examples/analog_hamiltonian_simulation/03_Parallel_tasks_on_Aquila.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "43ae51e2",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Parallel quantum tasks on Aquila\n",
    "\n",
    "For quantum tasks with few qubits in the register, we can use parallel execution that makes use of full area allowed by the QPU. In this tutorial we go through the previously explored checkerboard preparation but now we take advantage of the full area. \n",
    "\n",
    "We will break up the register into __batches__ which will run in parallel."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8af10669-31a2-4eef-993c-6fb2f29c698f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-29T21:44:54.003722Z",
     "start_time": "2023-08-29T21:44:53.647579Z"
    }
   },
   "outputs": [],
   "source": [
    "# Use Braket SDK Cost Tracking to estimate the cost to run this example\n",
    "from braket.tracking import Tracker\n",
    "\n",
    "tracker = Tracker().start()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f31e9ade-6b2d-4727-bd4e-cc6a8bbb302e",
   "metadata": {},
   "source": [
    "## Defining batches\n",
    "\n",
    "First, we will define a _single_ batch containing a 3x3 square grid of atoms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e5598963",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-29T21:44:54.010669Z",
     "start_time": "2023-08-29T21:44:53.705279Z"
    }
   },
   "outputs": [],
   "source": [
    "from braket.ahs.analog_hamiltonian_simulation import AnalogHamiltonianSimulation\n",
    "from braket.ahs.atom_arrangement import AtomArrangement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4a9c026d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-29T21:44:54.010852Z",
     "start_time": "2023-08-29T21:44:53.957408Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "# distance between atoms\n",
    "distance = 6.7e-6\n",
    "\n",
    "# number of atoms in a batch\n",
    "Lx = 3\n",
    "Ly = 3\n",
    "\n",
    "register = AtomArrangement()\n",
    "\n",
    "for ix in range(Lx):\n",
    "    for iy in range(Ly):\n",
    "        pos = (ix * distance, iy * distance)\n",
    "        register.add(pos)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa2efd6c",
   "metadata": {},
   "source": [
    "## Bounding box\n",
    "\n",
    "Next, we define a bounding box by calculating its width and height of the batch. This will prove useful in setting up multiple batches in parallel."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "db0da94b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-29T21:44:54.010940Z",
     "start_time": "2023-08-29T21:44:53.963882Z"
    }
   },
   "outputs": [],
   "source": [
    "x_min = min(*[site.coordinate[0] for site in register])\n",
    "x_max = max(*[site.coordinate[0] for site in register])\n",
    "y_min = min(*[site.coordinate[1] for site in register])\n",
    "y_max = max(*[site.coordinate[1] for site in register])\n",
    "\n",
    "single_problem_width = x_max - x_min\n",
    "single_problem_height = y_max - y_min"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08ff59e6",
   "metadata": {},
   "source": [
    "### Calculating batch placement\n",
    "\n",
    "To prevent entanglement via the Rydberg interaction between batches, we need to place them far apart. Each batch needs an area occupied by its bounding box plus a padding that ensure proper separation.\n",
    "\n",
    "We pick the distance between the batches to be $3 \\times d$, where $d$ is the distance between neighboring atoms in a single batch. This means the ratio between intra- and inter-batch interaction strengths (using the van der Waals formula, $V = C_6/d^6$) is at most $\\frac{1}{3^6} \\approx 0.001372$.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "36a82617",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-29T21:44:56.505252Z",
     "start_time": "2023-08-29T21:44:53.973138Z"
    }
   },
   "outputs": [],
   "source": [
    "from braket.ahs.atom_arrangement import AtomArrangement\n",
    "from braket.aws import AwsDevice\n",
    "from braket.devices import Devices\n",
    "\n",
    "qpu = AwsDevice(Devices.QuEra.Aquila)\n",
    "\n",
    "# get values from device capabilities\n",
    "field_of_view_width = qpu.properties.paradigm.lattice.area.width\n",
