Deustch Josza

deustsch_josza.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "45a73a54",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install qiskit\n",
    "!pip install qiskit_aer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "dd1a9bcb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 580.387x284.278 with 1 Axes>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from qiskit import QuantumCircuit\n",
    "\n",
    "qc = QuantumCircuit(2)\n",
    "qc.h(0)\n",
    "qc.cx(0, 1)\n",
    "qc.measure_all()\n",
    " \n",
    " \n",
    "qc.draw(\"mpl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0b3de757",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from qiskit import transpile\n",
    "from qiskit_aer import AerSimulator\n",
    "from qiskit.visualization import plot_histogram\n",
    "\n",
    "backend = AerSimulator()\n",
    "circ = transpile(qc, backend)\n",
    "\n",
    "# Run and get counts\n",
    "result = backend.run(circ).result()\n",
    "counts = result.get_counts(circ)\n",
    "plot_histogram(counts)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14d83f8a",
   "metadata": {},
   "source": [
    "## Deutsch Oracle Problem\n",
    "\n",
    "*Reference:* https://quantum.cloud.ibm.com/learning/en/modules/computer-science/deutsch-jozsa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d412fd01",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[6 5 7 1]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1123.61x367.889 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from qiskit import QuantumCircuit\n",
    "import numpy as np\n",
    " \n",
    " \n",
    "def dj_function(num_qubits):\n",
    "    \"\"\"\n",
    "    Create a random Deutsch-Jozsa function.\n",
    "    \"\"\"\n",
    " \n",
    "    qc_dj = QuantumCircuit(num_qubits + 1)\n",
    "    if np.random.randint(0, 2):\n",
    "        # return constant circuit with 50% chance.\n",
    "        if np.random.randint(0, 2):\n",
    "            qc_dj.x(range(num_qubits)) # Constant case f(x) = 1\n",
    "        return qc_dj\n",
    " \n",
    "    # If the \"if\" statement above was \"TRUE\" then we've returned the constant\n",
    "    # function and the function is complete. If not, we proceed in creating our\n",
    "    # balanced function. Everything below is to produce the balanced function:\n",
    " \n",
    "    # select half of all possible states at random:\n",
    "    on_states = np.random.choice(\n",
    "        range(2**num_qubits),  # numbers to sample from\n",
    "        2**num_qubits // 2,  # number of samples\n",
    "        replace=False,  # makes sure states are only sampled once\n",
    "    )\n",
    "    print(on_states)\n",
