{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": { 
    "license":"MIT-0"
    },
   "source": [
    "# LICENSE\n",
    "\n",
    "Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.\n",
    "SPDX-License-Identifier: MIT-0\n",
    "\n",
    "Permission is hereby granted, free of charge, to any person obtaining a copy of this\n",
    "software and associated documentation files (the 'Software'), to deal in the Software\n",
    "without restriction, including without limitation the rights to use, copy, modify,\n",
    "merge, publish, distribute, sublicense, and/or sell copies of the Software, and to\n",
    "permit persons to whom the Software is furnished to do so.\n",
    "\n",
    "THE SOFTWARE IS PROVIDED 'AS IS', WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED,\n",
    "INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A\n",
    "PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT\n",
    "HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION\n",
    "OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\n",
    "SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Getting started with Amazon Braket\n",
    "\n",
    "In this hello-world tutorial we prepare a maximally entangled Bell state between two qubits. We then run our circuit on a local simulator and obtain the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# general imports\n",
    "import matplotlib.pyplot as plt\n",
    "# magic word for producing visualizations in notebook\n",
    "%matplotlib inline\n",
    "import string\n",
    "import time\n",
    "import numpy as np\n",
    "\n",
    "# AWS imports: Import Braket SDK modules\n",
    "from braket.circuits import Circuit, Gate, Observable, Noise\n",
    "from braket.devices import LocalSimulator\n",
    "from braket.aws import AwsDevice, AwsQuantumTask"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build a circuit\n",
    "\n",
    "Let's build a Bell state with two qubits. By calling `Circuit()` we create an empty circuit, and we can just add gates to the circuit. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# build a Bell state with two qubits. Here 'cnot(control=0, target=1)' can be simplified as 'cnot(0,1)'\n",
    "bell = Circuit().h(0).cnot(control=0, target=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Submit the circuit to the local simulator and obtain the results\n",
    "\n",
    "Here we submit our circuit to the local simulator and obtain the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set up device\n",
    "device = LocalSimulator()\n",
    "\n",
    "# run circuit\n",
    "result = device.run(bell, shots=1000).result()\n",
    "# get measurement shots\n",
    "counts = result.measurement_counts\n",
    "# print counts\n",
    "print(counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plot using Counter\n",
    "plt.bar(counts.keys(), counts.values());\n",
    "plt.xlabel('bitstrings');\n",
    "plt.ylabel('counts');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Running larger circuits on managed simulators"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Apart from the local simulator, you can also run your circuit on a managed simulator. This approach adds some latency overhead, but is beneficial for larger circuits by leveraging the optimized cloud hardware infrastructure. Moreover, all your results will be stored reliably in S3. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__NOTE__: If you are working with the local simulator ```LocalSimulator()``` you do not need to specify any S3 location. However, if you are using a managed device or any QPU devices you need to specify the S3 location where your results will be stored. Remember that bucket names for Amazon Braket always begin with `\"amazon-braket-\"`. In this case, you must replace the API call ```device.run(circuit, ...)``` below with ```device.run(circuit, s3_folder, ...)```, where `s3_folder = (my_bucket, my_prefix)`. If you don't specify the S3 location, default S3 folder, where all inputs and outputs for your tasks are saved, follows the convention `amazon-braket-<region>-<account number>`. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prepare a GHZ State"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# function to build a GHZ state\n",
    "def ghz_circuit(n_qubits):\n",
    "    \"\"\"\n",
    "    function to return a GHZ circuit ansatz\n",
    "    input: number of qubits\n",
    "    \"\"\"\n",
    "\n",
    "    # instantiate circuit object\n",
    "    circuit = Circuit()\n",
    "    \n",
    "    # add Hadamard gate on first qubit\n",
    "    circuit.h(0)\n",
    "\n",
    "    # apply series of CNOT gates\n",
    "    for ii in range(0, n_qubits-1):\n",
    "        circuit.cnot(control=ii, target=ii+1)\n",
    "\n",
    "    return circuit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The GHZ state is a quantum superposition of all subsystems being in state 0 with all of them being in state 1 (as often discussed in the famous Gedanken experiment of a cat being dead and alive at the same time). The GHZ state is a maximally entangled quantum state. \n",
    "\n",
    "To prepare this state, build and run the following circuit using a single-qubit Hadamard gate (denoted as H) acting on the first qubit followed by a series of two-qubit CNOT gates: "
   ]
  },
  {
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    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div>\n",
