mosaic.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "593e90ad-fd42-43b4-b733-7c16f10b0992",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from qiskit import QuantumCircuit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "0e07b053-f493-4b4e-a766-970c4714d842",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# User inputs\n",
    "want_colors = 1 # from input 1=color, 0=b/w\n",
    "seconds = 180 # length of generated gif\n",
    "fps = 12\n",
    "x = 100 # dimension of generated image ( # of tiles in x)\n",
    "y = 100 # dimension of generated image ( # of tiles in y)\n",
    "\n",
    "frameAmount = seconds * fps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "c907569a-af45-4c59-bae2-7947936a230c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[<IBMBackend('ibm_nairobi')>, <IBMBackend('ibm_perth')>, <IBMBackend('ibm_brisbane')>, <IBMBackend('ibmq_qasm_simulator')>, <IBMBackend('simulator_mps')>, <IBMBackend('simulator_stabilizer')>, <IBMBackend('simulator_extended_stabilizer')>, <IBMBackend('simulator_statevector')>, <IBMBackend('ibm_lagos')>]\n"
     ]
    }
   ],
   "source": [
    "from qiskit_ibm_provider import IBMProvider\n",
    "\n",
    "provider = IBMProvider()\n",
    "print(provider.backends())\n",
    "backend = provider.get_backend('ibm_nairobi')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "c24b494f-a11e-4596-a501-2bf8cadd768a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<qiskit.circuit.instructionset.InstructionSet at 0x7fbb3a9d8fa0>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a Quantum Circuit acting on a quantum register of seven qubits\n",
    "qubits = 7\n",
    "circ = QuantumCircuit(qubits)\n",
    "\n",
    "#always is variable 0-1\n",
    "circ.h(1) #shape\n",
    "circ.h(5) #rot1\n",
    "circ.h(6) #rot2\n",
    "\n",
    "# set q0 to 1 if wants colors\n",
    "if want_colors:\n",
    "    circ.x(0)\n",
    "\n",
    "# Set colors to 0-1 if q0 is 1 (want_colors)\n",
    "circ.ch(0, 2) #colors1 0-1 if q0 is 1\n",
    "circ.ch(0, 3) #colors2 0-1 if q0 is 1\n",
    "\n",
    "# Set b/w-w/b to 0-1 if !want_colors\n",
    "circ.x(0) #invert q0\n",
    "circ.ch(0, 4) #b/w 0-1 if q0 is 1 now"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "a029bb9f-31de-4cb5-ade9-f18bf461c40f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 538.33x618.722 with 1 Axes>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "circ.draw('mpl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "0ed14bab-4a2e-4d68-b625-b9836136ddd7",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1207.22x702.333 with 1 Axes>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a Quantum Circuit\n",
    "meas = QuantumCircuit(qubits, qubits)\n",
    "meas.barrier(range(qubits))\n",
    "# map the quantum measurement to the classical bits\n",
    "meas.measure(range(qubits), range(qubits))\n",
    "\n",
    "# The Qiskit circuit object supports composition.\n",
    "# Here the meas has to be first and front=True (putting it before) \n",
    "# as compose must put a smaller circuit into a larger one.\n",
    "qc = meas.compose(circ, range(qubits), front=True)\n",
    "\n",
    "#drawing the circuit\n",
    "qc.draw('mpl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4882d030-06d3-4d1c-9331-b43d8c6cd186",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Adding the transpiler to reduce the circuit to QASM instructions\n",
    "# supported by the backend\n",
    "from qiskit import compiler\n",
    "\n",
    "qc_compiled = compiler.transpile(qc, backend, optimization_level=2)\n",
    "\n",
    "# Execute the circuit on the qasm simulator.\n",
    "# We've set the number of repeats of the circuit\n",
    "job = backend.run(qc_compiled, shots=x*y, memory=True)\n",
    "jobGlitch = backend.run(qc_compiled, shots=1000, memory=True)\n",
    "\n",
    "# Grab the results from the job.\n",
    "result = job.result()\n",
    "resultGlitch = jobGlitch.result()\n",
    "\n",
    "memory = result.get_memory(0)\n",
    "memoryGlitch = resultGlitch.get_memory(0)\n",
    "print(memory)\n",
    "print(len(memory))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1827d0f9-14bd-4b7d-823f-c82325db5dc6",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "counts = result.get_counts(qc_compiled)\n",
