rabi-oscillation.ipynb

{
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  {
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   "execution_count": 23,
   "id": "48738e05",
   "metadata": {},
   "outputs": [],
   "source": [
    "import cirq\n",
    "import cirq_google\n",
    "from cirq.contrib.svg import SVGCircuit\n",
    "import sympy\n",
    "import pandas\n",
    "import datetime\n",
    "import hashlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b48a509c",
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   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "                                             (0, 5)───(0, 6)\n",
      "                                             │        │\n",
      "                                             │        │\n",
      "                                    (1, 4)───(1, 5)───(1, 6)───(1, 7)\n",
      "                                    │        │        │        │\n",
      "                                    │        │        │        │\n",
      "                           (2, 3)───(2, 4)───(2, 5)───(2, 6)───(2, 7)───(2, 8)\n",
      "                           │        │        │        │        │        │\n",
      "                           │        │        │        │        │        │\n",
      "                  (3, 2)───(3, 3)───(3, 4)───(3, 5)───(3, 6)───(3, 7)───(3, 8)───(3, 9)\n",
      "                  │        │        │        │        │        │        │        │\n",
      "                  │        │        │        │        │        │        │        │\n",
      "         (4, 1)───(4, 2)───(4, 3)───(4, 4)───(4, 5)───(4, 6)───(4, 7)───(4, 8)───(4, 9)\n",
      "         │        │        │        │        │        │        │        │\n",
      "         │        │        │        │        │        │        │        │\n",
      "(5, 0)───(5, 1)───(5, 2)───(5, 3)───(5, 4)───(5, 5)───(5, 6)───(5, 7)───(5, 8)\n",
      "         │        │        │        │        │        │        │\n",
      "         │        │        │        │        │        │        │\n",
      "         (6, 1)───(6, 2)───(6, 3)───(6, 4)───(6, 5)───(6, 6)───(6, 7)\n",
      "                  │        │        │        │        │\n",
      "                  │        │        │        │        │\n",
      "                  (7, 2)───(7, 3)───(7, 4)───(7, 5)───(7, 6)\n",
      "                           │        │        │\n",
      "                           │        │        │\n",
      "                           (8, 3)───(8, 4)───(8, 5)\n",
      "                                    │\n",
      "                                    │\n",
      "                                    (9, 4)\n"
     ]
    }
   ],
   "source": [
    "# print the cirq 54-qubit Sycamore chip\n",
    "working_device = cirq_google.Sycamore\n",
    "print(working_device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9ab52347",
   "metadata": {},
   "outputs": [],
   "source": [
    "# pick a qubit\n",
    "my_qubit = cirq.GridQubit(2, 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d5450f4a",
   "metadata": {},
   "outputs": [
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     "text": [
      "findfont: Font family ['Arial'] not found. Falling back to DejaVu Sans.\n"
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      ],
      "text/plain": [
       "<cirq.contrib.svg.svg.SVGCircuit at 0x7f41c47d0190>"
      ]
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     "execution_count": 10,
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    }
   ],
   "source": [
    "# build circuits that use your qubit\n",
    "\n",
    "# Create a circuit with X, Ry(pi/2) and H.\n",
    "my_circuit = cirq.Circuit(\n",
    "    # Rotate the qubit pi/2 radians around the X axis.\n",
    "    cirq.rx(3.141 / 2).on(my_qubit),\n",
    "    # Measure the qubit.\n",
    "    cirq.measure(my_qubit, key='out')\n",
    ")\n",
    "SVGCircuit(my_circuit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "aff77c17",
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   "outputs": [
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       "      <th></th>\n",
       "      <th>out</th>\n",
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      "text/plain": [
       "   out\n",
       "0    0\n",
       "1    0\n",
       "2    0\n",
       "3    1\n",
       "4    1\n",
       "5    0\n",
       "6    0\n",
       "7    1\n",
       "8    1\n",
       "9    1"
      ]
     },
     "execution_count": 11,
     "metadata": {},
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    }
   ],
   "source": [
    "# simulate\n",
    "sim = cirq.Simulator()\n",
    "samples = sim.sample(my_circuit, repetitions=10)\n",
