{
 "cells": [
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "# Expressive power of the variational linear quantum photonic circuit (dependent of photon number)",
   "id": "f1aad8daa8cdd20"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "The goal here will be to fit a degree 3 Fourier series g(x) with a Variational Quantum Circuit (VQC) using MerLin for the optimization. Since this is a fitting task, we won't use a validation or test dataset because our goal is to overfit on the data to assess the expressivity of a VQC.\n",
    "\n",
    "As we will see, the expressivity of a VQC depends on the number of photons sent through the quantum circuit.\n",
    "\n",
    "This notebook presents an experiment from the paper: [Fock State-enhanced Expressivity of Quantum Machine Learning Models](https://arxiv.org/abs/2107.05224)."
   ],
   "id": "d6f644861594f155"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## 0. Imports and prep",
   "id": "ecce09be4899e558"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-05T18:44:24.686087Z",
     "start_time": "2025-06-05T18:44:24.658255Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Import required libraries\n",
    "import matplotlib.pyplot as plt\n",
    "import perceval as pcvl\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import numpy as np\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from tqdm import tqdm\n",
    "from math import comb\n",
    "\n",
    "# Import from our custom packages\n",
    "from merlin import QuantumLayer, OutputMappingStrategy"
   ],
   "id": "acbe33cef30ce280",
   "outputs": [],
   "execution_count": 81
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## 1. Define input domain, x, and target function g(x)",
   "id": "f6f7fa599036c74f"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-05T18:44:27.221484Z",
     "start_time": "2025-06-05T18:44:27.199901Z"
    }
   },
   "cell_type": "code",
   "source": [
    "x = np.arange(-3*np.pi, 3*np.pi+0.01, 0.05)\n",
    "\n",
    "# Fourier coefficents\n",
    "c_0 = 0.2\n",
    "c_1 = 0.69 + 0.52j\n",
    "c_2 = 0.81 + 0.41j\n",
    "c_3 = 0.68 + 0.82j\n",
    "# We want real valued g(x) so we have c_{-n} = conjugate( c_{n})\n",
    "coefs = np.array([np.conj(c_3), np.conj(c_2), np.conj(c_1), c_0, c_1, c_2, c_3])\n",
    "n = np.arange(-3, 4, 1)\n",
    "\n",
    "# Compute the Fourier series sum\n",
    "g = np.zeros_like(x, dtype=complex)\n",
    "for k, c in zip(n, coefs):\n",
    "    g += c * np.exp(1j * k * x)\n",
    "\n",
    "# g should be real valued\n",
    "assert np.allclose(g, g.real), \"g != g.real\"\n",
    "g = g.real"
   ],
   "id": "f7025aef462f8b54",
   "outputs": [],
   "execution_count": 82
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": " Let's visualize g(x)",
   "id": "3a7326c63d78217e"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-05T18:44:29.663810Z",
     "start_time": "2025-06-05T18:44:29.496633Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Plot using matplotlib\n",
    "plt.figure(figsize=(8, 4))\n",
    "plt.scatter(x, g, label='g(x)', s=30)\n",
    "plt.title('Visualization of g(x) from Fourier Series')\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('g(x)')\n",
    "plt.grid(True)\n",
    "plt.legend()\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "plt.clf()"
   ],
   "id": "3f27964b6712204d",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 83
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## 2. Model definitions",
   "id": "9fb78e24366119bb"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "First off, we define the photonic circuit using Perceval.",
   "id": "2d141b4d4717e128"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-05T18:44:35.305229Z",
     "start_time": "2025-06-05T18:44:35.198257Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def create_vqc_general(m, input_size):\n",
    "    \"\"\"Create variational quantum classifier with specified number of modes using general meshes\"\"\"\n",
    "\n",
    "    wl = pcvl.GenericInterferometer(m,\n",
    "                                    lambda i: pcvl.BS() // pcvl.PS(pcvl.P(f\"theta_li{i}_ps\")) // \\\n",
    "                                              pcvl.BS() // pcvl.PS(pcvl.P(f\"theta_lo{i}_ps\")),\n",
    "                                    shape=pcvl.InterferometerShape.RECTANGLE)\n",
    "\n",
    "    c_var = pcvl.Circuit(m)\n",
    "    for i in range(input_size):\n",
    "        px = pcvl.P(f\"px{i + 1}\")\n",
    "        c_var.add(i + (m - input_size) // 2, pcvl.PS(px))\n",
    "\n",
    "    wr = pcvl.GenericInterferometer(m,\n",
    "                                    lambda i: pcvl.BS() // pcvl.PS(pcvl.P(f\"theta_ri{i}_ps\")) // \\\n",
    "                                              pcvl.BS() // pcvl.PS(pcvl.P(f\"theta_ro{i}_ps\")),\n",
    "                                    shape=pcvl.InterferometerShape.RECTANGLE)\n",
    "\n",
    "    c = pcvl.Circuit(m)\n",
    "    c.add(0, wl, merge=True)\n",
    "    c.add(0, c_var, merge=True)\n",
    "    c.add(0, wr, merge=True)\n",
    "\n",
    "    return c\n",
    "\n",
    "def count_parameters(model):\n",