    "field_of_view_height = qpu.properties.paradigm.lattice.area.height\n",
    "n_site_max = qpu.properties.paradigm.lattice.geometry.numberSitesMax\n",
    "\n",
    "# set distance between batches to be 3 x d\n",
    "interproblem_distance = 3 * distance\n",
    "\n",
    "# setting up a grid of problems filling the total area\n",
    "n_width = int(float(field_of_view_width) // (single_problem_width + interproblem_distance))\n",
    "n_height = int(float(field_of_view_height) // (single_problem_height + interproblem_distance))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f83106e",
   "metadata": {},
   "source": [
    "## Generating registers for all batches\n",
    "\n",
    "We now loop create the total set of registers that will run on the QPU. \n",
    "\n",
    "We will keep track of which set of registers (by atom number) belongs to which batch using a dictionary called `batch_mapping`.\n",
    "\n",
    "We stop generating batches once we have reached the maximum number of sites allowed by the QPU. In this case we check to make sure we can fit the necessary number of atoms into a batch in case the number of atoms per batch is not a factor of `n_site_max`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ab083fd7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-29T21:44:56.514012Z",
     "start_time": "2023-08-29T21:44:56.510717Z"
    }
   },
   "outputs": [],
   "source": [
    "batch_mapping = dict()\n",
    "parallel_register = AtomArrangement()\n",
    "\n",
    "atom_number = 0  # counting number of atoms added\n",
    "\n",
    "for ix in range(n_width):\n",
    "    x_shift = ix * (single_problem_width + interproblem_distance)\n",
    "\n",
    "    for iy in range(n_height):\n",
    "        y_shift = iy * (single_problem_height + interproblem_distance)\n",
    "\n",
    "        # reached the maximum number of batches possible given n_site_max\n",
    "        if atom_number + len(register) > n_site_max:\n",
    "            break\n",
    "\n",
    "        atoms = []\n",
    "        for site in register:\n",
    "            new_coordinate = (x_shift + site.coordinate[0], y_shift + site.coordinate[1])\n",
    "            parallel_register.add(new_coordinate, site.site_type)\n",
    "\n",
    "            atoms.append(atom_number)\n",
    "\n",
    "            atom_number += 1\n",
    "\n",
    "        batch_mapping[(ix, iy)] = atoms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9fab538e-e7fb-4d24-8458-d8de0a38446c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-29T21:44:56.526160Z",
     "start_time": "2023-08-29T21:44:56.521445Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{(0, 0): [0, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       " (0, 1): [9, 10, 11, 12, 13, 14, 15, 16, 17],\n",
       " (1, 0): [18, 19, 20, 21, 22, 23, 24, 25, 26],\n",
       " (1, 1): [27, 28, 29, 30, 31, 32, 33, 34, 35]}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Key indicates the position of the batch itself on a unitless grid,\n",
    "# with dictionary value being the atom number\n",
    "batch_mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b26b7e0a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-29T21:44:57.544699Z",
     "start_time": "2023-08-29T21:44:56.528070Z"
    }
   },
   "outputs": [
    {
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",
      "text/plain": [
       "<Figure size 700x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# [Optional] We visually inspect the register\n",
    "from ahs_utils import show_register\n",
    "\n",
    "show_register(register)\n",
    "show_register(parallel_register)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e30df2e-45fc-4a41-8000-4ee88a3a1519",
   "metadata": {},
   "source": [
    "## AHS program for creating the checkerboard phase"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46bae3ef-1c23-43df-b153-a527d64eea3d",
   "metadata": {},
   "source": [
    "We use the same values and timings for the Amplitude, Phase, and Detuning from the [Ordered phases in Rydberg Systems](./02_Ordered_phases_in_Rydberg_systems.ipynb) example "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5a31ab21",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-29T21:44:57.552189Z",
     "start_time": "2023-08-29T21:44:57.549024Z"
    }
   },
   "outputs": [],
