBCfBtWjQSMBIKSgNECQUnS4DESiKoEDnwvELkfnzmw5AuWbfHpRLA4DTYjgUhIIJJawCSijMS9JTMZhuKW8dtgo9NtAlFfRtI6qB6SqfUtAX2MajInMux7aKajQgLGLqAQjqkyEsgKCRgtkBXTbAZpJKCQgNECCuGYKiOBrJBAJK2DWTEzZpDpkwBfncjxdcJmBFtmeg3ekdQCYUxSgDOhOXth39MZgB8VID5MTM1YAj/2TpTtSGqBMCYpwJnQnL1EbxHTPuMlYOwCGT/FZoBaSIB3A7GVtGDFwYTRAg6RmAIjgRAkQLSue+65JwTEAaCM5I4ggHEbFA4JEJqJMiLQEpic8JOOelPgXwJ8Ik3k2zfeeMM/ijB7mrVAmNKNAu4ff/yRYN7k+SAEE8YRQradeOKJ5KQgRwCL2CiMIAI8Dho0iIh+xFDVk9fDPIi5ITCVkxAGXgn/HDfFULqG5OpSSkD7jz76SAS0xcAL/40bNybWNTA3NOUMjRQ3XN58881Ozl1xOpt5KXFFRe4doqETeJeYGfBAUH0yQZMFgAMFAulSLlKtUw7nrVq1sr2NXXF6YUbdZsqUKQT2IygjIb2rVq0qqHBqRVLgUaNGEYyYkORpuXfl8cqwejhB1QZLkRw85513HnHTUK+uHAZLzpVEnELuSH7kfmrWrNnw4cMfffRRgsMSjJQ9jKjy/s8tRdQ3HkXvXfy1ZEjOjps3bya96rXXXkstD/z48eNZholmPGZoNOaAO5v729mXElecri3jFrqi4r1KBG4i51PLb9KkSVyK0wQAkgJyK1COwuISRWCjQpWtJPlLppvFP3enhcpGhQklV827775rNUgZIHMiw6lhIFiK3JODBw9WcB4sOQWhWFUH7i1MlyQOJHe9aMSjwquJtGWx+sQq563CeF566aVYDYIqV0iN50okXyWbjUWOl9sFF1ywYcMGq8QJKHA6G6tL1Kj+++/PiYHMPCxYnOWiRI0zVi9FOWH8oYj2l9s4qZCkjMC+xKGWm6UAljmR4RSQhkSAFEm7SB7QPXv2KDgPkJyCiqLqCJagPDYkh7AasXBlmSrzTX5IVjVWg1gA621axqoNsFwttc8++4w3P1ErSakmiJJkiZtezYAap7qvrVaNivSw/GxduCRpJMrXWS5K1Dhj9YpVzmYEvU9qUFsDVyp33XUX+wVby7AvZU5kOGy6An+AFLGw8ECp2Q6QnJpQrNojWPzDBOlirRZoBBI8iEuykt17773sruOOxOqeAiCu1EQSC6HaCE/eoUOHuFzFxRkXg9VAjUoHLcCkk4zUYtgCXDlnY1WkSBEyiFvNUgDInMhwCkhDIiiKWFXOOussYY1ScB4UOQUJddURGKJISiM2qKIpe0WMxgJmLQ1AYP9oaQGGc+655yJcErHn5uZaiwKFLAKcCTUqHbQAuedI4uyURizOyf9FKgFn+/BKZE5kODyKMuZAKGLc4eTFy846EHIy/4nCeUgFzY7ACvZMBsgvv/ySLPFwxg8zmwCi9c9wJkyYwDab4xmyYnrJhBWtASbJLRbBGjVqeEciTNze25uWSIAsbBUrViTZl/7SyOGACo1lMUpmWJ58dgRWSUIAGS9jHYfIeNiUhp1SjtMKkiySx3LOnDnOTEQyMxYsrPTWZXjArl27nHlfhdOOgmiw7FWqVMmVlisVDo/JSU9eY9cuIRXCCbciyzrwu3IVEl2PaBEIpyck+F63bh05lDhl3717Ny8efuygMUu98sorpFrnhJiSuDiTGaAlpbhUXBtgkDqiU6dOrVu3FksIcrxhN8acbltReNwRYEZiMHHtcDbkPi4ZTNxee/fubdKkCblxYcmZ9NrZ3QtOZy/XEjUqHXYE6H3XZIGxOH/ooYe4T1wHG1KhzIkMh0TOhlZBkXU+x7pXXnllwYIFsfxxz3PoizrgBIqTlE2bNgGQFI/UeLyHzjnnHEwqaHxOrDDD26hYlwpyVptQgZy7774bTwHWz9gw0GfwWqdOHdjy8SMRJVrQY0o5H/gT6tK/f//evXvzEsNAgImbJKVMW0IYMrjxaaedhkDkNaB6sKR7xcqlbpPxtbz8cUJ5+OGHcbLkwZ46dWqsO4r1NRttJMy6mNUxZ+cY2sgN37NnT7J+W7tvjSSGjuHJx2WAFNEYBeCMI2Kb4vG4FrD1Cu8SJtXIFy9e3L17d9GGMw7ax817GxenmqJcq0alw1qAuxmrsMyzgF055+2HZvdyVOxE6LtE5kSGfSNMqKOT4owZM8qUKYP/D0dOcVG5ugmR+BtzG48SN6cNg5OcrUHYl4c9Tni2oah+/fVXG9VoaQG2ZxyDobnFKNiYwT/7AtZptnHJlwHOhBqVDloAjc+DzRtelgCwK+e8/VgA21qGfSlzIsNh0xX4ZYocMOFhzfLe6V7hyozaTejFF1/EC+vOO+/ktrS6y+SswlQCh2mBNm3asH6WyTMktCBHiddddx17G5urmdwylbBaangHyO4PMLZq1SosKJyAyq5QNobVOG2N1ZdqVDpoAfjHVRyjCRpTHouTc/zBcSJmqSg3SwEscyLDKSANCYvi+vXrK1SoQOpkzEweScd1E8IMjFa96KKL2IALnBY5jyQCb3ZQC7DJad68Ob5rxYoVa9u27fbt2wUltjfvv/8+/ytXrsQEwiYncA58IIwltZ07d/br1w/HR9wHZbR4PdStW5deLIOdLvqiZSycMh6PsAIVHMIeP97GMjbUE58PsWCJpWcVOGU8CcF4VbFG5aa0etmoiNcary+rQcoAmRMZTg0DguLy5ctLlCiRkKOERzch9uCYFTlH5OMXRpT6AdrEeKRgAra4BfnBEGczvDkBtP3Bp2Bb5hAz54oVK3jA4J/lFoc0WCtFA2w5OMBhs6EXI8XQNWTIELkvsCtOWxuPl66o2KF07NiRV4pYCuLjnD9/fl7IaF6+ccSllx8d4R91gE0e27JMzhWn3MAHjCgGDhyIYXjAgAEsA7F1WVT4mpBzQd4N1PJppg/kSXaxOAGPDCeJ1mN3QZGIAGwEPB4zg5mXDd9f4iZQv359L4Sef/55HDFYa6R+gDb2XB4nWwsNL8OQWoA4A0RlCT8MnAI52yW+H2XTy5fFvP0uvvhiVqqcGbNuYmHF2tDiIZWAPF4ZTg0P/iii03FOefXVVxNl0h+5RKko2hstcFA4Ac5EgKismQsDp4UcAEcm1AFbAKyGvNAwGaT3QEserwzLPIcH+6OICwbrKQ7LE2XMH7lEqSjaGy1wUDgBzkSAqKyZCwOnhVxDQB6vDKeG1RRTTDE5pwzzOItMiZGAkUBWScBogayabjNYIwEXCWh9FuDC779FHGGwiIpV6688wGMRzdnzJx/TK4MlEEm7QAbPhxmakIC8VZbh1MgnxRRTTM4pw0iuBZzDMCVGAgFKIIzVnIK9ANehCiqKKrMWUAjHVKVNAvLrUYbTxpCSsMyhDCs7aVRprIMaTYZhxUggLRIwWiAtYjdEjQQ0koCxCwQzGeL7hWBwecDCThKvdQ8NTRMjgfgSiKQWCOORS/K54hMm5wdO8cXvtwWbT79dTT8jAbsEIqkFwnjkzHNlvzXMddZIwNgFtJ5qoiFozZ9hLiMkYLSAvtM4c+ZMkXxVXxYNZxkhgUjuCDJC8nEGgfGP6HSErIjTzlQbCSQtAbMWSFqE4SAQmW08Rq0JhwWDNVskEEnfQVf3LNJpEtWL4wNRy7u0QIECxMwgrh4xNuVyQn05Eyi54vR+FyTZ3UaI+GgEpSN2NQE/bFXiMlhyriTSWygPUIbTy1Us6jKHMhyrvW7lB3cEWN2HDx9O0GXSeFSvXp2ws0uXLiWGf0LsEqGUHJikA+HZS6hjII3JvbdkyRJC5ZE9nawbRI8meBackECKx4mwkATeJjMaMQhJvtSoUaNAiIaEZPDgwWRYjKUCQiJq0GavBDjlJgEBAfwfeeQRYH5EmyNPGQlVxKX3f2LaIkfiVXrv4q8lVGJ1HDlyJLX82xo89thjlBNgz1ZuXSpwWm0UQJLdZcwkvSNaOemu5EIbHCA5G2ZNLuUByrAm7NnYkDmUYVszbS8PrAWIjUtg3C5dugDzq1KlCkmKateuLS4JQE4WY+L5ksKU1Y4odP3nGeM1m16zNiF94U38y0zGKpfbaAITo7pbt27OPYsm7Bk2Mk8CeVj5T5kyxVIBjJB43qxFCT6LaiDtNxFpicfMSpuEJcAKEZQqVYqMgOzGFW1MlVoCZH9A7ZLTTt3M1BoJBCiBPGQlI7OynGWdTF4kq4AG+RjYSOfm5hKRFpM1C4E+ffoESNugckqAhJYYBYwmdUrGlIQngQPJVTEHkglD0MA6TUTqOnXqcMljP2vWLIs28erJV2VdGiBwCWCLISI49svAMRuERgIKCeSQM0tk7xKNWAiQskYYBf7/35/V+YMPPrCMBVahP4Alxtdff+2vr5deHFUQWl9uiWlDvnSF1VYP1y6JFpKYjMwfpP2DH2ASk3GESZ4irACkxH3ggQfIbEEOJS9ow+CWT6o4LfJCPQVtGKDFTxiDDXYIaeTWkpK/EZUtWzZHzj/BGTu5sSpVquQ86mN3wN0psoDHIjZ58mQyf73wwgtxc92TBjsWEi/lce8JssTbjjnRC8uWLVMjx4SrbqCoVbOE2R/jCx7BPPyYVypXrsxZZtGiRVn5I3N+5ALC8oJfQ8uWLWnQokUL0kY6Z0FmIBluZTx6wsjTGqAMG27DkEAOhn2s+jyW2AJ5VDhsb9q0qY0S5+0824sWLcKbwFYlX7K54HCeGz2uFpB7ZTZM5i/e8EgGIY8ePZozFDS3c8hsxDjdJNUyqyTkPG3aNDIIkiEPMwFHNs72psRIIEgJoHFJTclCFBMAKYl54ZNEkULrt2DBgu7du4scm2wKrHInsH///g0bNjjLAy9h/LFwjh07llr+bQ0mTpxIOfknbeXWpQKn1UYBOLuTrwovDFwAn3766ViJki2EPXr0IOG6dQnAfgFu2Slw7OJMFe0kJ/fNAFgeoAzrOTSZQxnWk1snVwe2oJzwcQuSU50tK0YBcUBAOb+FCxdyN48aNYrtKwtXDhREues/GqR8+fKuVVlViJSRGAnqGzRogEdju3btOIVRSICFGKswci7LbVh28TURaysA1lYvv/yyXGtgI4EAJXCYIYq1KPtVcrYLAmwEUA1du3ZFBbCO5VbGAz9A2hmJCkdMVvI8tPhTc+zvuv63DVzhJlS4cOERI0awTEMjdO7cmdWWra+5NBJIXgIH96hsXDGe4fzLFwT8N2vWrFChQvgRz507V6ZBrnX5UkOYTyHgikfRxpsoF/+2qgAvWbo3btyYVQBGVi/PP6SFmxAyV7BRo0YNJgiT4dVXX42BxngTKGRlqnxI4KAllpUnjuscWXFQhAmAE0H1ItYHpQC7uBqNsbHPmzcPA9uOHTswv9eqVathw4atWrWaMWMG71J03Pbt21ldMzS8IVil2/hxxWlro7gU3fHFLl26dN++fRUtbVV88sTOn0MNW7nzkh0ZKwJGxCFCktw6ketWIg9QhnXjU/AjcyjDenLr5OrQeYyzTtuSMASdJE5/3dk4DBo0CJurRx8Ba0b8kbO66w/IA5RhPTmXOZRhPbl1cnWYXcBZbUpClQB2lieeeCJRFRAqSwZ5FkrArAUOTnqSKjzJ7oneeSkmlyh7ybeXByjDyWMOA4PMoQyHQSsMnGYtEIZUDU4jgShJwGiBKM2W4dVIIAwJGC0QhlQNTiOBKEnAxaddf/Y5imf3FSyfHo/3gyVqsBkJ6CCBSGoBTs51kJ3MQxiKScZvg43OsgnEXCYjgUhqgWQGHFLfRBWTbEmW4ZDYM2iNBBQSMHYBhXBMlZFAVkjAaIGsmGYzSCMBhQSMFlAIx1QZCWSFBIwWyIppNoM0ElBIwGgBhXBMlZFAVkjAaIGsmGYzSCMBhQS01gKEPyNXmoJ7U2UkYCSQvAS01gJEOiKAb/KD1AQDSk2kS43FDwELaUCzWA0yuxyN/+STTyrGSBJ6cuoqGqSyKpNmU2stMGnSJELrpHJqQ6VFHAFCoQ0bNiwWlaFDh5KbKGvDDRDwklDRixcvdpUPTx1Zm4jU7lqb+sKMmk1nWGJNSnhgyJKqCTNBsUE0N0KhkQCKu9bCKWBCv1IlIr5bVdkGkLGKSSc2nFM+BGUiO4NWAsmY2Tx0L2olX5ghGxLBuXXjKnl+Nm3axI1uu8sJW0ohS+Lk8Ucdw+zZs4ngbpMPKpIAjbhp6za6zJhNfbUA2RNJlKTbrAfCz5w5c4g1xhJXYCNRHDmjX3311UCQZwCS3r17EyfWGggBXXVWkRkwm/HtAuvXrydb1oABA/r168e2jblBT7v+fvzxRwL+skxyrU2okHRJ5PPRP/B5QoOyGjdp0uS6667jsRclWLy40QlhbjXIcoBsl0TBF/sCREHKxl69eiEiPcWSCbNpaVxXgKe6XLlyJCwXtUwGwbBdW1LYvn175ok0O7EaeC/HhEbSNO/tI9cS3UraAsF2zZo1SQAVuSGEyvBTTz1FshZI8GphIaDhXkAeftRnU7Uj2Lp1K7lJxo8fbw2YVDnVq1e3Lm0AmTlbt27tZXPLGjg1el3z7/Dz5s2bGjl4pKKbuHTjRy3GdM1mklI68DDanmT5sn///gxbfqrJ7fHpp5/KbcKAic9PEp4wMGuFc82aNfXr18ciAKAVY5owM2bMGPahZ5xxxr59+zRhScFGpGdTFWVk1apVZCgqWbKkpQJJvG3B4QG4CbRt2zY8/JpgJiU59zfHziY3ueuMkLIJ7wByPZIyy7WBVoWRnk2VFkDK2LH5pdKPhYSc8+fPzySXQcXNOnLkyMADKCrIRasqX758pH7Ozc2NCtvRnU2VFsAEgB0bRw7ZVr93795QMxejAkidrnOWxABvStKQBogt81BFy3M0wrOp2OpgHeTYFp8tuU2nTp04w5NLLHj58uUdOnQg66lVEgsw1kHxxCZp1wn8sTf8JCPSdEkvSbpxrIM8w3hEYBewHFrYsU+fPj3Ws33LLbcgROFTEKuNuhyv4apVq7q2YWPyySefkLebUxnXBqbQSMBIwJ8E4ucpxIEHF4Cff/65VKlSF1544fnnnx9LX27ZsuX1119nFVegQIFYbdTlaBCOJHBScjbr2rVrvXr1sKiTF5zT43HjxjnbmBIjASMBHxKIrwV8IPXdhYecHL74KTkxjB49Gvfy008/HU3x4osvvvbaa842psRIwEjAhwTiexD7QOqvy+bNm/E+dlUBIOzSpQtG4wkTJrA9efjhh/2RML2MBIwEnBLQSAtMmTKlTZs2ThatEiwUfGKEP/nUqVOtQgMYCRgJJCkBjXYEHEwuXLiwSJEiziHhRMCHxuI7hW3btp144onYCE877TRnS1NiJGAkkKgEdFkLrF69GkcxVxXAkH755RcOLPG0A16xYgUfnGn7hVmiE2DaGwmkXQK6rAV27txJVLmKFSvGkgifMKxdu1Y069y5MwcWsVrqX85hyuTJk3G74GPNli1btmjRQn+eU8khSz8+IkDRE3+NdwNeZKmknigtjs+IksZ+lq/Fr7nmmkS769Be5TuYSv7+++9PQZFDSn6KBhGqwjee885q1apxl5966qkcfJhPCeTpw3+ch79Zs2YUNmzYsFKlSsWLF5cbaAXzKSGfPBUrVozZ1Iox78zosiPwznEGtOSlgVMmAzn22GPxrcBHMwMGFeAQpk2bVrlyZYEQGxBO5QEiDxwVk1i+fPlIfw+iy1og8LnRGaF4y8EhtzubILwkdOY29bxxZkwkC0EXdcll6nnIKopmLZC26cYXG59ofLTTFZ0ibSOPR7hgwYK7d+8WrQC4jNfD1CclAaMFkhKf785E1OJAhIOP999/n/NR33gysiPhhgnKLIZG9EECtGfkMPUZlNkRpGEu+C5T/g7C2AVsczB8+PDBgwcff/zxGzduxIsEM6qtgVaXnG3xlR0f1LJz2bFjR/fu3bVizwsz/wPOBTl7l93+NgAAAABJRU5ErkJggg==",
|
|
"text/plain": [
|
|
"<PIL.Image.Image image mode=RGB size=343x132>"
|
|
]
|
|
},
|
|
"execution_count": 6,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"# Latex drawing\n",
|
|
"circuit.draw(output=\"latex\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "78688e6c-3ed2-4ef2-b3db-80fdae35e585",
|
|
"metadata": {
|
|
"jp-MarkdownHeadingCollapsed": true
|
|
},
|
|
"source": [
|
|
"### Control circuit drawings\n",
|
|
"\n",
|
|
"By default, the `draw()` method returns the rendered image as an object and does not output anything. The exact class returned depends on the output specified: `'text'` (the default) returns a `TextDrawer` object, `'mpl'` returns a `matplotlib.Figure` object, and `latex` returns a `PIL.Image` object. Jupyter notebooks understand these return types and render them properly, but when running outside of Jupyter, images will not display automatically.\n",
|
|
"\n",
|
|