    "<img src=\"attachment:image.png\" width=\"400\"/>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Amazon Braket provides two managed simulators: SV1 and TN1. SV1 calculates and keeps track of the full state vector evolution, supporting simulations of circuits with up to 34 qubits. TN1 is a tensor-network simulator, where each gate in a circuit is represented as a tensor. Compared with SV1, TN1 can simulate a larger number of qubits for circuits with local gates or other special structure, but typically is slower for circuits with long-range or all-to-all gate structure."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### State Vector Simulator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set up the managed simulator SV1\n",
    "device = AwsDevice(\"arn:aws:braket:::device/quantum-simulator/amazon/sv1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define a 15-qubit GHZ circuit\n",
    "n_qubits = 15\n",
    "ghz = ghz_circuit(n_qubits)\n",
    "\n",
    "# run GHZ circuit on SV1\n",
    "result = device.run(ghz, shots=1000).result()\n",
    "counts = result.measurement_counts\n",
    "print(counts)\n",
    "\n",
    "# plot using Counter\n",
    "plt.bar(counts.keys(), counts.values());\n",
    "plt.xlabel('bitstrings');\n",
    "plt.ylabel('counts');  \n",
    "\n",
    "# print counts of all-zero-string\n",
    "print('Counts for all-zero bitstring:', counts['0'*n_qubits])\n",
    "# print counts of all-one-string\n",
    "print('Counts for all-one bitstring:', counts['1'*n_qubits])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__NOTE__: Use unique task ID to look up task details in AWS console."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print unique TASK ID (task = execution of individual circuit)\n",
    "task_id = result.task_metadata.id\n",
    "# recover other metadata information such as number of qubits\n",
    "n = result.task_metadata.deviceParameters.paradigmParameters.qubitCount\n",
    "print('Task ID:', task_id)\n",
    "print('Number of qubits:', n)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Running quantum circuits on QPU devices"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section we show how to run circuits on QPU devices. We can run our circuit on both the superconducting machine from Rigetti and the ion-trap machine provided by IonQ. As shown below, one can seamlessly swap between different devices without any modifications to the circuit definition, by just re-defining the device object. We also show how to recover results using the unique ARN associated with every task. This tool is useful in order to deal with potential delays if your quantum task sits in the queue for some time waiting for execution. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Quantum Hardware: Rigetti"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we submit the Bell pair circuit to the superconducting quantum chip provided by Rigetti. Depending on our position in the queue, we may have to wait for some time till our circuit is actually run. However, thanks to asynchronous execution, we can always come back and recover the results by providing the unique ID associated with every task. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set up device\n",
    "rigetti = AwsDevice(\"arn:aws:braket:us-west-1::device/qpu/rigetti/Aspen-M-1\")\n",
    "\n",
    "# create a clean circuit with no result type attached.\n",
    "#(This is because some result types are only supported when shots=0)\n",
    "bell = Circuit().h(0).cnot(0, 1)  \n",
    "\n",
    "# add the Z \\otimes Z expectation value\n",
    "bell.expectation(Observable.Z() @ Observable.Z(), target=[0,1])\n",
    "\n",
    "# run circuit \n",
    "rigetti_task = rigetti.run(bell, shots=1000)\n",
    "\n",
    "# get id and status of submitted task\n",
    "rigetti_task_id = rigetti_task.id\n",
    "rigetti_status = rigetti_task.state()\n",
    "# print('ID of task:', rigetti_task_id)\n",
    "print('Status of task:', rigetti_status)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The task is submitted and we can regularly (or irregularly) check the status of this task by executing the following cell. You may easily build logic around this query to wait for this task to complete before your code proceeds. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print status\n",
    "status = rigetti_task.state()\n",
    "print('Status of (reconstructed) task:', status)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now search in the console for the Rigetti task in us-west-1 Region. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Quantum Hardware: IonQ"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we submit our example Bell state circuit to IonQ. To this end, we set the device as AwsDevice(\"arn:aws:braket:::device/qpu/ionq/ionQdevice\"). This task may not readily be executed but enter a queue for this specific machine. While we can interrupt our kernel (and work on something else), we can always recover our results using the unique ID of this task. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setup yourself the same circuit using the IonQ device,\n",
    "# specifying the device Amazon Resource Name and number of shots.\n",
    "\n",
    "# setup device\n",
    "ionq = AwsDevice(\"INSERT_ARN_HERE\")\n",
    "\n",
    "# run circuit\n",
    "ionq_task = ionq.run(bell, shots=INSERT_N_SHOTS_HERE)\n",
    "\n",
    "# get id and status of submitted task\n",
    "ionq_task_id = ionq_task.id\n",
    "ionq_status = ionq_task.state()\n",
    "# print('ID of task:', ionq_task_id)\n",
    "print('Status of task:', ionq_status)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print status\n",
    "status = ionq_task.state()\n",