    "    \"\"\"Count trainable parameters in a PyTorch model\"\"\"\n",
    "    return sum(p.numel() for p in model.parameters() if p.requires_grad)\n",
    "\n",
    "example_circuit = create_vqc_general(3, 1)\n",
    "pcvl.pdisplay(example_circuit)"
   ],
   "id": "f65611c13781e51c",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<drawsvg.drawing.Drawing at 0x257e5593110>"
      ],
      "image/svg+xml": "<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"\n     width=\"2445.0\" height=\"218.75\" viewBox=\"-28.0 0 1956.0 175.0\">\n<defs>\n</defs>\n<path d=\"M10,25.0 L25,25.0\" stroke-width=\"3\" stroke=\"darkred\" stroke-linejoin=\"miter\" fill=\"none\" />\n<path d=\"M10,75.0 L25,75.0\" stroke-width=\"3\" stroke=\"darkred\" stroke-linejoin=\"miter\" fill=\"none\" />\n<path d=\"M10,125.0 L25,125.0\" stroke-width=\"3\" stroke=\"darkred\" stroke-linejoin=\"miter\" fill=\"none\" />\n<path d=\"M25,25 L53,25 L72,44\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n<path d=\"M78,44 L97,25 L125,25\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n<path d=\"M25,75 L53,75 L72,56\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n<path d=\"M78,56 L97,75 L125,75\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n<path d=\"M50,43 L100,43 L100,57 L50,57 Z\" stroke=\"black\" fill=\"black\" stroke-linejoin=\"miter\" />\n<text x=\"75\" y=\"85\" font-size=\"7\" text-anchor=\"middle\"></text>\n<text x=\"75\" y=\"26\" font-size=\"7\" text-anchor=\"middle\"></text>\n<path d=\"M50,43 L100,43 L100,47 L50,47 Z\" stroke=\"black\" fill=\"lightgray\" stroke-linejoin=\"miter\" />\n<path d=\"M93,50 L103,50 L103,60 L93,60 Z\" stroke=\"black\" fill=\"thistle\" stroke-linejoin=\"miter\" />\n<text x=\"98\" y=\"57\" font-size=\"6\" text-anchor=\"middle\">Rx</text>\n<path d=\"M125,25 L175,25\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n<path d=\"M130,40 L139,40 L153,10 L144,10 L130,40 L139,40 Z\" stroke=\"black\" fill=\"gray\" stroke-linejoin=\"miter\" />\n<text x=\"147\" y=\"38\" font-size=\"7\" text-anchor=\"start\">Φ=theta_li0_ps</text>\n<path d=\"M125,75.0 L175,75.0\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n<path d=\"M175,25 L203,25 L222,44\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n<path d=\"M228,44 L247,25 L275,25\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n<path d=\"M175,75 L203,75 L222,56\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n<path d=\"M228,56 L247,75 L275,75\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n<path d=\"M200,43 L250,43 L250,57 L200,57 Z\" stroke=\"black\" fill=\"black\" stroke-linejoin=\"miter\" />\n<text x=\"225\" y=\"85\" font-size=\"7\" text-anchor=\"middle\"></text>\n<text x=\"225\" y=\"26\" font-size=\"7\" text-anchor=\"middle\"></text>\n<path d=\"M200,43 L250,43 L250,47 L200,47 Z\" stroke=\"black\" fill=\"lightgray\" stroke-linejoin=\"miter\" />\n<path d=\"M243,50 L253,50 L253,60 L243,60 Z\" stroke=\"black\" fill=\"thistle\" stroke-linejoin=\"miter\" />\n<text x=\"248\" y=\"57\" font-size=\"6\" text-anchor=\"middle\">Rx</text>\n<path d=\"M275,25 L325,25\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n<path d=\"M280,40 L289,40 L303,10 L294,10 L280,40 L289,40 Z\" stroke=\"black\" fill=\"gray\" stroke-linejoin=\"miter\" />\n<text x=\"297\" y=\"38\" font-size=\"7\" text-anchor=\"start\">Φ=theta_lo0_ps</text>\n<path d=\"M275,75.0 L325,75.0\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n<path d=\"M25,125.0 L325,125.0\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n<path d=\"M325,75 L353,75 L372,94\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n<path d=\"M378,94 L397,75 L425,75\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n<path d=\"M325,125 L353,125 L372,106\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n<path d=\"M378,106 L397,125 L425,125\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n<path d=\"M350,93 L400,93 L400,107 L350,107 Z\" stroke=\"black\" fill=\"black\" stroke-linejoin=\"miter\" />\n<text x=\"375\" y=\"135\" font-size=\"7\" text-anchor=\"middle\"></text>\n<text x=\"375\" y=\"76\" font-size=\"7\" text-anchor=\"middle\"></text>\n<path d=\"M350,93 L400,93 L400,97 L350,97 Z\" stroke=\"black\" fill=\"lightgray\" stroke-linejoin=\"miter\" />\n<path d=\"M393,100 L403,100 L403,110 L393,110 Z\" stroke=\"black\" fill=\"thistle\" stroke-linejoin=\"miter\" />\n<text x=\"398\" y=\"107\" font-size=\"6\" text-anchor=\"middle\">Rx</text>\n<path d=\"M425,75 L475,75\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n<path d=\"M430,90 L439,90 L453,60 L444,60 L430,90 L439,90 Z\" stroke=\"black\" fill=\"gray\" stroke-linejoin=\"miter\" />\n<text x=\"447\" y=\"88\" font-size=\"7\" text-anchor=\"start\">Φ=theta_li1_ps</text>\n<path d=\"M425,125.0 L475,125.0\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n<path d=\"M475,75 L503,75 L522,94\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n<path d=\"M528,94 L547,75 L575,75\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n<path d=\"M475,125 L503,125 L522,106\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n<path d=\"M528,106 L547,125 L575,125\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n<path