Lab4_QCBM_Solution.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Quantum Machine Learning Workshop\n",
    "\n",
    "### PyQuil implementation of the Data-Driven Quantum Circuit Learning (DDQCL) Algorithm "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Authors: Vicente Leyton-Ortega (ORNL) and Alejandro Perdomo-Ortiz\n",
    "(Zapata Computing)\n",
    "\n",
    "Summary: Quick (and probably suboptimal) implementation of DDQCL in pyQuil/Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib notebook\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "# NOTE: IF the next window doesn't run, open two terminals and run:\n",
    "# qvm -S\n",
    "# quilc -S"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#load the necessary packages and libraries\n",
    "from pyquil.quil import Program   \n",
    "import pyquil.api as api\n",
    "from pyquil.gates import *\n",
    "\n",
    "from utils import *\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from random import *\n",
    "import itertools\n",
    "from pso import PSO\n",
    "import time\n",
    "\n",
    "qvm = api.QVMConnection()  \n",
    "PRECISION = 8\n",
    "CLIP = 1e-8"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bars & Stripes data set\n",
    "\n",
    "Here we consider an $n \\times m$ data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "/* global mpl */\n",
       "window.mpl = {};\n",
       "\n",
       "mpl.get_websocket_type = function () {\n",
       "    if (typeof WebSocket !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof MozWebSocket !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert(\n",
       "            'Your browser does not have WebSocket support. ' +\n",
       "                'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "                'Firefox 4 and 5 are also supported but you ' +\n",
       "                'have to enable WebSockets in about:config.'\n",
       "        );\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = this.ws.binaryType !== undefined;\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById('mpl-warnings');\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent =\n",
       "                'This browser does not support binary websocket messages. ' +\n",
       "                'Performance may be slow.';\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = document.createElement('div');\n",
       "    this.root.setAttribute('style', 'display: inline-block');\n",
       "    this._root_extra_style(this.root);\n",
       "\n",
       "    parent_element.appendChild(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen = function () {\n",
       "        fig.send_message('supports_binary', { value: fig.supports_binary });\n",
       "        fig.send_message('send_image_mode', {});\n",
       "        if (fig.ratio !== 1) {\n",
       "            fig.send_message('set_device_pixel_ratio', {\n",
       "                device_pixel_ratio: fig.ratio,\n",
       "            });\n",
       "        }\n",
       "        fig.send_message('refresh', {});\n",
       "    };\n",
       "\n",
       "    this.imageObj.onload = function () {\n",
       "        if (fig.image_mode === 'full') {\n",
       "            // Full images could contain transparency (where diff images\n",
       "            // almost always do), so we need to clear the canvas so that\n",
       "            // there is no ghosting.\n",
       "            fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "        }\n",
       "        fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "    };\n",
       "\n",
       "    this.imageObj.onunload = function () {\n",
       "        fig.ws.close();\n",
       "    };\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_header = function () {\n",
       "    var titlebar = document.createElement('div');\n",
       "    titlebar.classList =\n",
       "        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
       "    var titletext = document.createElement('div');\n",
       "    titletext.classList = 'ui-dialog-title';\n",
       "    titletext.setAttribute(\n",
       "        'style',\n",
       "        'width: 100%; text-align: center; padding: 3px;'\n",
       "    );\n",
       "    titlebar.appendChild(titletext);\n",
       "    this.root.appendChild(titlebar);\n",
       "    this.header = titletext;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = (this.canvas_div = document.createElement('div'));\n",
       "    canvas_div.setAttribute(\n",
       "        'style',\n",
       "        'border: 1px solid #ddd;' +\n",
       "            'box-sizing: content-box;' +\n",
       "            'clear: both;' +\n",
       "            'min-height: 1px;' +\n",
       "            'min-width: 1px;' +\n",
       "            'outline: 0;' +\n",
       "            'overflow: hidden;' +\n",
       "            'position: relative;' +\n",
       "            'resize: both;'\n",
       "    );\n",
       "\n",
