ai-permutation-synthesis-demo.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Install the plugin\n",
    "# !pip install -e ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit import QuantumCircuit\n",
    "from qiskit.transpiler import PassManager\n",
    "from qiskit_transpiler_service.ai.collection import CollectPermutations\n",
    "from qiskit_transpiler_service.ai.synthesis import AIPermutationSynthesis\n",
    "from qiskit.circuit.library import Permutation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 1: Create your circuit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original circuit -> Depth: 78, Gates(2q): 78\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 693.779x687.283 with 1 Axes>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_qubits = 27\n",
    "circuit = QuantumCircuit(num_qubits)\n",
    "circuit.append(\n",
    "    Permutation(\n",
    "        num_qubits=num_qubits, pattern=[(i + 1) % num_qubits for i in range(num_qubits)]\n",
    "    ),\n",
    "    qargs=range(num_qubits),\n",
    ")\n",
    "circuit = circuit.decompose(reps=2)\n",
    "\n",
    "print(\n",
    "    f\"Original circuit -> Depth: {circuit.decompose(reps=3).depth()}, Gates(2q): {circuit.decompose(reps=3).num_nonlocal_gates()}\"\n",
    ")\n",
    "circuit.draw(output=\"mpl\", fold=-1, scale=0.3, style=\"iqp\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2: Optimize your circuit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Further optimization with AI Synthesis passes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "ai_optimize_perms = PassManager(\n",
    "    [\n",
    "        CollectPermutations(do_commutative_analysis=True),\n",
    "        AIPermutationSynthesis(backend_name=\"ibm_cairo\"),\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:qiskit_transpiler_service.ai.synthesis:Requesting synthesis to the service\n",
      "ERROR:qiskit_transpiler_service.wrappers.base:No model available for the requested subgraph\n",
      "WARNING:qiskit_transpiler_service.ai.synthesis:AIPermutationAPI couldn't synthesize the circuit: 'No model available for the requested subgraph'\n",
      "ERROR:qiskit_transpiler_service.wrappers.base:Qargs do not form a connected subgraph of the backend coupling map\n",
      "ERROR:qiskit_transpiler_service.wrappers.base:Qargs do not form a connected subgraph of the backend coupling map\n",
      "WARNING:qiskit_transpiler_service.ai.synthesis:AIPermutationAPI couldn't synthesize the circuit: 'Qargs do not form a connected subgraph of the backend coupling map'\n",
      "WARNING:qiskit_transpiler_service.ai.synthesis:AIPermutationAPI couldn't synthesize the circuit: 'Qargs do not form a connected subgraph of the backend coupling map'\n"
     ]
    }
   ],
   "source": [
    "# AI Synthesis passes respect the coupling map and should run after transpiling\n",
    "ai_optimized_circuit = ai_optimize_perms.run(circuit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AI-Optimized circuit -> Depth: 78, Gates(2q): 78\n"
     ]
    },
    {
     "data": {