d=\"M500,93 L550,93 L550,107 L500,107 Z\" stroke=\"black\" fill=\"black\" stroke-linejoin=\"miter\" />\n<text x=\"525\" y=\"135\" font-size=\"7\" text-anchor=\"middle\"></text>\n<text x=\"525\" y=\"76\" font-size=\"7\" text-anchor=\"middle\"></text>\n<path d=\"M500,93 L550,93 L550,97 L500,97 Z\" stroke=\"black\" fill=\"lightgray\" stroke-linejoin=\"miter\" />\n<path d=\"M543,100 L553,100 L553,110 L543,110 Z\" stroke=\"black\" fill=\"thistle\" stroke-linejoin=\"miter\" />\n<text x=\"548\" y=\"107\" font-size=\"6\" text-anchor=\"middle\">Rx</text>\n<path d=\"M575,75 L625,75\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n<path d=\"M580,90 L589,90 L603,60 L594,60 L580,90 L589,90 Z\" stroke=\"black\" fill=\"gray\" stroke-linejoin=\"miter\" />\n<text x=\"597\" y=\"88\" font-size=\"7\" text-anchor=\"start\">Φ=theta_lo1_ps</text>\n<path d=\"M325,25.0 L625,25.0\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n<path d=\"M575,125.0 L625,125.0\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n<path d=\"M625,25 L653,25 L672,44\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n<path d=\"M678,44 L697,25 L725,25\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n<path d=\"M625,75 L653,75 L672,56\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n<path d=\"M678,56 L697,75 L725,75\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n<path d=\"M650,43 L700,43 L700,57 L650,57 Z\" stroke=\"black\" fill=\"black\" stroke-linejoin=\"miter\" />\n<text x=\"675\" y=\"85\" font-size=\"7\" text-anchor=\"middle\"></text>\n<text x=\"675\" y=\"26\" font-size=\"7\" text-anchor=\"middle\"></text>\n<path d=\"M650,43 L700,43 L700,47 L650,47 Z\" stroke=\"black\" fill=\"lightgray\" stroke-linejoin=\"miter\" />\n<path d=\"M693,50 L703,50 L703,60 L693,60 Z\" stroke=\"black\" fill=\"thistle\" stroke-linejoin=\"miter\" />\n<text x=\"698\" y=\"57\" font-size=\"6\" text-anchor=\"middle\">Rx</text>\n<path d=\"M725,25 L775,25\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n<path d=\"M730,40 L739,40 L753,10 L744,10 L730,40 L739,40 Z\" stroke=\"black\" fill=\"gray\" stroke-linejoin=\"miter\" />\n<text x=\"747\" y=\"38\" font-size=\"7\" text-anchor=\"start\">Φ=theta_li2_ps</text>\n<path d=\"M725,75.0 L775,75.0\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n<path d=\"M775,25 L803,25 L822,44\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n<path d=\"M828,44 L847,25 L875,25\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n<path d=\"M775,75 L803,75 L822,56\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n<path d=\"M828,56 L847,75 L875,75\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n<path d=\"M800,43 L850,43 L850,57 L800,57 Z\" stroke=\"black\" fill=\"black\" stroke-linejoin=\"miter\" />\n<text x=\"825\" y=\"85\" font-size=\"7\" text-anchor=\"middle\"></text>\n<text x=\"825\" y=\"26\" font-size=\"7\" text-anchor=\"middle\"></text>\n<path d=\"M800,43 L850,43 L850,47 L800,47 Z\" stroke=\"black\" fill=\"lightgray\" stroke-linejoin=\"miter\" />\n<path d=\"M843,50 L853,50 L853,60 L843,60 Z\" stroke=\"black\" fill=\"thistle\" stroke-linejoin=\"miter\" />\n<text x=\"848\" y=\"57\" font-size=\"6\" text-anchor=\"middle\">Rx</text>\n<path d=\"M875,25 L925,25\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n<path d=\"M880,40 L889,40 L903,10 L894,10 L880,40 L889,40 Z\" stroke=\"black\" fill=\"gray\" stroke-linejoin=\"miter\" />\n<text x=\"897\" y=\"38\" font-size=\"7\" text-anchor=\"start\">Φ=theta_lo2_ps</text>\n<path d=\"M875,75.0 L925,75.0\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n<path d=\"M625,125.0 L925,125.0\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n<path d=\"M925,75 L975,75\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n<path d=\"M930,90 L939,90 L953,60 L944,60 L930,90 L939,90 Z\" stroke=\"black\" fill=\"gray\" stroke-linejoin=\"miter\" />\n<text x=\"947\" y=\"88\" font-size=\"7\" text-anchor=\"start\">Φ=px1</text>\n<path d=\"M925,25.0 L975,25.0\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n<path d=\"M975,25 L1003,25 L1022,44\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n<path d=\"M1028,44 L1047,25 L1075,25\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n<path d=\"M975,75 L1003,75 L1022,56\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n<path d=\"M1028,56 L1047,75 L1075,75\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n<path d=\"M1000,43 L1050,43 L1050,57 L1000,57 Z\" stroke=\"black\" fill=\"black\" stroke-linejoin=\"miter\" />\n<text x=\"1025\" y=\"85\" font-size=\"7\" text-anchor=\"middle\"></text>\n<text x=\"1025\" y=\"26\" font-size=\"7\" text-anchor=\"middle\"></text>\n<path d=\"M1000,43 L1050,43 L1050,47 L1000,47 Z\" stroke=\"black\" fill=\"lightgray\" stroke-linejoin=\"miter\" />\n<path d=\"M1043,50 L1053,50 L1053,60 L1043,60 Z\" stroke=\"black\" fill=\"thistle\" stroke-linejoin=\"miter\" />\n<text x=\"1048\" y=\"57\" font-size=\"6\" text-anchor=\"middle\">Rx</text>\n<path d=\"M1075,25 L1125,25\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n<path d=\"M1080,40 L1089,40 L1103,10 L1094,10 L1080,40 L1089,40 Z\" stroke=\"black\" fill=\"gray\" stroke-linejoin=\"miter\" />\n<text