       "    function on_keyboard_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.key_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'keydown',\n",
       "        on_keyboard_event_closure('key_press')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'keyup',\n",
       "        on_keyboard_event_closure('key_release')\n",
       "    );\n",
       "\n",
       "    this._canvas_extra_style(canvas_div);\n",
       "    this.root.appendChild(canvas_div);\n",
       "\n",
       "    var canvas = (this.canvas = document.createElement('canvas'));\n",
       "    canvas.classList.add('mpl-canvas');\n",
       "    canvas.setAttribute('style', 'box-sizing: content-box;');\n",
       "\n",
       "    this.context = canvas.getContext('2d');\n",
       "\n",
       "    var backingStore =\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        this.context.webkitBackingStorePixelRatio ||\n",
       "        this.context.mozBackingStorePixelRatio ||\n",
       "        this.context.msBackingStorePixelRatio ||\n",
       "        this.context.oBackingStorePixelRatio ||\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        1;\n",
       "\n",
       "    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
       "        'canvas'\n",
       "    ));\n",
       "    rubberband_canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
       "    );\n",
       "\n",
       "    // Apply a ponyfill if ResizeObserver is not implemented by browser.\n",
       "    if (this.ResizeObserver === undefined) {\n",
       "        if (window.ResizeObserver !== undefined) {\n",
       "            this.ResizeObserver = window.ResizeObserver;\n",
       "        } else {\n",
       "            var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n",
       "            this.ResizeObserver = obs.ResizeObserver;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n",
       "        var nentries = entries.length;\n",
       "        for (var i = 0; i < nentries; i++) {\n",
       "            var entry = entries[i];\n",
       "            var width, height;\n",
       "            if (entry.contentBoxSize) {\n",
       "                if (entry.contentBoxSize instanceof Array) {\n",
       "                    // Chrome 84 implements new version of spec.\n",
       "                    width = entry.contentBoxSize[0].inlineSize;\n",
       "                    height = entry.contentBoxSize[0].blockSize;\n",
       "                } else {\n",
       "                    // Firefox implements old version of spec.\n",
       "                    width = entry.contentBoxSize.inlineSize;\n",
       "                    height = entry.contentBoxSize.blockSize;\n",
       "                }\n",
       "            } else {\n",
       "                // Chrome <84 implements even older version of spec.\n",
       "                width = entry.contentRect.width;\n",
       "                height = entry.contentRect.height;\n",
       "            }\n",
       "\n",
       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
       "            // the canvas container.\n",
       "            if (entry.devicePixelContentBoxSize) {\n",
       "                // Chrome 84 implements new version of spec.\n",
       "                canvas.setAttribute(\n",
       "                    'width',\n",
       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
       "                );\n",
       "                canvas.setAttribute(\n",
       "                    'height',\n",
       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
       "                );\n",
       "            } else {\n",
       "                canvas.setAttribute('width', width * fig.ratio);\n",
       "                canvas.setAttribute('height', height * fig.ratio);\n",
       "            }\n",
       "            canvas.setAttribute(\n",
       "                'style',\n",
       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
       "            );\n",
       "\n",
       "            rubberband_canvas.setAttribute('width', width);\n",
       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
       "            // And update the size in Python. We ignore the initial 0/0 size\n",
       "            // that occurs as the element is placed into the DOM, which should\n",
       "            // otherwise not happen due to the minimum size styling.\n",
       "            if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n",
       "                fig.request_resize(width, height);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "    this.resizeObserverInstance.observe(canvas_div);\n",
       "\n",
       "    function on_mouse_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.mouse_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'dblclick',\n",
       "        on_mouse_event_closure('dblclick')\n",
       "    );\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousemove',\n",
       "        on_mouse_event_closure('motion_notify')\n",