    " \n",
    "    def add_cx(qc_dj, bit_string):\n",
    "        for qubit, bit in enumerate(reversed(bit_string)):\n",
    "            if bit == \"1\":\n",
    "                qc_dj.x(qubit)\n",
    "        return qc_dj\n",
    " \n",
    "    for state in on_states:\n",
    "        # qc_dj.barrier()  # Barriers are added to help visualize how the functions are created. They can safely be removed.\n",
    "        qc_dj = add_cx(qc_dj, f\"{state:0b}\")\n",
    "        qc_dj.mcx(list(range(num_qubits)), num_qubits, ctrl_state=0)\n",
    "        qc_dj = add_cx(qc_dj, f\"{state:0b}\")\n",
    " \n",
    "    # qc_dj.barrier()\n",
    " \n",
    "    return qc_dj\n",
    " \n",
    " \n",
    "n = 3  # number of input qubits\n",
    "oracle = dj_function(n)\n",
    "display(oracle.draw(\"mpl\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ad3f41f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 872.774x451.5 with 1 Axes>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "blackbox = oracle.to_gate()\n",
    "blackbox.label = \"$U_f$\"\n",
    " \n",
    " \n",
    "qc_dj = QuantumCircuit(n + 1, n)\n",
    "qc_dj.x(n)\n",
    "qc_dj.h(range(n + 1))\n",
    "qc_dj.barrier()\n",
    "qc_dj.compose(blackbox, inplace=True)\n",
    "qc_dj.barrier()\n",
    "qc_dj.h(range(n))\n",
    "qc_dj.measure(range(n), range(n))\n",
    " \n",
    "qc_dj.decompose().decompose()\n",
    " \n",
    " \n",
    "qc_dj.draw(\"mpl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "74d2a24d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "circ = transpile(qc_dj, backend)\n",
    "\n",
    "# Run and get counts\n",
    "result = backend.run(circ).result()\n",
    "counts = result.get_counts(circ)\n",
    "plot_histogram(counts, title='Deutsch Josza')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "45736b57",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'100': 249, '001': 241, '110': 269, '011': 265}\n",
      "balanced\n"
     ]
    }
   ],
   "source": [
    "## Analysis\n",
    "print(counts)\n",
    " \n",
    "if (\n",
    "    \"0\" * n in counts\n",
    "):  # The D-J algorithm returns all zeroes if the function was constant\n",
    "    print(\"constant\")\n",
    "else:\n",
    "    print(\"balanced\")  # anything other than all zeroes means the function is balanced."