"The `draw()` method has optional arguments to display or save the output. When specified, the `filename` kwarg takes a path to which it saves the rendered output. Alternatively, if you're using the `mpl` or `latex` outputs, you can use the `interactive` kwarg to open the image in a new window (this will not always work from within a notebook)."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "fc41270b-3518-40af-8921-dfa9666fc8e0",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Customize the output\n",
|
|
"\n",
|
|
"Depending on the output, there are also options to customize the circuit diagram.\n",
|
|
"\n",
|
|
"#### Disable plot barriers and reverse bit order\n",
|
|
"The first two options are shared among all three backends. They allow you to configure both the bit orders and whether or not you draw barriers. These can be set by the `reverse_bits` kwarg and `plot_barriers` kwarg, respectively. The following examples work with any output renderer; `mpl` is used here for brevity."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 7,
|
|
"id": "db61dc71-4cdc-4bb7-8139-8820e7785e55",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"from qiskit import QuantumRegister, ClassicalRegister\n",
|
|
"\n",
|
|
"# Draw a new circuit with barriers and more registers\n",
|
|
"q_a = QuantumRegister(3, name=\"a\")\n",
|
|
"q_b = QuantumRegister(5, name=\"b\")\n",
|
|
"c_a = ClassicalRegister(3)\n",
|
|
"c_b = ClassicalRegister(5)\n",
|
|
"\n",
|
|
"circuit = QuantumCircuit(q_a, q_b, c_a, c_b)\n",
|
|
"circuit.x(q_a[1])\n",
|
|
"circuit.x(q_b[1])\n",
|
|
"circuit.x(q_b[2])\n",
|
|
"circuit.x(q_b[4])\n",
|
|
"circuit.barrier()\n",
|
|
"circuit.h(q_a)\n",
|
|
"circuit.barrier(q_a)\n",
|
|
"circuit.h(q_b)\n",
|
|
"circuit.cswap(q_b[0], q_b[1], q_b[2])\n",
|
|
"circuit.cswap(q_b[2], q_b[3], q_b[4])\n",
|
|
"circuit.cswap(q_b[3], q_b[4], q_b[0])\n",
|
|
"circuit.barrier(q_b)\n",
|
|
"circuit.measure(q_a, c_a)\n",
|
|
"circuit.measure(q_b, c_b);"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 8,
|
|
"id": "8e57cd43-8a48-469d-8f69-8e7c936d4a1e",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/svg+xml": [
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|
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|
],
|
|
"text/plain": [
|
|
"<Figure size 1374.64x869.556 with 1 Axes>"
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|
]
|
|
},
|
|
"execution_count": 8,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"# Draw the circuit\n",
|
|
"circuit.draw(output=\"mpl\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 9,
|
|
"id": "8e7a251a-0a4f-43e0-8cf5-48493df7bad9",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
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|
"image/svg+xml": [
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|
],
|
|
"text/plain": [
|
|
"<Figure size 1123.81x869.556 with 1 Axes>"
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|
]
|
|
},
|
|
"execution_count": 9,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"# Draw the circuit with reversed bit order\n",
|
|
"circuit.draw(output=\"mpl\", reverse_bits=True)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 10,
|
|
"id": "b4a601ad-1c04-4b16-afbd-ac5a0ad42653",
|
|
"metadata": {},
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|