       "    );\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseenter',\n",
       "        on_mouse_event_closure('figure_enter')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseleave',\n",
       "        on_mouse_event_closure('figure_leave')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener('wheel', function (event) {\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        on_mouse_event_closure('scroll')(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.appendChild(canvas);\n",
       "    canvas_div.appendChild(rubberband_canvas);\n",
       "\n",
       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
       "    this.rubberband_context.strokeStyle = '#000000';\n",
       "\n",
       "    this._resize_canvas = function (width, height, forward) {\n",
       "        if (forward) {\n",
       "            canvas_div.style.width = width + 'px';\n",
       "            canvas_div.style.height = height + 'px';\n",
       "        }\n",
       "    };\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
       "        event.preventDefault();\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus() {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'mpl-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', { width: x_pixels, height: y_pixels });\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_message = function (type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function () {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function (fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
       "    fig.rubberband_canvas.style.cursor = msg['cursor'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message('ack', {});\n",
       "};\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            var img = evt.data;\n",
       "            if (img.type !== 'image/png') {\n",
       "                /* FIXME: We get \"Resource interpreted as Image but\n",
       "                 * transferred with MIME type text/plain:\" errors on\n",
       "                 * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "                 * to be part of the websocket stream */\n",
       "                img.type = 'image/png';\n",
       "            }\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src\n",
       "                );\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                img\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig['handle_' + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "};\n",
       "\n",
       "// from https://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function (e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e) {\n",
       "        e = window.event;\n",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n",
       "\n",
       "    return { x: x, y: y };\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * https://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys(original) {\n",
       "    return Object.keys(original).reduce(function (obj, key) {\n",
       "        if (typeof original[key] !== 'object') {\n",
       "            obj[key] = original[key];\n",
       "        }\n",
       "        return obj;\n",
       "    }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function (event, name) {\n",
       "    var canvas_pos = mpl.findpos(event);\n",
       "\n",
       "    if (name === 'button_press') {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.key_event = function (event, name) {\n",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.key === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.key;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.key !== 'Control') {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    else if (event.altKey && event.key !== 'Alt') {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    else if (event.shiftKey && event.key !== 'Shift') {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k' + event.key;\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
       "    if (name === 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message('toolbar_button', { name: name });\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "\n",
       "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
       "// prettier-ignore\n",
       "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "var comm_websocket_adapter = function (comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.binaryType = comm.kernel.ws.binaryType;\n",
       "    ws.readyState = comm.kernel.ws.readyState;\n",
       "    function updateReadyState(_event) {\n",
       "        if (comm.kernel.ws) {\n",
       "            ws.readyState = comm.kernel.ws.readyState;\n",
       "        } else {\n",
       "            ws.readyState = 3; // Closed state.\n",
       "        }\n",
       "    }\n",
       "    comm.kernel.ws.addEventListener('open', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('close', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('error', updateReadyState);\n",