    "samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "30f46a47",
   "metadata": {},
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    {
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     "text": [
      "State before measurement:\n",
      "measurements: (no measurements)\n",
      "output vector: 0.707|0⟩ - 0.707j|1⟩\n",
      "State after measurement:\n",
      "measurements: out=1\n",
      "output vector: -1j|1⟩\n"
     ]
    }
   ],
   "source": [
    "# get properties of circuit (density matrix, state vectors, etc.)\n",
    "state_vector_before_measurement = sim.simulate(my_circuit[:-1])\n",
    "sampled_state_vector_after_measurement = sim.simulate(my_circuit)\n",
    "\n",
    "print(f'State before measurement:')\n",
    "print(state_vector_before_measurement)\n",
    "print(f'State after measurement:')\n",
    "print(sampled_state_vector_after_measurement)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "00068861",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Noisy state after measurement:measurements: out=0\n",
      "final density matrix:\n",
      "[[0.9333334 +0.j 0.        +0.j]\n",
      " [0.        +0.j 0.06666666+0.j]]\n",
      "Noisy state before measurement:measurements: (no measurements)\n",
      "final density matrix:\n",
      "[[0.50012845+0.j         0.        +0.43333334j]\n",
      " [0.        -0.43333334j 0.49987155+0.j        ]]\n"
     ]
    }
   ],
   "source": [
    "# noisy environment output (10% depolarization added)\n",
    "noisy_sim = cirq.DensityMatrixSimulator(noise=cirq.depolarize(0.1))\n",
    "noisy_post_measurement_state = noisy_sim.simulate(my_circuit)\n",
    "noisy_pre_measurement_state = noisy_sim.simulate(my_circuit[:-1])\n",
    "\n",
    "print('Noisy state after measurement:' + str(noisy_post_measurement_state))\n",
    "print('Noisy state before measurement:' + str(noisy_pre_measurement_state))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6e431d24",
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   ],
   "source": [
    "# create a parameterized circuit that rotates by angle theta\n",
    "theta = sympy.Symbol('theta')\n",
    "\n",
    "parameterized_circuit = cirq.Circuit(\n",
    "    cirq.rx(theta).on(my_qubit),\n",
    "    cirq.measure(my_qubit, key='out')\n",
    ")\n",
    "SVGCircuit(parameterized_circuit)"
   ]
  },
  {
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   "execution_count": 16,
   "id": "838d2921",
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       "   theta  out\n",
       "0      2    1\n",
       "1      2    1\n",
       "2      2    1\n",
       "3      2    0\n",
       "4      2    0\n",
       "5      2    1\n",
       "6      2    0\n",
       "7      2    0\n",
       "8      2    1\n",
       "9      2    1"
      ]
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   ],
   "source": [
    "# specify theta value for simulation\n",
    "samples_at_theta_equals_2 = sim.sample(\n",
    "    parameterized_circuit, \n",
    "    params={theta: 2}, \n",
    "    repetitions=10)\n",
    "samples_at_theta_equals_2"
   ]
  },
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   "execution_count": 17,
   "id": "8200b77d",
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       "      <td>3.141</td>\n",
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       "      <th>9</th>\n",
       "      <td>3.141</td>\n",
       "      <td>1</td>\n",
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      "text/plain": [
       "   theta  out\n",
       "0  0.500    0\n",
       "1  0.500    0\n",
       "2  0.500    0\n",
       "3  0.500    0\n",
       "4  0.500    0\n",
       "5  0.500    0\n",
       "6  0.500    0\n",
       "7  0.500    0\n",
       "8  0.500    0\n",
       "9  0.500    0\n",
       "0  3.141    1\n",
       "1  3.141    1\n",
       "2  3.141    1\n",
       "3  3.141    1\n",
       "4  3.141    1\n",
       "5  3.141    1\n",
       "6  3.141    1\n",
       "7  3.141    1\n",
       "8  3.141    1\n",
       "9  3.141    1"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# multiple values for theta\n",
    "samples_at_multiple_theta = sim.sample(\n",
    "    parameterized_circuit, \n",
    "    params=[{theta: 0.5}, {theta: 3.141}], \n",
    "    repetitions=10)\n",
    "samples_at_multiple_theta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "63bbeb25",
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>theta</th>\n",