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "147b82e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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o+4DkPvuBpx0L3TkHOB8297lQITMfCyXz5cC5c+eq/L5uW/g7efKkJKlly5alzs/Pzy8q6t1Y+LP3CrKqWLdunSIiIipcLikpSQMHDpQkPfLII1qyZInTYipNVFSUpGt/w/z8fPn4lGzm+Pj4YstK0p49e+Tl5aWHH364xPLp6emSpJ07d6px48aSpLi4uGo9WzE8PLxSV/zJzh8GnPz8e922/AXFTl8uSWp+Rw+d3ri7wvXyrmTKmpsvbz9fJX29R7KV/SsAezQJb+LQXzM4W9rOj5V1Yr+yz8QpbfvKEvM7Lj4ivwYt7N5ekyZNHPqrNnvb3904Kg/MngOS5/UDjuwDJOfngKPbX3JcDtAHXGPmHJA8rw+QPKv9Jfc+DpTX/pnn0lQ7qpm8/HxK3N4oqHE9ZV+4LKuLft0scS7AuYDnnAtI7psDFbW9O/cDnnQs4FzAfc8FPPl8WPKcY6GR9gHJPfYDTzsWunMOcD5s7nOhQmY+FkrmyAGr1Vp0wVSjRlUvc7pt4S8jI0NSyWfQFVq5cqVSU1MVGhqqVq1a1WRo5Tpz5oxiYmKUkJCgBx98UO+//768vb1rNIZu3brJz89POTk52r17t/r06VNime3bt0uSevfuXWy61Wott5Kcl5dXNN9azfv6Hjt2TMHBwXYtm5eZrQ8iR9q17OW4JMkm1YlqpktxSQpt1VhXl1dcHb914e/l5eujS3Gn1fkPw5W4bqeunqx6VT3uWJx8gwKqvP6NsvKl/p85ZFNlqh8zSvVjRjlse3FxxxTooB6mMu3vbhyVB2bPAcnz+gFH9gGS83PA0e0vOS4H6AOuMXMOSJ7XB0ie1f6Sex8Hymv/1H3H1XRQV4V1a6vz3x0tmu7t76t6N0Xo3K6jpa5XUzgXcNjmPK4f8LRzAcl9c6CitnfnfsCTjgWcC7jvuYAnnw9LnnMsNNI+ILnHfuBpx0J3zgHOh819LlTIzMdCyRw5kJGRUfTou8IaTlU47/K4aiq8qmzPnj0l5iUnJxc9g65z586yWCw1Glt5ZsyYoePHj2vYsGFasWJFqVfbOVtoaKjuuOMOSdI//vGPEvO3bt2quLg4+fn56b777iuafunSJdlstlL/TZ8+XZI0cODAoml16tSpkc9TFae/3K3md/aUT1CA8tIrrqhHPzVE4bfepH2v/UffjF0gL29v3fr6czUQKQBnoR8AzI0+wDxOrN0pm9WqDmOHFpvedsTt8g0KUMLqmnvWNtwL/YB50A+gNPQBAPuBWXAcRFnoA8zLbQt/t99+uyRp7ty5iouLK5oeGxurmJgYpaamSpK6du3q9FgGDhyo4cOH23V12qJFizRjxgytXLnSJUW/QtOmTZPFYtGyZcv0z3/+s2h6fHy8nnrqKUnSb3/7WzVs2NBVITrV6Y271fzXPdRkUBed3Xqg3GVDWzXWzVMf1897j+nQ4k90KS5J+xb8W437dlT0U0NqKGJUR+sHB6jzH4ar8x+GK6B+LfmGBhW9bv3gAFeHBxehHzAX+gH8En2AeVz68ZR+XPZfRQzto5ilk9T28dvUY/oT6vXSaKXsPKyE1VX/lSQ8G/2AedAPoDRm6wM4H0ZpzLYfmBXHQZSFPsC83PZWn5MnT9aHH36o06dPq2PHjmrfvr2ys7N1/PhxDR48WBEREfriiy+KPd/PWWbMmGH3soGBgXrxxRcd9t47duwodlVeZua1B0F+9NFHWr9+fdH0tWvX6tZbby163adPH82ZM0cvvPCCxowZo+nTp6tu3bo6dOiQ8vPz1bt3b82ZM8dhcbqbc98dVa3W4Wo5uJe2T/jfshe0WNRv4Th5eXlp+4TFsv3f7UsP/e9atRzSWzdPfVxJX/1QrcuZ4XxRj92mxrd0LDbt5hcek6RrJzir+GWTGdEPmAv9AH6JPsBcvn9xudJP/6yokber2W03K/viFR1993Ptnbey2s+kgOeiHzAX+gH8ktn6AM6HURqz7QdmxnEQpaEPMC+3Lfw1a9ZM27Zt06RJk7RlyxYlJiaqQ4cO+vvf/66xY8cqMjJSkmqk8OdKeXl5unDhQonpOTk5ysnJKbbcL02ePFldunTRggULFBsbq3PnzikqKkojRozQxIkT5e/v79TYXclWYNWZb/Zf+385zyLs+Ow9atSrvXbP/JcuHztzfX2rVdsnLNY9G1/Vra8/p/8+MN3pMaPq/juc9kFJ9APmQj+AX6IPMBeb1arDf/9Uh//+qatDgRuhHzAX+gH8ktn6AM6HURqz7QdmxnEQpaEPMC+3vdWnJEVHR2v9+vW6evWqrl69qu+++07PPPOMMjIylJiYKC8vL910002uDtOpBg0aVOZz9278N2jQoFLXv/POO7Vx40alpaUpKytLhw8f1tSpUytd9HvppZdks9n0zTffVP9D1ZBTn3+vU/+NLXN+7bZNdfPkR3V+9086/LeSB0UuZwY8H/0AYG70AQDoBwBzow8A2A8As6MPMCe3veKvPIcPH5bNZlNUVJSCgoJKzF+1apUk6ciRI8VeR0REqEePHjUXKFzq5Ppvy51/+dgZ/avV4+Uuc/DNNTr45hpHhgWgBtEPAOZGHwCAfgAwN/oAgP0AMDv6AHPyyMLfwYMHJZV9m8+HHnqo1NejR4/W8uXLnRobALha/pULinvxtqLX1pxM5aQkqMt75+UTWq/cddN2rNKVA1+r5e/e0uXdn+nMB9Mkm1W2gnw1HjZJ9X812tnhA3CS7LPHlLhwtPKvpso7qLYiJixXYIuOJZaLm/5r5aelSF5e8g4MVfOxbyiodTe75wPwPIXH/+ZPva6E+Y8q+/QRefkFyqd2Q7X43VsKCG/j6hDhAmk7Vuny7g3Kz0gjJwyiOsfwwn6iTs97GCPAo7EfAKgMzoc828GxEbL4+MvLP1CS1Hj4FNXr/0iF6xnh+1FDFv5sPLAUqJT89Es6Mv4mWXOz5BfWXLa8HOWkJKjeoFGKeP4dV4eHSvKpVV8dFu4rep2yZr7SD2+psOgnSZd2rVG9mCdks9l04vWRinrlGwVFdFbOuUQd/n171enzgLyDQp0YPVyFfsD4Ti35rcLufEZht41R2o5VSlw0RtELSt7uo/Wkf8snpI4kKe3bNUpcNEYdFu23ez48E32AuRUe/yWpwa+fUa3ug2WxWHR+w2KdXPy02r3yjWsDhEtc2rVGdfs9LIu3LzlhENU5hhf2EycWPMYYAR6N/QDl4ZwYv8T5kOdrPWmlglp3rdQ6Rvh+1JCFPwCV4xNSR/UGPC7vwFCFP/IXXd7zhVJWzeKkxiAufLVUTUfNllT+SWzL372l9B93KGLC8msrWiwqyLgkSSrIuiKf0Pqy+Fbu+aDwHPQDxpZ36bwyju9W2xkbJUl1bhmuU2+PU3by8RK/Uiz8IkSSCjIvSxZLpebDM9EHGJu9x3+Lj69q97j+3I7gqD4698l8F0YOZ6noi01bfl6xvChETni2so7hlckHxgjwdOwHKA/nxObC+ZB5meH7UY8s/G3atMnVIQAe5cfJfZV99lip8zq8vld+DZor88Q+Nbx7vCQpM/4HbttmEOlHdyo/PU21e94tqfyT2Ct7Nyq4/S1FJzOt/7hS8bMfkHdAsPLT0xT5p9Xy8vVz5cdBNdAPmFtu6mn51g2XxfvaqZ/FYpFfgxbK/flUqbcnOfH6E7p6cLMkqe2Ln1V6PtwPfYC5Veb4f6Pz6xepTq/7XBAxnK2iLzavHtxcal6QE56vtGN4ZfKBMQKMgP3AvDgnxo04HzKHxIVPyCabgtv2UtMn5si3dgNTfD/qkYU/AJXTfl75D3GVpKwT+4pOZjLjf1CdXvc6OyzUgNSvlqp+zBNFX/ZLKvMk9tJ3n6hun2GSJFtBvpL/81dFTlmt0I4DlHEsVsdfuVcd3zgon1phNf9BUG30A8ZW0QC2slr9v/ckSRc2/VNJ771QorhX0Xy4H/oA2HP8v1Hyf2YpJ/m4Ws78ukbjhGNU94vN0vKCnHBv9rS5VPYx3J58YIwAd8d+gIpwTmwunA+h3ayt8mvQQrb8PJ35YJoSF42usM83yvejFP4AKPfCGUkW+dVvKknKSjyg8If+7NqgUG0FWelK2/7vEs/wKu0k1maz6fLeL9R09Lxr0xP2Ke/iWYV2HCBJCm7bU371mykzYa9qdb2jZj8IagT9gGeraABr8fVXXlqybAX5snj7yGazKffnU/Jr0KLc9er/arROvvWs8q9ckE+t+pWeD89BH2B89hz/C6Wsma9L365W25e/kpd/kCvCRTVV54vN0vKCnHB/9rT5jX55DLcnHxgjwN2xH6C6OCc2Fs6HUPidh8XHV43u+YMO/S6qaJ7Rvx/1cnUAAFwvM2FvsV+0eAfX0fnPlrgwIjhC2vaVCmzVRQHN2hdNK+0kNrBlJ2XGfa/AZtHyDgyRJPk1aK68i8nKOn1UkpSdfFw5KfEKaNquxj8Hagb9gLH51mmooMibdeGb9yVJl3Z+LL/6zUrc5jM//ZJyL5wten1p1yfyCa0v79B6ds2H56IPMDZ7j/+SdG7ta0rbtkJtX/6y2HOQYCxl5YSkEnlBThhDecdwe/OBMQI8HfsBKsI5sblwPmRsBdkZyk+/VPT64rYVRfu3Gb4f5Yo/AKrT827V+b9nwEkqcYUYPFPql0sV9uuxxaaVdRLrUytMdXrfXzTdt04jtfz920p49WFZLF6y2axq8cziCq8OgueiHzC+lr/7uxLfGKOUVbPkHVhLEeOXFc1LfPNp1el1rwJbdVHCvIdkzc2SxeIln1oN1GbaelksFklSQeblcufDc9EHGJu9x//c1CQlvTtRfo1bK25ajCTJ4uOv6Pnf1XTIcLKyciLi+XeUtmtNUV6QE8ZR3jHc3nxgjABPx36AinBObC6cDxlb/qVzip8zXLIWyCab/Bu1VsQfrt3q2Qzfj1L4AwCDaj9vZ4lpZZ3EHh7XUY3+urnYsvUGPKZ6Ax5zbpAAakxAs3Zl3uqk8OHlkhQ9//syt+HfsGW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      "text/plain": [
       "<Figure size 2299.49x6471.5 with 1 Axes>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from qiskit_ibm_runtime.fake_provider import FakeSherbrooke\n",
    "backend = FakeSherbrooke()\n",
    "\n",
    "transpiled_circ = transpile(qc_dj, backend=backend, optimization_level=3)\n",
    "transpiled_circ.draw(\"mpl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dc403765",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "qiskit",
   "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": "3.12.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}