       "\n",
       "    ws.close = function () {\n",
       "        comm.close();\n",
       "    };\n",
       "    ws.send = function (m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function (msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        var data = msg['content']['data'];\n",
       "        if (data['blob'] !== undefined) {\n",
       "            data = {\n",
       "                data: new Blob(msg['buffers'], { type: data['blob'] }),\n",
       "            };\n",
       "        }\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(data);\n",
       "    });\n",
       "    return ws;\n",
       "};\n",
       "\n",
       "mpl.mpl_figure_comm = function (comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element;\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error('Failed to find cell for figure', id, fig);\n",
       "        return;\n",
       "    }\n",
       "    fig.cell_info[0].output_area.element.on(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "    fig.resizeObserverInstance.unobserve(fig.canvas_div);\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable();\n",
       "    fig.parent_element.innerHTML =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "    fig.close_ws(fig, msg);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message('ack', {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    if (event.target !== this) {\n",
       "        // Ignore bubbled events from children.\n",
       "        return;\n",
       "    }\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('tabindex', 0);\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "};\n",
       "\n",
       "mpl.find_output_cell = function (html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i = 0; i < ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code') {\n",
       "            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] === html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel !== null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"900\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/luisserrano/opt/anaconda3/lib/python3.7/site-packages/matplotlib/text.py:1223: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if s != self._text:\n"
     ]
    }
   ],
   "source": [
    "global n_qubits, hist_sample, n, m\n",
    "\n",
    "n , m =  2, 2\n",
    "\n",
    "bas = bars_and_stripes(n,m)\n",
    "\n",
    "n_points, n_qubits  =  bas.shape\n",
    "\n",
    "fig, ax_b = plt.subplots(1, bas.shape[0], figsize=(9,2))   #visualization of bars ans stripes data set\n",
    "\n",
    "for i in range(bas.shape[0]):\n",
    "    ax_b[i].matshow(bas[i].reshape(n, m), vmin=-1, vmax=1)\n",
    "    \n",
    "    ax_b[i].set_xticks([])\n",
    "    ax_b[i].set_yticks([])\n",
    "    \n",
    "    ax_b[i].set_xticks([0.5], minor=True);\n",
    "    ax_b[i].set_yticks([0.5], minor=True);\n",
    "    \n",
    "    ax_b[i].set_title(bas[i])\n",
    "    ax_b[i].grid(which='minor', color='black', linestyle='-', linewidth=0.75)\n",
    "    \n",
    "'''sample distribution'''\n",
    "hist_sample = get_histogram(bas)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Topologies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx \n",
    "\n",
    "edges_all_connected = []\n",
    "edges_star          = []\n",
    "edges_line          = []\n",
    "\n",
    "''' line topology'''\n",
    "for i in range(n_qubits-1):\n",
    "    connections = [i,i+1]\n",
    "    edges_line.append(connections)\n",
    "\n",
    "\n",
    "'''star topology'''\n",
    "for i in range(1,n_qubits):\n",
    "    connections = [0,i]\n",
    "    edges_star.append(connections)\n",
    "\n",
    "\n",
    "'''all connected topology'''\n",
    "for i in range(n_qubits-1):\n",
    "    for j in range(i+1,n_qubits):\n",
    "        connections = [i,j]\n",
    "        edges_all_connected.append(connections)\n",
    "\n",
    "# Or build any other you would like to explore. For example, ring topology?\n",
    "        \n",
    "        \n",
    "'''lets define a little function for the connections'''\n",
    "def top(x):\n",
    "    if x == 0:\n",
    "        edges = edges_line\n",
    "    if x == 1: \n",
    "        edges = edges_star\n",
    "    if x == 2:\n",
    "        edges = edges_all_connected\n",
    "    return edges"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Circuit parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "global n_top, single_g, entangling_g, n_layers, dimension, machine\n",
    "global min_bounds, max_bounds, n_rotations, init_state, n_measurements, q_arrange \n",
    "\n",
    "'''define the topology and initial state'''\n",
    "n_top = 2             # options: 0 - line, 1 - star,  2 - all connected \n",
    "init_state = 'zero'   # options: zero and hadammard\n",
    "\n",
    "'''define single and entangling gates'''\n",
    "single_g      =  ['RY','RZ']   \n",
