Quintessential Quantum Quintuplets

team_solutions/Quintessential_Quantum_Quintuplets/README.md

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+# Quantum Image Processing Visualization
+
+Project for IQuHack 2023 IonQ Challenge at https://github.com/iQuHACK/2023_IonQ.
+
+Image processing is in growing needs with the development of applications like facial recognition and autonomous vehicles. Due to the large volume and complexity of data, image processing is computationally expensive. More and more often, specialized hardwares such as GPUs are used to accelerate image processing. Quantum computing promises speed ups in a number of image processing algorithms, such as edge extraction [1]. 
+
+We demonstrate Quantum Image Processing on the trapped ion quantum computers provided by IonQ. We first transform the image into a qubit representation named Flexible Representation of Quantum Images (FRQI). Next, we perform Quantum Fourier Transform on the input image as a demonstration of practical image processing on quantum computers. We visualize this process by repeatedly measuring the output qubits and feed the output into a fractal generation routine.
+
+## Documentation
+
+The link to our documentation and process can be found here: https://docs.google.com/document/d/1rIbXHpgPaqvQ9ja3OycBM_sBB9STySyqZ8Fe9A27SLs/edit
+
+To generate the fractals, you can upload an image (preferably 8 x 8 or smaller) and then follow the steps in fractal_generation.ipynb. We support larger image sizes but are constrained by simulation speed/hardware limitations.
+
+## Credit
+
+Credit to https://www.linkedin.com/pulse/space-fractal-art-qiskit-wiktor-mazin-phd-mmt/ for fractal generation method.
+