"outputs": [
|
|
{
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|
"data": {
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|
"image/svg+xml": [
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],
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"text/plain": [
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"<Figure size 1207.42x869.556 with 1 Axes>"
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]
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},
|
|
"execution_count": 10,
|
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"metadata": {},
|
|
"output_type": "execute_result"
|
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}
|
|
],
|
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"source": [
|
|
"# Draw the circuit without barriers\n",
|
|
"circuit.draw(output=\"mpl\", plot_barriers=False)"
|
|
]
|
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},
|
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{
|
|
"cell_type": "markdown",
|
|
"id": "18b0bfbe-b586-4182-9a0d-b858655a9240",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Renderer-specific customizations\n",
|
|
"\n",
|
|
"Some available customizing options are specific to a renderer.\n",
|
|
"\n",
|
|
"The `fold` argument sets a maximum width for the output. In the `text` renderer, this sets the length of the lines of the diagram before it is wrapped to the next line. When using the 'mpl' renderer, this is the number of (visual) layers before folding to the next line.\n",
|
|
"\n",
|
|
"The `mpl` renderer has the `style` kwarg, which changes the colors and outlines. See the [API documentation](/api/qiskit/qiskit.circuit.QuantumCircuit#draw) for more details.\n",
|
|
"\n",
|
|
"The `scale` option scales the output of the `mpl` and `latex` renderers."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
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|
"execution_count": 11,
|
|
"id": "1e08bf74-378f-45e6-b195-a9539061013d",
|
|
"metadata": {},
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|
"outputs": [
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{
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"data": {
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"text/html": [
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"<pre style=\"word-wrap: normal;white-space: pre;background: #fff0;line-height: 1.1;font-family: "Courier New",Courier,monospace\"> ┌───┐┌───┐┌───┐┌───┐┌───┐┌───┐┌───┐»\n",
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"q: ┤ H ├┤ H ├┤ H ├┤ H ├┤ H ├┤ H ├┤ H ├»\n",
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" └───┘└───┘└───┘└───┘└───┘└───┘└───┘»\n",
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"« ┌───┐┌───┐┌───┐\n",
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"«q: ┤ H ├┤ H ├┤ H ├\n",
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"« └───┘└───┘└───┘</pre>"
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],
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"text/plain": [