    "entangling_g  =  ['YY']       # options YY, ZZ, CPHASE -- the entangling gate can be modify in the circuit           \n",
    "\n",
    "'''define the number of layers'''\n",
    "n_layers = 2\n",
    "\n",
    "'''number of parameters'''\n",
    "n_rotations   = len(single_g)\n",
    "even_n_layers = int(np.floor(n_layers/2))\n",
    "odd_n_layers  = n_layers - even_n_layers\n",
    "if single_g[0] == 'RZ' and init_state == 'zero':\n",
    "    dimension     = (n_rotations*odd_n_layers-1)*n_qubits + len(top(n_top))*even_n_layers  \n",
    "else:\n",
    "    dimension     = (n_rotations*odd_n_layers)*n_qubits + len(top(n_top))*even_n_layers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Quantum Circuit: Instructions to the quantum computer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def circuit(angles):\n",
    "    \n",
    "    if init_state == 'zero' :\n",
    "        C = Program([I(i) for i in range(n_qubits)])\n",
    "    elif init_state == 'hadamard':\n",
    "        C =  Program([H(i) for i in range(n_qubits)])\n",
    "    \n",
    "    i = 0\n",
    "    \n",
    "    for ly in range(1, n_layers + 1):\n",
    "        if ly % 2 == 1:                       #single gates are only in odd layers\n",
    "            for qb in range(n_qubits):\n",
    "                for op in single_g:\n",
    "                    \n",
    "                    x = angles[i]*np.pi       #rotating angles are written in units of pi \n",
    "                    gate = (op + \"(\"+str(x)+\")\",qb)\n",
    "                    C.inst(gate)\n",
    "                    \n",
    "                    i+=1                      #now it will call the next element in angles\n",
    "                                        \n",
    "        else:\n",
    "            for qb_qb in top(n_top):\n",
    "                if entangling_g[0] == 'YY':\n",
    "                    \n",
    "                    x = angles[i]*np.pi  #entangling angles are written in units of 2*pi\n",
    "                    idx1 = qb_qb[0]\n",
    "                    idx2 = qb_qb[1]\n",
    "                    \n",
    "                    C.inst(CNOT(idx1,idx2))\n",
    "                    C.inst(RY(x,idx2))\n",
    "                    C.inst(CNOT(idx1,idx2))\n",
    "                            \n",
    "                    i+=1\n",
    "                    \n",
    "                elif entangling_g[0] == 'ZZ':  #ZZ(theta,1,2) = CNOT(1,2) RZ(2 theta,2) CNOT(1,2), - pi < 2 theta < pi\n",
    "                    \n",
    "                    x = angles[i]*np.pi     # theta = x*pi/2, then -1 < x < 1\n",
    "                    idx1 = qb_qb[0]\n",
    "                    idx2 = qb_qb[1]\n",
    "                    \n",
    "                    C.inst(CNOT(idx1,idx2))\n",
    "                    C.inst(RZ(x,idx2))\n",
    "                    C.inst(CNOT(idx1,idx2))\n",
    "                    \n",
    "                    i+=1 \n",
    "                    \n",
    "                elif entangling_g[0] == 'XX':  #XX(theta,1,2) = CNOT(1,2) RX(2 theta,2) CNOT(1,2), - pi < 2 theta < pi\n",
    "                    \n",
    "                    x = angles[i]*np.pi     \n",
    "                    idx1 = qb_qb[0]\n",
    "                    idx2 = qb_qb[1]\n",
    "                    \n",
    "                    C.inst(CNOT(idx1,idx2))\n",
    "                    C.inst(RX(x,idx2))\n",
    "                    C.inst(CNOT(idx1,idx2))\n",
    "                    \n",
    "                    i+=1 \n",
    "                                               ## we can define more entangling gates!\n",
    "                else:\n",
    "                    \n",
    "                    x = angles[i]*np.pi \n",
    "                    idx1 = qb_qb[0]\n",
    "                    idx2 = qb_qb[1]\n",
    "                    gate = (entangling_g[0] + \"(\"+str(x)+\")\",idx1,idx2)\n",
    "                    C.inst(gate)\n",
    "                    \n",
    "    wf         =  qvm.wavefunction(C)     #get the output circuit wavefunction\n",
    "    probs_dist =  wf.get_outcome_probs()  #get the probability distribution --- be careful it is a dict\n",
    "    probs      =  list(range(2**n_qubits))\n",
    "    \n",
    "    '''here we extract the distribution values from the dict'''\n",
    "    for i in range(2**n_qubits):\n",
    "\n",
    "        aa = str(np.binary_repr(i,n_qubits))\n",
    "        probs[i]=probs_dist[aa]\n",
    "        \n",
    "    assert(round(sum(probs), PRECISION)==1.)  # just to make sure \n",
    "       \n",
    "    return probs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Kullback - Leibler cost function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def KL(angles):\n",
    "    l = 0.0\n",
    "    probs = circuit(angles)\n",
    "    for idx in range(2**n_qubits):\n",
    "        l += -hist_sample[idx] * np.log(np.clip(probs[idx], CLIP, 1.)) \\\n",
    "                 + hist_sample[idx] * np.log(np.clip(hist_sample[idx], CLIP, 1.))    \n",