team_solutions/Quintessential_Quantum_Quintuplets/gpu_fractal.ipynb

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+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    },
+    "accelerator": "GPU",
+    "gpuClass": "standard"
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# GPU Version of Fractal Generation"
+      ],
+      "metadata": {
+        "id": "cyU9rG615ujj"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 26,
+      "metadata": {
+        "id": "AEGh5t_ghw6N"
+      },
+      "outputs": [],
+      "source": [
+        "amps = {'0': (0.05334708691207961+0.02209708691207961j), '1': (0.1923456572581369+0.07967217989988726j), '10': (0.07544417382415922+0.03125j), '11': (0.15088834764831843+0.0625j), '100': (0.2502200171930884+0.10364452469860624j), '101': (0.1686983011771303+0.06987712429686843j), '110': (0.15088834764831843+0.0625j), '111': (0.2970240093698727+0.12303137303143455j), '1000': (0.23857542547399874+0.09882117688026186j), '1001': (0.2502200171930884+0.10364452469860624j), '1010': (0.17693227094584735+0.07328774624724109j), '1011': (0.10669417382415922+0.04419417382415922j), '1100': (0.07544417382415922+0.03125j), '1101': 0j, '1110': (0.05334708691207961+0.02209708691207961j), '1111': (0.10669417382415922+0.04419417382415922j), '10000': (0.07544417382415922+0.03125j), '10001': (0.11928771273699937+0.04941058844013093j), '10010': 0j, '10011': (0.07544417382415922+0.03125j), '10100': (0.10669417382415922+0.04419417382415922j), '10101': (0.11928771273699937+0.04941058844013093j), '10110': (0.1600412607362388+0.06629126073623882j), '10111': 0j, '11000': (0.18479972993502913+0.07654655446197431j), '11001': (0.1923456572581369+0.07967217989988726j), '11010': (0.10669417382415922+0.04419417382415922j), '11011': 0j, '11100': (0.09239986496751457+0.038273277230987154j), '11101': 0j, '11110': (0.09239986496751457+0.038273277230987154j), '11111': (0.09239986496751457+0.038273277230987154j), '100000': (0.09239986496751457+0.038273277230987154j), '100001': (0.15088834764831843+0.0625j), '100010': (0.10669417382415922+0.04419417382415922j), '100011': 0j, '100100': (0.2444670638799928+0.10126157341262282j), '100101': (0.05334708691207961+0.02209708691207961j), '100110': (0.09239986496751457+0.038273277230987154j), '100111': (0.13067314219850173+0.05412658773652741j), '101000': (0.05334708691207961+0.02209708691207961j), '101001': (0.1411431251391113+0.05846339666834283j), '101010': (0.10669417382415922+0.04419417382415922j), '101011': (0.05334708691207961+0.02209708691207961j), '101100': (0.07544417382415922+0.03125j), '101101': 0j, '101110': (0.13067314219850173+0.05412658773652741j), '101111': (0.13067314219850173+0.05412658773652741j), '110000': (0.10669417382415922+0.04419417382415922j), '110001': (0.10669417382415922+0.04419417382415922j), '110010': (0.11928771273699937+0.04941058844013093j), '110011': (0.09239986496751457+0.038273277230987154j), '110100': (0.1411431251391113+0.05846339666834283j), '110101': (0.10669417382415922+0.04419417382415922j), '110110': (0.10669417382415922+0.04419417382415922j), '110111': (0.1923456572581369+0.07967217989988726j), '111000': (0.1686983011771303+0.06987712429686843j), '111001': 0j, '111010': (0.05334708691207961+0.02209708691207961j), '111011': (0.10669417382415922+0.04419417382415922j), '111100': (0.3064556827767503+0.1269381000724369j), '111101': (0.27719959490254376+0.11481983169296148j), '111110': 0j, '111111': (0.3156056223678123+0.1307281291459493j)}"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "! pip install celluloid"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Kh6TqlrDh1lL",
+        "outputId": "83cc7667-32df-4d7e-8850-5b65daf993a9"
+      },
+      "execution_count": 2,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
+            "Requirement already satisfied: celluloid in /usr/local/lib/python3.8/dist-packages (0.2.0)\n",
+            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.8/dist-packages (from celluloid) (3.2.2)\n",
+            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.8/dist-packages (from matplotlib->celluloid) (1.4.4)\n",
+            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.8/dist-packages (from matplotlib->celluloid) (2.8.2)\n",
+            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.8/dist-packages (from matplotlib->celluloid) (3.0.9)\n",
+            "Requirement already satisfied: numpy>=1.11 in /usr/local/lib/python3.8/dist-packages (from matplotlib->celluloid) (1.21.6)\n",
+            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.8/dist-packages (from matplotlib->celluloid) (0.11.0)\n",
+            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.8/dist-packages (from python-dateutil>=2.1->matplotlib->celluloid) (1.15.0)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "import matplotlib.image as mpimg\n",
+        "import numpy as np\n",
+        "from celluloid import Camera\n",
+        "import torch\n",
+        "import tqdm\n",
+        "\n",
+        "c = 1.4 + 0.2j\n",
+        "\n",
+        "print(\"c= \",c)\n",
+        "\n",
+        "# Define the size\n",
+        "size = 1000\n",
+        "heightsize = size\n",
+        "widthsize = size\n",
+        "\n",
+        "def julia_set(c=c, height=heightsize, width=widthsize, x=0, y=0, zoom=1, max_iterations=100, amps=amps):\n",
+        "    # To make navigation easier we calculate these values\n",
+        "    x_width = 1.5\n",
+        "    y_height = 1.5*height/width\n",
+        "    x_from = x - x_width/zoom\n",
+        "    x_to = x + x_width/zoom\n",
+        "    y_from = y - y_height/zoom\n",
+        "    y_to = y + y_height/zoom\n",
+        "    \n",
+        "    # Here the actual algorithm starts and the z paramter is defined for the Julia set function\n",
+        "    x = torch.tensor(np.linspace(x_from, x_to, width).reshape((1, width)), device='cuda')\n",
+        "    y = torch.tensor(np.linspace(y_from, y_to, height).reshape((height, 1)), device='cuda')\n",
+        "    z = x + 1j * y\n",
+        "    \n",
+        "    # Initialize c to the complex number obtained from the quantum circuit\n",
+        "    c = torch.tensor(np.full(z.shape, c), device='cuda')\n",
+        "\n",
+        "    # To keep track in which iteration the point diverged\n",
+        "    div_time = torch.tensor(np.zeros(z.shape, dtype=int), device='cuda')\n",
+        "    \n",
+        "    # To keep track on which points did not converge so far\n",
+        "    m = torch.tensor(np.full(c.shape, True, dtype=bool), device='cuda')\n",
+        "    \n",
+        "    for key in amps:\n",
+        "        amps[key] = torch.tensor(np.full(z.shape, amps[key]), device='cuda')\n",
+        "    \n",
+        "    for i in range(max_iterations):\n",
+        "        num = z[m]**4 + sum([amps[bin(num)[2:]][m] * z[m] ** ((num // 2)  % 4) for num in range(len(amps.keys()), 2)])\n",
+        "        den = z[m]**4 + sum([amps[bin(num)[2:]][m] * z[m] ** (((num - 1) // 2) % 4) for num in range(1, len(amps.keys()), 2)])\n",
+        "        # print(num.shape)\n",
+        "        # print(den.shape)\n",
+        "        z[m] = num / den + c[m]\n",
+        "        m[torch.abs(z) > 2] = False\n",
+        "        div_time[m] = i\n",
+        "    # print(div_time.max())\n",
+        "    # print(div_time.min())\n",
+        "    return div_time.cpu().numpy()\n",
+        "\n",
+        "\n",
+        "fig, ax = plt.subplots(1, 1) \n",
+        "camera = Camera(fig)\n",
+        "def color(data, i):\n",
+        "    normalizedData = (data - np.min(data)) / (np.max(data) - np.min(data))\n",
+        "    normi = i/(frameno)\n",
+        "    # print(data.shape)\n",
+        "    rgb = np.zeros((data.shape[0], data.shape[1], 3))\n",
+        "    r = normalizedData*normi\n",
+        "    b = (1-normi)*normalizedData\n",
+        "    # print(r,b)\n",
+        "    rgb[:,:,0] = r#np.full(rgb[:,:,1].shape, 0)\n",
+        "    rgb[:,:,1] = np.full(rgb[:,:,1].shape, 0)\n",
+        "    rgb[:,:,2] = b#normalizedData\n",
+        "    return rgb\n",
+        "\n",
+        "\n",
+        "frameno = 50\n",
+        "for i in tqdm.tqdm(range(frameno)):\n",
+        "    map = julia_set(c=c, amps=amps.copy())\n",
+        "    data = color(map, i)\n",
+        "    ax.imshow(data)\n",
+        "    ax.axis('off')\n",
+        "    camera.snap()\n",
+        "    plt.close()\n",
+        "    c += 0.03 + 0.03j\n",
+        "    \n",
+        "anim = camera.animate(blit=True)\n",
+        "anim.save('1qubit_simulator_4animations_H_2' + str(frameno) + ' steps c=' + str(c) + '.gif', writer='pillow')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "53XULgCwh4Lh",
+        "outputId": "af0ecee6-07bb-4358-91ec-eec2ffb2dc1b"
+      },
+      "execution_count": 27,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "c=  (1.4+0.2j)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "100%|██████████| 50/50 [00:54<00:00,  1.09s/it]\n"
+          ]
+        }
+      ]
+    }
+  ]
+}