    "print(counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b336fb1e-d4ff-4643-bb42-8602110120a3",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [],
   "source": [
    "from PIL import Image, ImageDraw, ImageFilter\n",
    "\n",
    "tileFileNames = ['0000000','0000001','0000010','0000011','0001000','0001001','0001010','0001011','0010000','0010001','0010010','0010011','0011000','0011001','0011010','0011011','0100000','0100001','0100010','0100011','0101000','0101001','0101010','0101011','0110000','0110001','0110010','0110011','0111000','0111001','0111010','0111011','1000000','1000001','1000010','1000011','1000100','1000101','1000110','1000111','1100000','1100001','1100010','1100011','1100100','1100101','1100110','1100111']\n",
    "tiles = {}\n",
    "for tile in tileFileNames:\n",
    "    tiles[tile] = Image.open('tiles/'+tile+'.png')\n",
    "\n",
    "tile_size = 128\n",
    "image = Image.new(mode=\"RGB\", size=(x*tile_size, y*tile_size))\n",
    "\n",
    "for idx, i in enumerate(memory):\n",
    "    tile_x = idx % x\n",
    "    tile_y = idx // x\n",
    "    im = tiles[i[::-1]]\n",
    "    image.paste(im, (tile_x*tile_size, tile_y*tile_size))\n",
    "image.save('output.png', quality=100)\n",
    "display(image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5ed96694-da98-4f5e-a8e3-a5512edd2979",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Create a Quantum Circuit\n",
    "glitchingCircuit = QuantumCircuit(4)\n",
    "\n",
    "glitchingCircuit.h(0)\n",
    "glitchingCircuit.h(1)\n",
    "glitchingCircuit.h(2)\n",
    "glitchingCircuit.h(3)\n",
    "\n",
    "glitchingCircuit.u(2, 0, 3, 1)\n",
    "glitchingCircuit.u(2, 0, 3, 2)\n",
    "glitchingCircuit.u(2, 0, 3, 3)\n",
    "glitchingCircuit.u(2, 0, 3, 0)\n",
    "# glitchingCircuit.measure(2,[0])\n",
    "glitchingCircuit.x(3)\n",
    "\n",
    "meas = QuantumCircuit(4, 4)\n",
    "meas.barrier(range(4))\n",
    "# map the quantum measurement to the classical bits\n",
    "meas.measure(range(4), range(4))\n",
    "\n",
    "# The Qiskit circuit object supports composition.\n",
    "# Here the meas has to be first and front=True (putting it before) \n",
    "# as compose must put a smaller circuit into a larger one.\n",
    "qc = meas.compose(glitchingCircuit, range(4), front=True)\n",
    "\n",
    "#drawing the circuit\n",
    "qc.draw('mpl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c7d82cbd-a05d-4183-90ab-aba45347666d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#prepare randomnes data for glitching\n",
    "\n",
    "qc_compiled = compiler.transpile(qc, backend)\n",
    "\n",
    "glitchOpportunities = frameAmount\n",
    "job = backend.run(qc_compiled, shots=glitchOpportunities, memory=True)\n",
    "\n",
    "# Grab the results from the job.\n",
    "result = job.result()\n",
    "\n",
    "glitchMemory = result.get_memory(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "16f0cbcd-de4c-4e52-a449-bc62292bea61",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "counts = result.get_counts(qc_compiled)\n",
    "print(counts)\n",
    "from qiskit.visualization import plot_histogram\n",
    "plot_histogram(counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "96fd15a2-dbf2-4b9a-8f7c-3879aca566dd",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from copy import deepcopy\n",
    "\n",
    "timePerFrame = 1 / fps\n",
    "\n",
    "frames = [image];\n",
    "\n",
    "for frame in range(frameAmount):\n",
    "    newImage = deepcopy(frames[-1])\n",
    "    for idx, i in enumerate(memory):\n",
    "        if glitchMemory[(idx * frame) % frameAmount] == '0010':\n",
    "            tile_x = idx % x\n",
    "            tile_y = idx // x\n",
    "            randomFile = memoryGlitch.pop()\n",
    "            im = tiles[randomFile[::-1]]\n",
    "            newImage.paste(im, (tile_x*128, tile_y*128))\n",
    "    frames.append(deepcopy(newImage))\n",
    "    \n",
    "frames[0].save('output.gif',\n",
    "               save_all=True, append_images=frames[1:], optimize=False, duration=timePerFrame, loop=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "46cabfee-ad2b-4e09-a963-4bff53b0abba",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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