    "    return l"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Optimizer: Particle Swarm optimization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "c1           = 1.0\n",
    "c2           = 1.0\n",
    "w            = 0.5\n",
    "particles    = 2*dimension\n",
    "n_iterations = 30\n",
    "\n",
    "np.random.seed(4)\n",
    "\n",
    "optimizer = PSO( \n",
    "            n_parameters = dimension, \n",
    "            n_particles  = particles,\n",
    "            phip         = 1.0, \n",
    "            phig         = 1.0,\n",
    "            omega        = 0.5,\n",
    "            type         = 'circular')\n",
    "\n",
    "batch =  optimizer.x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training process"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', { width: x_pixels, height: y_pixels });\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_message = function (type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function () {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function (fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
       "    fig.rubberband_canvas.style.cursor = msg['cursor'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message('ack', {});\n",
       "};\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            var img = evt.data;\n",
       "            if (img.type !== 'image/png') {\n",
       "                /* FIXME: We get \"Resource interpreted as Image but\n",
       "                 * transferred with MIME type text/plain:\" errors on\n",
       "                 * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "                 * to be part of the websocket stream */\n",
       "                img.type = 'image/png';\n",
       "            }\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src\n",
       "                );\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                img\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig['handle_' + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "};\n",
       "\n",
       "// from https://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function (e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e) {\n",
       "        e = window.event;\n",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n",
       "\n",
       "    return { x: x, y: y };\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * https://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys(original) {\n",
       "    return Object.keys(original).reduce(function (obj, key) {\n",
       "        if (typeof original[key] !== 'object') {\n",
       "            obj[key] = original[key];\n",
       "        }\n",
       "        return obj;\n",
       "    }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function (event, name) {\n",
       "    var canvas_pos = mpl.findpos(event);\n",
       "\n",
       "    if (name === 'button_press') {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.key_event = function (event, name) {\n",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.key === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.key;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.key !== 'Control') {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    else if (event.altKey && event.key !== 'Alt') {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    else if (event.shiftKey && event.key !== 'Shift') {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k' + event.key;\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
       "    if (name === 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message('toolbar_button', { name: name });\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "\n",
       "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
       "// prettier-ignore\n",
       "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "var comm_websocket_adapter = function (comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.binaryType = comm.kernel.ws.binaryType;\n",
       "    ws.readyState = comm.kernel.ws.readyState;\n",
       "    function updateReadyState(_event) {\n",
       "        if (comm.kernel.ws) {\n",
       "            ws.readyState = comm.kernel.ws.readyState;\n",
       "        } else {\n",
       "            ws.readyState = 3; // Closed state.\n",
       "        }\n",
       "    }\n",
       "    comm.kernel.ws.addEventListener('open', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('close', updateReadyState);\n",
       "    comm.kernel.ws.addEventListener('error', updateReadyState);\n",
       "\n",
       "    ws.close = function () {\n",
       "        comm.close();\n",
       "    };\n",
       "    ws.send = function (m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function (msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        var data = msg['content']['data'];\n",
       "        if (data['blob'] !== undefined) {\n",
       "            data = {\n",