       "      <th>out</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.785397</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.785397</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.785397</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.785397</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.785397</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.570795</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.570795</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.570795</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.570795</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.570795</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.356192</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.356192</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.356192</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.356192</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.356192</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.141590</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.141590</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.141590</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.141590</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.141590</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      theta  out\n",
       "0  0.000000    0\n",
       "1  0.000000    0\n",
       "2  0.000000    0\n",
       "3  0.000000    0\n",
       "4  0.000000    0\n",
       "0  0.785397    0\n",
       "1  0.785397    0\n",
       "2  0.785397    0\n",
       "3  0.785397    1\n",
       "4  0.785397    0\n",
       "0  1.570795    1\n",
       "1  1.570795    1\n",
       "2  1.570795    0\n",
       "3  1.570795    0\n",
       "4  1.570795    0\n",
       "0  2.356192    0\n",
       "1  2.356192    0\n",
       "2  2.356192    1\n",
       "3  2.356192    0\n",
       "4  2.356192    1\n",
       "0  3.141590    1\n",
       "1  3.141590    1\n",
       "2  3.141590    1\n",
       "3  3.141590    1\n",
       "4  3.141590    1"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# cirq shorthand for multiple values sweep\n",
    "samples_at_swept_theta = sim.sample(\n",
    "    parameterized_circuit, \n",
    "    params=cirq.Linspace(theta, start=0, stop=3.14159, length=5), \n",
    "    repetitions=5)\n",
    "samples_at_swept_theta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ad7d369b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='theta'>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# use pandas for analysis\n",
    "big_results = sim.sample(\n",
    "    parameterized_circuit, \n",
    "    params=cirq.Linspace(theta, start=0, stop=3.14159, length=20), \n",
    "    repetitions=10_000)\n",
    "\n",
    "# big_results is too big to look at. Plot cross tabulated data instead.\n",
    "pandas.crosstab(big_results.theta, big_results.out).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "34af66d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/rimuru/.local/lib/python3.8/site-packages/cirq/experiments/qubit_characterizations.py:99: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "  fig.show()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Rabi Angle (Radian)', ylabel='Excited State Probability'>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# cirq built-in rabi oscillation\n",
    "result = cirq.experiments.rabi_oscillations(\n",
    "    sampler=noisy_sim,\n",
    "    qubit=my_qubit,\n",
    "    num_points=50,\n",
    "    repetitions=10000)\n",
    "result.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "57cca417",
   "metadata": {},
   "outputs": [],
   "source": [
    "# find the qubit with the lowest noise\n",
    "class SecretNoiseModel(cirq.NoiseModel):\n",
    "    def noisy_operation(self, op):\n",
    "        # Hey! No peeking!\n",
    "        q = op.qubits[0]\n",
    "        v = hashlib.sha256(str(q).encode()).digest()[0] / 256\n",
    "        yield cirq.depolarize(v).on(q)\n",
    "        yield op\n",
    "\n",
    "secret_noise_sampler = cirq.DensityMatrixSimulator(noise=SecretNoiseModel())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "801ef0cd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "qubit cirq.GridQubit(1, 5)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/rimuru/.local/lib/python3.8/site-packages/cirq/experiments/qubit_characterizations.py:99: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "  fig.show()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Rabi Angle (Radian)', ylabel='Excited State Probability'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "q = cirq_google.Sycamore.qubits[3]\n",
    "print('qubit', repr(q))\n",
    "cirq.experiments.rabi_oscillations(\n",
    "    sampler=secret_noise_sampler,\n",
    "    qubit=q\n",
    ").plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f2a1fb34",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}