   "source": [
    "from braket.ahs.driving_field import DrivingField\n",
    "\n",
    "time_points = [0, 2.5e-7, 2.75e-6, 3e-6]  # s\n",
    "\n",
    "amplitude_min = 0\n",
    "amplitude_max = 1.5708e7  # rad / s\n",
    "\n",
    "detuning_min = -5.49778714e7  # rad / s\n",
    "detuning_max = 5.49778714e7  # rad / s\n",
    "\n",
    "amplitude_values = [amplitude_min, amplitude_max, amplitude_max, amplitude_min]\n",
    "detuning_values = [detuning_min, detuning_min, detuning_max, detuning_max]\n",
    "phase_values = [0, 0, 0, 0]\n",
    "\n",
    "drive = DrivingField.from_lists(time_points, amplitude_values, detuning_values, phase_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9993d59b-7a20-4098-b99c-dcf4e3d1b0ad",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-29T21:44:58.183046Z",
     "start_time": "2023-08-29T21:44:57.558503Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x700 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from ahs_utils import show_global_drive\n",
    "\n",
    "# [Optional] We visually inspect the time series\n",
    "\n",
    "show_global_drive(drive);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8d1fc59-54e2-4518-a65b-ddd2fdcb1f0e",
   "metadata": {},
   "source": [
    "We now construct the AHS program to be run. Note that we create two programs and execute them to allow us to compare the accuracy of our results, with `parallel_ahs_program` being run on Aquila and `ahs_program` just being simulated with the local simulator. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "20d44c2e-cdc5-44e3-9592-f934999cf914",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-29T21:44:58.183221Z",
     "start_time": "2023-08-29T21:44:58.147637Z"
    }
   },
   "outputs": [],
   "source": [
    "parallel_ahs_program = AnalogHamiltonianSimulation(register=parallel_register, hamiltonian=drive)\n",
    "\n",
    "ahs_program = AnalogHamiltonianSimulation(register=register, hamiltonian=drive)\n",
    "\n",
    "ahs_program = ahs_program.discretize(qpu)\n",
    "parallel_ahs_program = parallel_ahs_program.discretize(qpu)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6c6e1ed-c726-4d46-b5f3-749f9eb07ec0",
   "metadata": {},
   "source": [
    "## Run on simulator"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69aad79a-a494-463f-911f-2e7fc71d0ff7",
   "metadata": {},
   "source": [
    "First, we run a non-parallelized register on the local simulator, and sample 400 shots. Below we also explicitly specified the values of `steps`, which are the number of time steps in the simulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "88cdddd1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-29T21:45:00.337571Z",
     "start_time": "2023-08-29T21:44:58.155252Z"
    }
   },
   "outputs": [],
   "source": [
    "from braket.devices import LocalSimulator\n",
    "\n",
    "sim = LocalSimulator(\"braket_ahs\")\n",
    "\n",
    "sim_result = sim.run(ahs_program, shots=400, steps=100).result()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8d716b5-acbb-48a4-909d-4d5470d246d9",
   "metadata": {},
   "source": [
    "We compute the average Rydberg density on the 9 sites."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "025f3a2f-d0c0-493e-b6d6-7fd15b7ee348",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-29T21:45:00.337773Z",
     "start_time": "2023-08-29T21:45:00.315369Z"
    }
   },
   "outputs": [],
   "source": [
    "sim_density = sim_result.get_avg_density()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b27fd6ec-c677-4c18-aee3-533f6faeb24d",
   "metadata": {},
   "source": [
    "## Run on Aquila\n",
    "\n",
    "Next, we run a the parallelized register on the QPU. This time, we only need to run 100 shots to get to the same statistical certainty, because we are running four batches in parallel in each shot."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "767ec35c-b9f2-4027-a2b6-ea10a3c40512",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "<b>Note: </b> Some atoms may be missing even if the shot was successful. We recommend comparing <code>pre_sequence</code> of each shot with the requested atom filling in the AHS program specification.  \n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "fc86d8ac-9ea0-4005-b156-e271782e3377",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-29T21:46:58.031426Z",