x=\"1097\" y=\"38\" font-size=\"7\" text-anchor=\"start\">Φ=theta_ri0_ps</text>\n<path d=\"M1075,75.0 L1125,75.0\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n<path d=\"M1125,25 L1153,25 L1172,44\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n<path d=\"M1178,44 L1197,25 L1225,25\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n<path d=\"M1125,75 L1153,75 L1172,56\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n<path d=\"M1178,56 L1197,75 L1225,75\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n<path d=\"M1150,43 L1200,43 L1200,57 L1150,57 Z\" stroke=\"black\" fill=\"black\" stroke-linejoin=\"miter\" />\n<text x=\"1175\" y=\"85\" font-size=\"7\" text-anchor=\"middle\"></text>\n<text x=\"1175\" y=\"26\" font-size=\"7\" text-anchor=\"middle\"></text>\n<path d=\"M1150,43 L1200,43 L1200,47 L1150,47 Z\" stroke=\"black\" fill=\"lightgray\" stroke-linejoin=\"miter\" />\n<path d=\"M1193,50 L1203,50 L1203,60 L1193,60 Z\" stroke=\"black\" fill=\"thistle\" stroke-linejoin=\"miter\" />\n<text x=\"1198\" y=\"57\" font-size=\"6\" text-anchor=\"middle\">Rx</text>\n<path d=\"M1225,25 L1275,25\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n<path d=\"M1230,40 L1239,40 L1253,10 L1244,10 L1230,40 L1239,40 Z\" stroke=\"black\" fill=\"gray\" stroke-linejoin=\"miter\" />\n<text x=\"1247\" y=\"38\" font-size=\"7\" text-anchor=\"start\">Φ=theta_ro0_ps</text>\n<path d=\"M1225,75.0 L1275,75.0\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n<path d=\"M925,125.0 L1275,125.0\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n<path d=\"M1275,75 L1303,75 L1322,94\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n<path d=\"M1328,94 L1347,75 L1375,75\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n<path d=\"M1275,125 L1303,125 L1322,106\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n<path d=\"M1328,106 L1347,125 L1375,125\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n<path d=\"M1300,93 L1350,93 L1350,107 L1300,107 Z\" stroke=\"black\" fill=\"black\" stroke-linejoin=\"miter\" />\n<text x=\"1325\" y=\"135\" font-size=\"7\" text-anchor=\"middle\"></text>\n<text x=\"1325\" y=\"76\" font-size=\"7\" text-anchor=\"middle\"></text>\n<path d=\"M1300,93 L1350,93 L1350,97 L1300,97 Z\" stroke=\"black\" fill=\"lightgray\" stroke-linejoin=\"miter\" />\n<path d=\"M1343,100 L1353,100 L1353,110 L1343,110 Z\" stroke=\"black\" fill=\"thistle\" stroke-linejoin=\"miter\" />\n<text x=\"1348\" y=\"107\" font-size=\"6\" text-anchor=\"middle\">Rx</text>\n<path d=\"M1375,75 L1425,75\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n<path d=\"M1380,90 L1389,90 L1403,60 L1394,60 L1380,90 L1389,90 Z\" stroke=\"black\" fill=\"gray\" stroke-linejoin=\"miter\" />\n<text x=\"1397\" y=\"88\" font-size=\"7\" text-anchor=\"start\">Φ=theta_ri1_ps</text>\n<path d=\"M1375,125.0 L1425,125.0\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n<path d=\"M1425,75 L1453,75 L1472,94\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n<path d=\"M1478,94 L1497,75 L1525,75\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n<path d=\"M1425,125 L1453,125 L1472,106\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n<path d=\"M1478,106 L1497,125 L1525,125\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n<path d=\"M1450,93 L1500,93 L1500,107 L1450,107 Z\" stroke=\"black\" fill=\"black\" stroke-linejoin=\"miter\" />\n<text x=\"1475\" y=\"135\" font-size=\"7\" text-anchor=\"middle\"></text>\n<text x=\"1475\" y=\"76\" font-size=\"7\" text-anchor=\"middle\"></text>\n<path d=\"M1450,93 L1500,93 L1500,97 L1450,97 Z\" stroke=\"black\" fill=\"lightgray\" stroke-linejoin=\"miter\" />\n<path d=\"M1493,100 L1503,100 L1503,110 L1493,110 Z\" stroke=\"black\" fill=\"thistle\" stroke-linejoin=\"miter\" />\n<text x=\"1498\" y=\"107\" font-size=\"6\" text-anchor=\"middle\">Rx</text>\n<path d=\"M1525,75 L1575,75\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n<path d=\"M1530,90 L1539,90 L1553,60 L1544,60 L1530,90 L1539,90 Z\" stroke=\"black\" fill=\"gray\" stroke-linejoin=\"miter\" />\n<text x=\"1547\" y=\"88\" font-size=\"7\" text-anchor=\"start\">Φ=theta_ro1_ps</text>\n<path d=\"M1275,25.0 L1575,25.0\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n<path d=\"M1525,125.0 L1575,125.0\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n<path d=\"M1575,25 L1603,25 L1622,44\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n<path d=\"M1628,44 L1647,25 L1675,25\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n<path d=\"M1575,75 L1603,75 L1622,56\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n<path d=\"M1628,56 L1647,75 L1675,75\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n<path d=\"M1600,43 L1650,43 L1650,57 L1600,57 Z\" stroke=\"black\" fill=\"black\" stroke-linejoin=\"miter\" />\n<text x=\"1625\" y=\"85\" font-size=\"7\" text-anchor=\"middle\"></text>\n<text x=\"1625\" y=\"26\" font-size=\"7\" text-anchor=\"middle\"></text>\n<path d=\"M1600,43 L1650,43 L1650,47 L1600,47 Z\" stroke=\"black\" fill=\"lightgray\" stroke-linejoin=\"miter\" />\n<path d=\"M1643,50 L1653,50 L1653,60 L1643,60 Z\" stroke=\"black\" fill=\"thistle\" stroke-linejoin=\"miter\" />\n<text