team_solutions/Quintessential_Quantum_Quintuplets/requirements.txt

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+anyio==3.6.2
+attrs==21.4.0
+cachetools==5.3.0
+celluloid==0.2.0
+certifi==2022.12.7
+cffi==1.15.1
+charset-normalizer==3.0.1
+cirq==1.1.0
+cirq-aqt==1.1.0
+cirq-core==1.1.0
+cirq-google==1.1.0
+cirq-ionq==1.1.0
+cirq-pasqal==1.1.0
+cirq-rigetti==1.1.0
+cirq-web==1.1.0
+contourpy==1.0.7
+cryptography==39.0.0
+cycler==0.11.0
+decorator==5.1.1
+dill==0.3.6
+duet==0.2.7
+exceptiongroup==1.0.4
+fonttools==4.38.0
+google-api-core==1.34.0
+google-auth==2.16.0
+googleapis-common-protos==1.58.0
+grpcio==1.51.1
+grpcio-status==1.48.2
+h11==0.14.0
+httpcore==0.16.3
+httpx==0.23.3
+idna==3.4
+importlib-metadata==6.0.0
+iniconfig==1.1.1
+iso8601==1.1.0
+kiwisolver==1.4.4
+lark==0.11.3
+matplotlib==3.6.3
+mpmath==1.2.1
+msgpack==0.6.2
+networkx==2.8.8
+ntlm-auth==1.5.0
+numpy==1.23.5
+packaging==22.0
+pandas==1.5.3
+pbr==5.11.1
+Pillow==9.4.0
+pluggy==1.0.0
+ply==3.11
+powerline-status==2.7
+proto-plus==1.22.2
+protobuf==3.20.3
+psutil==5.9.4
+py==1.11.0
+pyasn1==0.4.8
+pyasn1-modules==0.2.8
+pycparser==2.21
+pydantic==1.10.4
+Pygments==2.13.0
+PyJWT==2.6.0
+pyparsing==3.0.9
+pyquil==3.3.3
+pytest==7.2.0
+python-dateutil==2.8.2
+python-rapidjson==1.9
+pytz==2022.7.1
+pyzmq==25.0.0
+qcs-api-client==0.21.3
+qiskit==0.40.0
+qiskit-aer==0.11.2
+qiskit-ibmq-provider==0.19.2
+qiskit-ionq==0.3.10
+qiskit-terra==0.23.0
+requests==2.28.2
+requests-ntlm==1.1.0
+retry==0.9.2
+retrying==1.3.4
+rfc3339==6.2
+rfc3986==1.5.0
+rpcq==3.10.0
+rsa==4.9
+ruamel.yaml==0.17.21
+ruamel.yaml.clib==0.2.7
+rustworkx==0.12.1
+scipy==1.9.3
+six==1.16.0
+sniffio==1.3.0
+sortedcontainers==2.4.0
+stevedore==4.1.1
+symengine==0.9.2
+sympy==1.11.1
+toml==0.10.2
+tomli==2.0.1
+tqdm==4.64.1
+types-python-dateutil==2.8.19.6
+types-retry==0.9.9.1
+typing_extensions==4.4.0
+urllib3==1.26.14
+websocket-client==1.5.0
+websockets==10.4
+zipp==3.12.0