     "start_time": "2023-08-29T21:45:00.325955Z"
    }
   },
   "outputs": [],
   "source": [
    "# This cell submits the quantum task, waits for its completion, and gets the results.\n",
    "# To check the status of the quantum task, go to the Amazon Braket quantum tasks page at\n",
    "# https://us-east-1.console.aws.amazon.com/braket/home?region=us-east-1#/tasks\n",
    "\n",
    "qpu_result = qpu.run(parallel_ahs_program, shots=100).result()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5a799030",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-29T21:46:58.040126Z",
     "start_time": "2023-08-29T21:46:58.036334Z"
    }
   },
   "outputs": [],
   "source": [
    "# collecting QPU Data\n",
    "\n",
    "all_sequences = []\n",
    "for measurement in qpu_result.measurements:\n",
    "    # iterate over key and values\n",
    "    for (ix, iy), inds in batch_mapping.items():\n",
    "        batch_sequence = list(measurement.post_sequence[inds])\n",
    "        all_sequences.append(batch_sequence)\n",
    "\n",
    "all_rydberg = 1 - np.array(all_sequences)\n",
    "qpu_density = all_rydberg.mean(axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19f42800-41ee-4ca4-960c-2da6495d5777",
   "metadata": {},
   "source": [
    "After running the separate programs, we can analyze the results.\n",
    "\n",
    "We average the data across all the batches that ran in parallel across multiple shots, represented visually as a single batch versus the simulator result which just ran a single batch."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8f897f5b-2c9f-4bc7-9f71-b2ab227b1e9e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-29T21:46:58.863063Z",
     "start_time": "2023-08-29T21:46:58.148200Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from ahs_utils import plot_avg_density_2D\n",
    "\n",
    "# Compare results side-by-side\n",
    "fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(12, 5))\n",
    "\n",
    "# plot density of all batches that ran in parallel with geometry and averaged values of single batch\n",
    "\n",
    "plot_avg_density_2D(qpu_density, parallel_register, batch_mapping=batch_mapping, custom_axes=ax1)\n",
    "ax1.set_title(\"Averages of Densities across parallelized batches\")\n",
    "# plot density of simulator result\n",
    "plot_avg_density_2D(sim_density, register, custom_axes=ax2)\n",
    "ax2.set_title(\"Simulator Result\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c1e79d8-c9ff-4d5d-8b4f-3aa0e0b7d7cb",
   "metadata": {},
   "source": [
    "As we can see from the results, the simulated single batch result and the results from the batches running in parallel line up with what we expect for the checkerboard phase."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "5974573a-2a03-46bc-aad9-9a39fefb906a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-08-29T21:46:59.389363Z",
     "start_time": "2023-08-29T21:46:58.857811Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Quantum Task Summary\n",
      "{'arn:aws:braket:us-east-1::device/qpu/quera/Aquila': {'shots': 100, 'tasks': {'COMPLETED': 1}}}\n",
      "Note: Charges shown are estimates based on your Amazon Braket simulator and quantum processing unit (QPU) task usage. Estimated charges shown may differ from your actual charges. Estimated charges do not factor in any discounts or credits, and you may experience additional charges based on your use of other services such as Amazon Elastic Compute Cloud (Amazon EC2).\n",
      "Estimated cost to run this example: 1.30 USD\n"
     ]
    }
   ],
   "source": [
    "print(\"Quantum Task Summary\")\n",
    "print(tracker.quantum_tasks_statistics())\n",
    "print(\n",
    "    \"Note: Charges shown are estimates based on your Amazon Braket simulator and quantum processing unit (QPU) task usage. Estimated charges shown may differ from your actual charges. Estimated charges do not factor in any discounts or credits, and you may experience additional charges based on your use of other services such as Amazon Elastic Compute Cloud (Amazon EC2).\"\n",
    ")\n",
    "print(\n",
    "    f\"Estimated cost to run this example: {tracker.qpu_tasks_cost() + tracker.simulator_tasks_cost():.2f} USD\"\n",
    ")"
   ]
  }
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