       "                data: new Blob(msg['buffers'], { type: data['blob'] }),\n",
       "            };\n",
       "        }\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(data);\n",
       "    });\n",
       "    return ws;\n",
       "};\n",
       "\n",
       "mpl.mpl_figure_comm = function (comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element;\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error('Failed to find cell for figure', id, fig);\n",
       "        return;\n",
       "    }\n",
       "    fig.cell_info[0].output_area.element.on(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "    fig.resizeObserverInstance.unobserve(fig.canvas_div);\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable();\n",
       "    fig.parent_element.innerHTML =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "    fig.close_ws(fig, msg);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message('ack', {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    if (event.target !== this) {\n",
       "        // Ignore bubbled events from children.\n",
       "        return;\n",
       "    }\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('tabindex', 0);\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "};\n",
       "\n",
       "mpl.find_output_cell = function (html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i = 0; i < ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code') {\n",
       "            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] === html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel !== null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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\" width=\"900\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "save_filename = 'Trial1_top-all_'\n",
    "file_path = str(save_filename) + 'cost_and_angles_all.txt'\n",
    "f = open(file_path, 'w')\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(9, 4))\n",
    "ax_c = fig.add_axes([0.37,0.2,0.105,0.05])\n",
    "y_position, width, height = 0.7, 0.05, 0.07\n",
    "ax_b = []\n",
    "ax_b.append(fig.add_axes([0.55,y_position,width,height]))\n",
    "ax_b.append(fig.add_axes([0.606,y_position,width,height]))\n",
    "ax_b.append(fig.add_axes([0.65,y_position,width,height]))\n",
    "ax_b.append(fig.add_axes([0.75,y_position,width,height]))\n",
    "ax_b.append(fig.add_axes([0.795,y_position,width,height]))\n",
    "ax_b.append(fig.add_axes([0.851,y_position,width,height]))\n",
    "\n",
    "\n",
    "best_angles = []\n",
    "best_costs = []\n",
    "best_histogram = []\n",
    "    \n",
    "for i in range(n_iterations):\n",
    "        \n",
    "    costs       = [KL(batch[p]) for p in range(particles)]\n",
    "    costs       = np.array(costs).reshape((particles,))\n",
    "    \n",
    "    b_angles, b_cost, _, _  = optimizer.run(costs)\n",
    "    b_hist = circuit(b_angles)\n",
    "    \n",
    "    best_angles.append(b_angles)\n",
    "    best_costs.append(b_cost)\n",
    "    best_histogram.append(circuit(b_angles))   \n",
    "\n",
    "    batch = optimizer.x\n",
    "        \n",
    "    f.write('%s \\t' % i)\n",
    "    f.write('%s \\t' % b_cost)\n",
    "    for item in b_angles:\n",
    "        f.write(\"%s \\t\" % item)\n",
    "        f.write('\\n')\n",
    "    \n",
    "    plots(ax1,ax2,ax_b,ax_c,fig,best_costs,bas,hist_sample,b_hist,n,m)\n",
    "    \n",
    "    if i == 1:\n",
    "        time.sleep(10) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercises:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Modify the code to build a circuit that outputs the distribution that gives '0000' and '1111' a probability of 0.5, and any other state a probability of 0. What are the angles of this circuit?\n",
    "\n",
    "\n",
    "2. Modify the code to train a Bars and Stripes distribution with a 'line' and a 'star' topology. Which one did better among the 'line', 'star', and 'all' topology? Why do you think this happened?\n",
    "\n",
    "\n",
    "3. Modify the code to create a 'cycle' topology, where the conections are between qubits (0,1), (1,2), (2,3), and (3,0). How did this one perform on the Bars and Stripes distribution?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Solutions:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Set `hist_sample = [0.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.5]` and rerun all the code."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Set `n_top = 0` for the 'line' topology and `n_top = 1` for the star topology, and rerun all the code."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. Add this code in the topology definitions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "''' cycle topology'''\n",
    "for i in range(n_qubits-1):\n",
    "    connections = [i,i+1]\n",
    "    edges_line.append(connections)\n",
    "\n",
    "edges_line.append([0,n_qubits-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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