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" ┌───┐┌───┐┌───┐┌───┐┌───┐┌───┐┌───┐»\n",
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"q: ┤ H ├┤ H ├┤ H ├┤ H ├┤ H ├┤ H ├┤ H ├»\n",
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" └───┘└───┘└───┘└───┘└───┘└───┘└───┘»\n",
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"« ┌───┐┌───┐┌───┐\n",
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"«q: ┤ H ├┤ H ├┤ H ├\n",
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"« └───┘└───┘└───┘"
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]
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},
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"execution_count": 11,
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"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"circuit = QuantumCircuit(1)\n",
|
|
"for _ in range(10):\n",
|
|
" circuit.h(0)\n",
|
|
"# limit line length to 40 characters\n",
|
|
"circuit.draw(output=\"text\", fold=40)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 12,
|
|
"id": "decadf88-4866-45a0-9e2f-836c51491f9e",
|
|
"metadata": {},
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"outputs": [
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{
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"data": {
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"image/svg+xml": [
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],
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"text/plain": [
|
|
"<Figure size 937.953x117.056 with 1 Axes>"
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]
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|
},
|
|
"execution_count": 12,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
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|
}
|
|
],
|
|
"source": [
|
|
"# Change the background color in mpl\n",
|
|
"\n",
|
|
"style = {\"backgroundcolor\": \"lightgreen\"}\n",
|
|
"circuit.draw(output=\"mpl\", style=style)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 13,
|
|
"id": "ade9a653-3243-4ac9-bb0e-c8fb82f7a034",
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"metadata": {},
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"outputs": [
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{
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"image/svg+xml": [
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],
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"text/plain": [
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"<Figure size 468.976x58.5278 with 1 Axes>"
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]
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},
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"execution_count": 13,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
|
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"# Scale the mpl output to 1/2 the normal size\n",
|
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"circuit.draw(output=\"mpl\", scale=0.5)"
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|
]
|