    "print('Status of (reconstructed) task:', status)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task Recovery"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By simply grabbing the unique task ID associated with the quantum tasks we have submitted above, we can recover this task at any point in time and (once the status is completed) visualize and analyze the corresponding results. Note that apart from other metadata, you can retrieve the compiled circuit that was actually run on the Rigetti device. More information about the compiling process can be found [here](https://pyquil-docs.rigetti.com/en/v2.22.0/compiler.html#partial). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Rigetti"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# recover task\n",
    "task_load = AwsQuantumTask(arn=rigetti_task_id)\n",
    "\n",
    "# print status\n",
    "status = task_load.state()\n",
    "print('Status of (reconstructed) task:', status)\n",
    "print('\\n')\n",
    "# wait for job to complete\n",
    "# terminal_states = ['COMPLETED', 'FAILED', 'CANCELLED']\n",
    "if status == 'COMPLETED':\n",
    "    # get results\n",
    "    rigetti_results = task_load.result()\n",
    "    # print(rigetti_results)\n",
    "    \n",
    "    # get all metadata of submitted task\n",
    "    metadata = task_load.metadata()\n",
    "    # example for metadata\n",
    "    shots = metadata['shots']\n",
    "    machine = metadata['deviceArn']\n",
    "    # print example metadata\n",
    "    print(\"{} shots taken on machine {}.\\n\".format(shots, machine))\n",
    "    \n",
    "    # get the compiled circuit\n",
    "    print(\"The compiled circuit is:\\n\", rigetti_results.additional_metadata.rigettiMetadata.compiledProgram)\n",
    "    \n",
    "    # get measurement counts\n",
    "    rigetti_counts = rigetti_results.measurement_counts\n",
    "    print('Measurement counts:', rigetti_counts)\n",
    "\n",
    "    # plot results: see effects of noise\n",
    "    plt.bar(rigetti_counts.keys(), rigetti_counts.values())\n",
    "    plt.xlabel('bitstrings')\n",
    "    plt.ylabel('counts')\n",
    "    plt.tight_layout()\n",
    "    plt.savefig('rigetti.png', dpi=700)\n",
    "    \n",
    "elif status in ['FAILED', 'CANCELLED']:\n",
    "    # print terminal message \n",
    "    print('Your task is in terminal status, but has not completed.')\n",
    "\n",
    "else:\n",
    "    # print current status\n",
    "    print('Sorry, your task is still being processed and has not been finalized yet.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### IonQ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# recover task\n",
    "task_load = AwsQuantumTask(arn=ionq_task_id)\n",
    "\n",
    "# print status\n",
    "status = task_load.state()\n",
    "print('Status of (reconstructed) task:', status)\n",
    "\n",
    "# wait for job to complete\n",
    "# terminal_states = ['COMPLETED', 'FAILED', 'CANCELLED']\n",
    "if status == 'COMPLETED':\n",
    "    # get results\n",
    "    results = task_load.result()\n",
    "    # print(ionq_results)\n",
    "    \n",
    "    # get all metadata of submitted task\n",
    "    metadata = task_load.metadata()\n",
    "    # example for metadata\n",
    "    shots = metadata['shots']\n",
    "    machine = metadata['deviceArn']\n",
    "    # print example metadata\n",
    "    print(\"{} shots taken on machine {}.\".format(shots, machine))\n",
    "    \n",
    "    # get measurement counts\n",
    "    counts = results.measurement_counts\n",
    "    print('Measurement counts:', counts)\n",
    "\n",
    "    # plot results: see effects of noise\n",
    "    plt.bar(counts.keys(), counts.values())\n",
    "    plt.xlabel('bitstrings')\n",
    "    plt.ylabel('counts')\n",
    "    plt.tight_layout()\n",
    "    plt.savefig('bell_ionq.png', dpi=700)\n",
    "    \n",
    "elif status in ['FAILED', 'CANCELLED']:\n",
    "    # print terminal message \n",
    "    print('Your task is in terminal status, but has not completed.')\n",
    "\n",
    "else:\n",
    "    # print current status\n",
    "    print('Sorry, your task is still being processed and has not been finalized yet.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Taking it further"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check gate compatibility across devices, so you can write circuits that can run without any modification across devices."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print all (the usual suspects) available gates currently available within SDK\n",
    "gate_set = [attr for attr in dir(Gate) if attr[0] in string.ascii_uppercase]\n",
    "print('Gate set supported by SDK:\\n', gate_set)\n",
    "print('\\n') \n",
    "\n",
    "# the Rigetti device\n",
    "device = AwsDevice(\"arn:aws:braket:us-west-1::device/qpu/rigetti/Aspen-M-1\")\n",
    "supported_gates = device.properties.action['braket.ir.jaqcd.program'].supportedOperations\n",
    "# print the supported gate set\n",
    "print('Gate set supported by the Rigetti device:\\n', supported_gates)\n",
    "print('\\n') \n",
    "\n",
    "# the IonQ device\n",
    "device = AwsDevice(\"arn:aws:braket:::device/qpu/ionq/ionQdevice\")\n",
    "supported_gates = device.properties.action['braket.ir.jaqcd.program'].supportedOperations\n",
    "# print the supported gate set\n",
    "print('Gate set supported by the IonQ device:\\n', supported_gates)\n",
    "print('\\n') \n",
    "\n",
    "# the Oxford device\n",
    "device = AwsDevice(\"arn:aws:braket:eu-west-2::device/qpu/oqc/Lucy\")\n",
    "supported_gates = device.properties.action['braket.ir.jaqcd.program'].supportedOperations\n",
    "# print the supported gate set\n",
    "print('Gate set supported by the Oxford device:\\n', supported_gates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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