x=\"1648\" y=\"57\" font-size=\"6\" text-anchor=\"middle\">Rx</text>\n<path d=\"M1675,25 L1725,25\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n<path d=\"M1680,40 L1689,40 L1703,10 L1694,10 L1680,40 L1689,40 Z\" stroke=\"black\" fill=\"gray\" stroke-linejoin=\"miter\" />\n<text x=\"1697\" y=\"38\" font-size=\"7\" text-anchor=\"start\">Φ=theta_ri2_ps</text>\n<path d=\"M1675,75.0 L1725,75.0\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n<path d=\"M1725,25 L1753,25 L1772,44\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n<path d=\"M1778,44 L1797,25 L1825,25\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n<path d=\"M1725,75 L1753,75 L1772,56\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n<path d=\"M1778,56 L1797,75 L1825,75\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n<path d=\"M1750,43 L1800,43 L1800,57 L1750,57 Z\" stroke=\"black\" fill=\"black\" stroke-linejoin=\"miter\" />\n<text x=\"1775\" y=\"85\" font-size=\"7\" text-anchor=\"middle\"></text>\n<text x=\"1775\" y=\"26\" font-size=\"7\" text-anchor=\"middle\"></text>\n<path d=\"M1750,43 L1800,43 L1800,47 L1750,47 Z\" stroke=\"black\" fill=\"lightgray\" stroke-linejoin=\"miter\" />\n<path d=\"M1793,50 L1803,50 L1803,60 L1793,60 Z\" stroke=\"black\" fill=\"thistle\" stroke-linejoin=\"miter\" />\n<text x=\"1798\" y=\"57\" font-size=\"6\" text-anchor=\"middle\">Rx</text>\n<path d=\"M1825,25 L1875,25\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n<path d=\"M1830,40 L1839,40 L1853,10 L1844,10 L1830,40 L1839,40 Z\" stroke=\"black\" fill=\"gray\" stroke-linejoin=\"miter\" />\n<text x=\"1847\" y=\"38\" font-size=\"7\" text-anchor=\"start\">Φ=theta_ro2_ps</text>\n<path d=\"M1825,75.0 L1875,75.0\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n<path d=\"M1575,125.0 L1875,125.0\" stroke=\"darkred\" stroke-width=\"3\" fill=\"none\" />\n<path d=\"M1875,25.0 L1890,25.0\" stroke-width=\"3\" stroke=\"darkred\" stroke-linejoin=\"miter\" fill=\"none\" />\n<path d=\"M1875,75.0 L1890,75.0\" stroke-width=\"3\" stroke=\"darkred\" stroke-linejoin=\"miter\" fill=\"none\" />\n<path d=\"M1875,125.0 L1890,125.0\" stroke-width=\"3\" stroke=\"darkred\" stroke-linejoin=\"miter\" fill=\"none\" />\n<text x=\"1900\" y=\"28.0\" font-size=\"6\" text-anchor=\"end\">0</text>\n<text x=\"1900\" y=\"78.0\" font-size=\"6\" text-anchor=\"end\">1</text>\n<text x=\"1900\" y=\"128.0\" font-size=\"6\" text-anchor=\"end\">2</text>\n<text x=\"0\" y=\"28.0\" font-size=\"6\" text-anchor=\"start\">0</text>\n<text x=\"0\" y=\"78.0\" font-size=\"6\" text-anchor=\"start\">1</text>\n<text x=\"0\" y=\"128.0\" font-size=\"6\" text-anchor=\"start\">2</text>\n</svg>"
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 84
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "We will define a scaling layer for the first layer of our model. It simply multiplies the input by a constant.",
   "id": "a2b038907651a441"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-05T18:28:07.238702Z",
     "start_time": "2025-06-05T18:28:07.175859Z"
    }
   },
   "cell_type": "code",
   "source": [
    "class ScaleLayer(nn.Module):\n",
    "    \"\"\"\n",
    "        Multiply the input tensor by a learned or fixed factor.\n",
    "\n",
    "        Args:\n",
    "            dim (int): Dimension of the input data to be encoded.\n",
    "            scale_type (str): Type of scaling method.\n",
    "\n",
    "        Returns: nn.Module that multiplies the input tensor by a learned or fixed factor.\n",
    "    \"\"\"\n",
    "    def __init__(self, dim, scale_type = \"learned\"):\n",
    "        super(ScaleLayer, self).__init__()\n",
    "        # Create a single learnable parameter (initialized to 1.0 by default)\n",
    "        if scale_type == \"learned\":\n",
    "            self.scale = nn.Parameter(torch.rand(dim))\n",
    "        elif scale_type == \"2pi\":\n",
    "            self.scale = torch.full((dim,), 2 * torch.pi)\n",
    "        elif scale_type == \"/2pi\":\n",
    "            self.scale = torch.full((dim,), 1/ (2 * torch.pi))\n",
    "        elif scale_type == \"/2\":\n",
    "            self.scale = torch.full((dim,), 1/ 2)\n",
    "        elif scale_type == \"pi\":\n",
    "            self.scale = torch.full((dim,), torch.pi)\n",
    "        elif scale_type == \"/pi\":\n",
    "            self.scale = torch.full((dim,), 1 / torch.pi)\n",
    "        elif scale_type == \"1\":\n",
    "            self.scale = torch.full((dim,), 1)\n",
    "\n",
    "    def forward(self, x):\n",
    "        # Element-wise multiplication of each input element by the learned scale\n",
    "        return x * self.scale"
   ],
   "id": "c50bc1ebbcf5315d",
   "outputs": [],
   "execution_count": 69
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "Then, we use MerLin's QuantumLayer which allows backpropagation for optimization with gradient descent. It was also designed to be used with PyTorch so this facilitates its usage immensely.\n",
    "\n",
    "Let's create three quantum layers that each have a different input state to their circuit. Then, let's see how this affects their number of parameters."