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},
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{
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"cell_type": "markdown",
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"id": "495b560a-d835-448b-aa01-5025a7a0689e",
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"metadata": {},
|
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"source": [
|
|
"### Standalone circuit-drawing function\n",
|
|
"\n",
|
|
"If you have an application where you prefer to draw a circuit with a self-contained function instead of as a method of a circuit object, you can directly use the `circuit_drawer()` function, which is part of the public stable interface from `qiskit.visualization`. The function behaves identically to the `circuit.draw()` method, except that it takes in a circuit object as a required argument."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"id": "256dd092-b2eb-47af-a025-0ecdf85c2d5a",
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"metadata": {},
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"outputs": [
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{
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"data": {
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],
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|
"text/plain": [
|
|
"<Figure size 937.953x117.056 with 1 Axes>"
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]
|
|
},
|
|
"execution_count": 14,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"from qiskit.visualization import circuit_drawer\n",
|
|
"\n",
|
|
"circuit_drawer(circuit, output=\"mpl\", plot_barriers=False)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "0761df2f-183f-48e3-9070-766a9920c2b9",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Next steps\n",
|
|
"\n",
|
|
"<Admonition type=\"tip\" title=\"Recommendations\">\n",
|
|
" - See an example of circuit visualization in the [Grover's Algorithm](https://learning.quantum.ibm.com/tutorial/grovers-algorithm) tutorial.\n",
|
|
" - Visualize simple circuits in the [Explore gates and circuits with the Quantum Composer](https://learning.quantum.ibm.com/tutorial/explore-gates-and-circuits-with-the-quantum-composer) tutorial.\n",
|
|
" - Review the [Qiskit visualizations API documentation](/api/qiskit/visualization).\n",
|
|
"</Admonition>"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"description": "Create visualizations of circuits and plot job data using the Qiskit visualization module ",
|
|
"kernelspec": {
|
|
"display_name": "Python 3",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
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"version": 3
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},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3"
|
|
},
|
|
"title": "Visualize circuits"
|
|
},
|
|
"nbformat": 4,
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"nbformat_minor": 4
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}
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