      "image/png": 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/lvm1Zuj9+ZctW4Zly5bddfxohj209gX4WjqQ/9//gc8PnlJ24FGSvL99JPT5ld6f31B9/qvllbharuiSuqr98xEAwD9/886wvx94Pz/w7wBwdttbg1+f/+3eEddrq24YcX0aGe/2kFgMP4nF8JNYDD+JxfCTWAw/icXwk1gMP4nF8JNYDD+JxfCTWJr2+fft24eoqCisWbMGFotlzOsO7G1/ZisrujRxmu7Pf+7cOcTExMDv949r3YG97YmUoGmff/78+fB6vfB4PEhISBg8frR9fiP0ye+F80+c2P35D619ASF/H/LK1uH8y++io/YLRdYdC8n72xt9/pEYqs/Pve1JSbzbQ2Ix/CQWw09iMfwkFsNPYjH8JBbDT2Ix/CQWw09iMfwkFsNPYmna56+ursbbb7+NzZs3IyMjY8zrZjy9GCmPzkX93qPD9qcnGg9N+/z5+fnIzMwcV/CBOxvVXtn/EaakOZQcl4TStM/f19cHh+Pu4I62z++MjcXMVY/h0+371Bj7viT04e/FKPNP2j7/rCcWoq/Lh6vllei4dF2RdcdCch/e6POPxFB9/jO/2q3oeiQb7/aQWAw/icXwk1gMP4nF8JNYDD+JxfCTWAw/icXwk1gMP4nF8JNYhgp/2rI8LPj5d+HIzdR7FJoENH2YZefOnQgGg1i/fj2cTueY122ruYLZ6woR6guqMDVJo+nDLP39/WhtbYXVah3XuoFb3Tj70tuYmpmq5LgklKYPs4TDYaSlpaG1tRXTpk0bPH60D7MUrH8cSflz0LDvuBpj35eEh0HuxSjzT9qHWYz8MAXnnzilH2ZR9VtZ6YdZiJRkqLs9REpi+Ekshp/EYvhJLIafxGL4SSyGn8Ri+Ekshp/EYvhJLMOFv2DrBtiz0/UegyYBTfv8u3btgt/vR2lpKex2+5jXTS10oa2mUfmBSSRN+/zz5s1DOBxGbe342oGOnAwkP5yF5Pw5So5LQmna57fZbAgGg3C5XMOOH22fP/l0LVKX5KLX26nG2PcloQ9/L0aZn31+lUjuwxt9/pGwz09iGe5uD5FSGH4Si+EnsRh+EovhJ7EYfhKL4SexGH4Si+EnsRh+EkuVYpta8rZ8E6HeABoOVMDfpk+5jSYP1fv8mZmZsFqt2LhxIw4dOoT29naUlJSMq88fuNUNi92G/lBY+aFJHNX7/Fu2bIHJZILVakVvby9WrlwJt9s9rnXr3nwf9e98iFlPLFR4YpJIlUrztm3bYLfbcfz4cSxZsgQ2mw2lpaU4ePAgvF4vSkpKkJiYOHj8aPv8j61chuQFWah/50N0XLqu9Nj31WoL46P54/tUmOVuM5K79P0RS8L87POrRHIf3ujzj4R9fhKLtzpJLIafxDJ0+HN//AzyytZh9trCUf+f1CW5d+37M++5J+/829JcReejyGaoX3L9q5A/gAt/OIJnr7wFe3Y6PFW1SHo4C2F/H8LBEOJS7DBZLbi48yDsc9NhS09BoLMb0wuyYYqNwcxvPIKmkxeQ8shD8FTVwZ6dDvu8WbA6ElC/9xgW/OJ78HnaceaXb+r9UkkFhr7ym2ItyP/Zt+E5XYvA7R7EJk3FtSNVCAX6cPFPB+Fr6UDL2Tr0h+/8UiwqOho9N7z44kgVYuLj0NPshSMnAy3nLsP72Z3NsEwxZlza8w+kPPIQWs9dRkftNZjjYvV8maQSQ4c/5A+gevs+tJyvR8gXgN97G/3h8OBvgMN9QaAf6A+F4SyYB4crEz2edmQ+sxRT0hwYuMlrjotF0lcbYYX6gpj7bDFazl2+803T3w9ERen1EklFhn7bU/vnIwCAf/7mnWF/31F3bdi/A8DZbW8Nfn3+t3tHXK+tumHE9WlyMvSVn2giGH4Si+EnsQwVfodrNlw/WQeHa7beo9AkoGmf/8KFC6iqqkJZWdm41n3g8XyE/IHBW5dEE6Fpn3/RokWDe/WPh9WRAPeuw3hgeb6C05JUqu/P//rrr8NmsyEQCKChoWFwr/74+PjB40fb5482m+DavBadV26oMfZ9Sdjf/l6MMj/7/CqR3Ic3+vwjYZ+fxDLU3R4iJTH8JBbDT2Ix/CQWw09iMfwkFsNPYjH8JBbDT2Ix/CSWoZ7hTSvKg3PRfPTcbMel3X/XexwyOE37/NXV1aisrMTGjRvHVW1uqqhB3HQ7mk58psLUJI2mff7CwsLBr8drSmoSfDfbFZyWpNK0z//KK68Mfm02f33q0fb5nXFWhHoDaow8KhL68PdilPnZ51eJ5D680ecfCfv8JBZvdZJYDD+JxfCTWAw/icXwk1gMP4nF8JNYDD+JxfCTWAw/icXwk1ia9vnr6+tx9OhRlJaWwul0jnndeRueRHSMCe21X6CpokaFyUkSTfv8WVlZ6OzshMViGffaUx5Igs/TodywJJamfX63243ExER0dnYiMTFx8PjR9vkLZk3H2V/vQfb3Vw1+3KiWJPTh78Uo80/aPn/yw1kwWS3wnHbjZqVbkXXHQnIf3ujzj0TVB9iV7vNf/NNBRdcj2Xi3h8Ri+Ekshp/EYvhJLIafxGL4SSyGn8Ri+Ekshp/EYvhJLIafxNK0z2+1WrFhwwbs2bNnXOtmlayAbWYKGv/6MW5d/vctUKL70bTPf/jwYRQWFo573djEBNTtfh8zv/GogtOSVJr2+bu6ulBfX4+WlhakpKQMHj/aPr/LbELmmqUI9vSqMfZ9SejD34tR5p+0ff5oswnTF2ajfu8x9DR7FVl3LCT34Y0+/0gM1edvPnkBzScvKLomycW7PSQWw09iMfwkFsNPYjH8JBbDT2Ix/CQWw09iMfwkFsNPYjH8JJamff7XXnsNLpcLxcXF41o3rSgPM1YswK3L12GbNR0Xd5bD772t8PQkhaZ9/vT0dLS3tyMUCo1r3aaKGnRd88AUa4F712GkLslVeHKSRNM+v9PpxIkTJ+Dz+WCz2QaPH22f3wh98nvh/BMnus8f8vchr2wdbn7ihtUxFRd3HYK/rVOR9UdDch/e6POPxFB9/rbqBhz70cuKrkly8W4PicXwk1gMP4nF8JNYDD+JxfCTWAw/icXwk1gMP4nF8JNYDD+JpWmf/8iRI4iKisKaNWtgsVjGvO5An7/l7CVMe2gGPt2xX4Xp1TUZXkPB1g249NYH6Ki7pvcoE6Jpn//cuXOIiYmB3+8f17oDff6r5ZUI3OpWeGptGP01pBa60FbTqPcYitC0zz9//nx4vV54PB4kJCQMHs8+vzaUmN+Rk4GEzFRERUVpfuVnn/+rPr9z0XxU/fINdF9vVWT90VCiD5+UP0e316BUnz91SS56vZ2ah599/q/6/O7XDim6tlYmw2toPjU5PiOBd3tILIafxGL4SSxV3/MrKWF2qt4jTGgGzj9xSs+g6t0eokjGtz0kFsNPYjH8JBbDT2Ix/CQWw09iMfwkFsNPYjH8JBbDT2Ix/CQWw09i/T9zpVeMgbJRnwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 222.802x572.736 with 1 Axes>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\n",
    "    f\"AI-Optimized circuit -> Depth: {ai_optimized_circuit.decompose(reps=3).depth()}, Gates(2q): {ai_optimized_circuit.decompose(reps=3).num_nonlocal_gates()}\"\n",
    ")\n",
    "ai_optimized_circuit.draw(output=\"mpl\", fold=-1, scale=0.25, style=\"iqp\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "env",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}