   ],
   "id": "c877235bfbdbd7e3"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-05T18:48:01.894060Z",
     "start_time": "2025-06-05T18:48:01.856727Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def create_quantum_layer(initial_state):\n",
    "    \"\"\" Create a quantum layer consisting of a VQC for a specific initial state \"\"\"\n",
    "\n",
    "    vqc = QuantumLayer(\n",
    "                input_size=1,\n",
    "                output_size=1,\n",
    "                circuit=create_vqc_general(3, 1),\n",
    "                trainable_parameters=[\"theta\"],\n",
    "                input_parameters=[\"px\"],\n",
    "                input_state= initial_state,\n",
    "                no_bunching=False,\n",
    "                output_mapping_strategy=OutputMappingStrategy.LINEAR,\n",
    "            )\n",
    "\n",
    "    return vqc\n",
    "\n",
    "\n",
    "def create_model(initial_state):\n",
    "    scale_layer = ScaleLayer(1, scale_type = \"/pi\")\n",
    "    vqc = create_quantum_layer(initial_state)\n",
    "\n",
    "    return nn.Sequential(scale_layer, vqc)\n",
    "\n",
    "vqc_100 = create_model([1, 0, 0])\n",
    "vqc_110 = create_model([1, 1, 0])\n",
    "vqc_111 = create_model([1, 1, 1])\n",
    "\n",
    "models = {\"VQC_[1, 0, 0]\" : vqc_100, \"VQC_[1, 1, 0]\" : vqc_110, \"VQC_[1, 1, 1]\" : vqc_111}\n",
    "\n",
    "for name, model in models.items():\n",
    "    print(f\"{name}: {count_parameters(model)} parameters\")"
   ],
   "id": "e26489afd2c62593",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "VQC_[1, 0, 0]: 16 parameters\n",
      "VQC_[1, 1, 0]: 19 parameters\n",
      "VQC_[1, 1, 1]: 23 parameters\n"
     ]
    }
   ],
   "execution_count": 91
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## 3. Training function",
   "id": "9382c7aeed4bd962"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "The optimization for the quantum model is as easy as for a classical PyTorch model thanks to MerLin. The structure of the training loop remains the same !\n",
    "\n",
    "Note that the loss function used for training is the Mean Squared Error (MSE) loss which is useful for regression tasks."
   ],
   "id": "415923bf7518fdfd"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-05T18:48:04.429475Z",
     "start_time": "2025-06-05T18:48:04.421405Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def train_model(model, X_train, y_train, model_name, n_epochs=60, batch_size=32, lr=0.02, betas=[0.9, 0.999]):\n",
    "    \"\"\"Train a model and return training metrics\"\"\"\n",
    "    optimizer = torch.optim.Adam(model.parameters(), lr=lr, betas=betas)\n",
    "    criterion = nn.MSELoss()\n",
    "\n",
    "    losses = []\n",
    "    train_mses = []\n",
    "\n",
    "    model.train()\n",
    "\n",
    "    pbar = tqdm(range(n_epochs), desc=f\"Training {model_name}\")\n",
    "    for epoch in pbar:\n",
    "        permutation = torch.randperm(X_train.size()[0])\n",
    "        total_loss = 0\n",
    "\n",
    "        for i in range(0, X_train.size()[0], batch_size):\n",
    "            indices = permutation[i:i + batch_size]\n",
    "            batch_x, batch_y = X_train[indices], y_train[indices]\n",
    "\n",
    "            outputs = model(batch_x)\n",
    "            loss = criterion(outputs.squeeze(), batch_y.squeeze())\n",
    "\n",
    "            optimizer.zero_grad()\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "\n",
    "            total_loss += loss.item()\n",
    "\n",
    "        avg_loss = total_loss / (X_train.size()[0] // batch_size)\n",
    "        losses.append(avg_loss)\n",
    "\n",
    "        # Evaluation\n",
    "        model.eval()\n",
    "        with torch.no_grad():\n",
    "            train_outputs = model(X_train)\n",
    "            train_mse = mean_squared_error(y_train.numpy(), train_outputs)\n",
    "            train_mses.append(train_mse)\n",
    "\n",
    "            pbar.set_description(f\"Training {model_name} - Loss: {avg_loss:.4f}, Train MSE: {train_mse:.4f}\")\n",
    "\n",
    "        model.train()\n",
    "\n",
    "    return {\n",
    "        'losses': losses,\n",
    "        'train_mses': train_mses,\n",
    "    }"
   ],
   "id": "d2a16c0f40e63fcb",
   "outputs": [],
   "execution_count": 92
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-05T18:48:49.930742Z",
     "start_time": "2025-06-05T18:48:06.846729Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def train_models_multiple_runs(initial_states, colors, X_train, y_train, num_runs=5):\n",
    "    \"\"\"Train all models multiple times and return results\"\"\"\n",
    "    results = {}\n",
    "    models = []\n",
    "\n",
    "    for initial_state, color in zip(initial_states, colors):\n",
    "        print(f\"\\nTraining VQC with initial state: {initial_state} ({num_runs} runs):\")\n",
    "        pending_models = []\n",
    "        model_runs = []\n",
    "\n",
    "        for run in range(num_runs):\n",
    "            # Create a fresh instance of the model for each run\n",
    "            vqc = create_model(initial_state)\n",
    "\n",
    "            print(f\"  Run {run+1}/{num_runs}...\")\n",
    "            run_results = train_model(vqc, torch.tensor(X_train, dtype=torch.float).unsqueeze(-1), torch.tensor(y_train, dtype=torch.float), f\"VQC_{initial_state}-run{run+1}\")\n",
    "            pending_models.append(vqc)\n",
    "            model_runs.append(run_results)\n",
    "\n",
    "        # Find and keep the best model for each initial state\n",
    "        index = torch.argmin(torch.tensor([model_run[\"train_mses\"][-1] for model_run in model_runs]))\n",
    "\n",
    "        models.append(pending_models[index])\n",
    "        # Store all runs for this model\n",
    "        results[f\"VQC_{initial_state}\"] = {\n",
    "            \"runs\": model_runs,\n",
    "            \"color\": color,\n",
    "        }\n",
    "\n",
    "    return results, models\n",
    "\n",
    "# Define number of runs per model\n",
    "num_runs = 3\n",
    "\n",
    "initial_states = [[1, 0, 0], [1, 1, 0], [1, 1, 1]]\n",
    "\n",
    "colors = [\"blue\", \"orange\", \"red\"]\n",
    "\n",
    "# Train all models\n",
    "all_results, models = train_models_multiple_runs(initial_states, colors, x, g, num_runs=num_runs)"
   ],
   "id": "4b56f0bd66e6daad",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Training VQC with initial state: [1, 0, 0] (3 runs):\n",
      "  Run 1/3...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training VQC_[1, 0, 0]-run1 - Loss: 4.2597, Train MSE: 3.9085: 100%|██████████| 60/60 [00:04<00:00, 13.75it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Run 2/3...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training VQC_[1, 0, 0]-run2 - Loss: 4.2933, Train MSE: 3.9207: 100%|██████████| 60/60 [00:03<00:00, 15.06it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Run 3/3...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training VQC_[1, 0, 0]-run3 - Loss: 4.2789, Train MSE: 3.9123: 100%|██████████| 60/60 [00:04<00:00, 13.87it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Training VQC with initial state: [1, 1, 0] (3 runs):\n",
      "  Run 1/3...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training VQC_[1, 1, 0]-run1 - Loss: 2.4816, Train MSE: 2.2958: 100%|██████████| 60/60 [00:04<00:00, 12.59it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Run 2/3...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training VQC_[1, 1, 0]-run2 - Loss: 2.5101, Train MSE: 2.2692: 100%|██████████| 60/60 [00:04<00:00, 12.16it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Run 3/3...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training VQC_[1, 1, 0]-run3 - Loss: 2.4925, Train MSE: 2.2723: 100%|██████████| 60/60 [00:04<00:00, 12.22it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Training VQC with initial state: [1, 1, 1] (3 runs):\n",
      "  Run 1/3...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training VQC_[1, 1, 1]-run1 - Loss: 0.0100, Train MSE: 0.0089: 100%|██████████| 60/60 [00:05<00:00, 11.62it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Run 2/3...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training VQC_[1, 1, 1]-run2 - Loss: 0.0000, Train MSE: 0.0000: 100%|██████████| 60/60 [00:05<00:00, 10.95it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Run 3/3...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training VQC_[1, 1, 1]-run3 - Loss: 0.0053, Train MSE: 0.0044: 100%|██████████| 60/60 [00:05<00:00, 11.97it/s]\n"
     ]
    }
   ],
   "execution_count": 93
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## 4. Plot training loss",
   "id": "e685a334447f4a1e"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-05T18:48:54.046465Z",
     "start_time": "2025-06-05T18:48:53.827354Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def plot_training_curves(all_results):\n",
    "    \"\"\"Plot training curves for all model variants with average and envelope (only loss shown)\"\"\"\n",
    "    fig, ax = plt.subplots(1, 1, figsize=(10, 6))\n",
    "\n",
    "    # Plot each metric\n",
    "    for model_name, model_data in all_results.items():\n",
    "        color = model_data['color']\n",
    "        linestyle = '-'\n",
    "\n",
    "        # Get data from all runs\n",
    "        losses_runs = [run['losses'] for run in model_data['runs']]\n",
    "\n",
    "        # Calculate mean values across all runs\n",
    "        epochs = len(losses_runs[0])\n",
    "        mean_losses = [sum(run[i] for run in losses_runs) / len(losses_runs) for i in range(epochs)]\n",
    "\n",
    "        # Calculate min and max values for the envelope\n",
    "        min_losses = [min(run[i] for run in losses_runs) for i in range(epochs)]\n",
    "        max_losses = [max(run[i] for run in losses_runs) for i in range(epochs)]\n",
    "\n",
    "        # Plot mean line\n",
    "        ax.plot(mean_losses, label=model_name, color=color, linestyle=linestyle, linewidth=2)\n",
    "\n",
    "        # Plot envelope\n",
    "        ax.fill_between(range(epochs), min_losses, max_losses, color=color, alpha=0.2)\n",
    "\n",
    "    # Customize plot\n",
    "    ax.set_title('Training Loss (MSE)', fontsize=14, pad=10)\n",
    "    ax.set_xlabel('Epoch', fontsize=12)\n",
    "    ax.set_ylabel('Loss (MSE)', fontsize=12)\n",
    "    ax.legend(fontsize=10, bbox_to_anchor=(1.05, 1), loc='upper left')\n",
    "    ax.grid(True, linestyle='--', alpha=0.7)\n",
    "    ax.spines['top'].set_visible(False)\n",
    "    ax.spines['right'].set_visible(False)\n",
    "\n",
    "    plt.tight_layout()\n",
    "    # plt.savefig(\"./results/expressive_power_vqc_loss.png\")\n",
    "    plt.show()\n",
    "    plt.clf()\n",
    "\n",
    "\n",
    "# Plot training curves\n",
    "plot_training_curves(all_results)"
   ],
   "id": "f7162c5436e42a80",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 94
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## 5. Summary of results",
   "id": "7f6478657dd87945"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-05T18:49:29.316817Z",
     "start_time": "2025-06-05T18:49:29.306345Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def print_summary_statistics(all_results):\n",
    "    \"\"\"Print summary statistics for all models\"\"\"\n",
    "    print(\"\\n----- Model Comparison Results -----\\n\")\n",
    "\n",
    "    for model_name, model_data in all_results.items():\n",
    "        # Calculate statistics across runs\n",
    "        final_mses = [run['train_mses'][-1] for run in model_data['runs']]\n",
    "        avg_mse = sum(final_mses) / len(final_mses)\n",
    "        min_mse = min(final_mses)\n",
    "        max_mse = max(final_mses)\n",
    "        std_mse = (sum((mse - avg_mse) ** 2 for mse in final_mses) / len(final_mses)) ** 0.5\n",
    "\n",
    "        print(f\"{model_name}:\")\n",
    "        print(f\"  Final train MSE: {avg_mse:.6f} ± {std_mse:.6f} (min: {min_mse:.6f}, max: {max_mse:.6f})\")\n",
    "        print()\n",
    "\n",
    "# Print summary statistics\n",
    "print_summary_statistics(all_results)"
   ],
   "id": "196c3c06801ddf01",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "----- Model Comparison Results -----\n",
      "\n",
      "VQC_[1, 0, 0]:\n",
      "  Final train MSE: 3.913869 ± 0.005102 (min: 3.908540, max: 3.920746)\n",
      "\n",
      "VQC_[1, 1, 0]:\n",
      "  Final train MSE: 2.279082 ± 0.011891 (min: 2.269184, max: 2.295804)\n",
      "\n",
      "VQC_[1, 1, 1]:\n",
      "  Final train MSE: 0.004431 ± 0.003619 (min: 0.000000, max: 0.008866)\n",
      "\n"
     ]
    }
   ],
   "execution_count": 95
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## 6. Visualize learned functions",
   "id": "90d96ce0b3d2403b"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-05T18:49:34.594692Z",
     "start_time": "2025-06-05T18:49:34.326617Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def visualize_learned_function(models, names, colors, x, g):\n",
    "    \"\"\"Visualize learned function of different models to compare them with the target function, g(x)\"\"\"\n",
    "    # Plot using matplotlib\n",
    "    plt.figure(figsize=(8, 4))\n",
    "    plt.scatter(x, g, label='g(x)', s=30)\n",
    "\n",
    "    for model, name, color in zip(models, names, colors):\n",
    "        model.eval()\n",
    "        with torch.no_grad():\n",
    "            output = model(torch.tensor(x, dtype=torch.float).unsqueeze(-1))\n",
    "        plt.plot(x, output.detach().numpy(), label=name, color=color, linewidth=3)\n",
    "\n",
    "    plt.title('Visualization of g(x) from Fourier Series and three VQCs after training')\n",
    "    plt.xlabel('x')\n",
    "    plt.ylabel('g(x)')\n",
    "    plt.grid(True)\n",
    "    plt.legend()\n",
    "    plt.tight_layout()\n",
    "    # plt.savefig(\"./results/expressive_power_of_the_VQC.png\")  # To save the figure locally\n",
    "    plt.show()\n",
    "    plt.clf()\n",
    "    return\n",
    "\n",
    "names = [\"VQC_[1, 0, 0]\", \"VQC_[1, 1, 0]\", \"VQC_[1, 1, 1]\"]\n",
    "colors = [\"blue\", \"orange\", \"red\"]\n",
    "visualize_learned_function(models, names, colors, x, g)"
   ],
   "id": "dd277788a5a12877",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 96
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "Hence, this result demonstrates that a quantum model with more input photons can be more expressive. [This paper](https://arxiv.org/abs/2107.05224) explains this phenomena by the fact that quantum models with more input photons have access to more basis functions, so they can learn Fourier series with  higher frequencies.",
   "id": "458e4340f599f728"
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}