{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f4b29f63",
   "metadata": {},
   "source": [
    "<a href=\"figures/Banner.jpeg\">\n",
    "    <img src=\"figures/Banner.jpeg\" alt=\"image0\" width=\"100%\">\n",
    "</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a62f00a8",
   "metadata": {},
   "source": [
    "# Measuring $g^2$-correlation using QTM\n",
    "Second-order photon correlation measurements $(g^2)$ are fundamental in quantum optics. Amongst many other applications,\n",
    "it can provide a clear signature of single-photon emission and enables the validation of quantum light sources.\n",
    "Color centers are for instance a typical example of such single photon emitter.\n",
    "\n",
    "<div class=\"admonition tip\">\n",
    "    <p>The second-order correlation function $g^2(\\tau)$ can be computed out of a single detection mode -- one would\n",
    "    generally call it *local* correlations or *autocorrelation* -- or between two detection modes -- in that case one\n",
    "    would typically call it *crossed* or sometimes *non-local* correlations. The latter case is for instance considered\n",
    "    in the Hanbury Brown and Twiss (HBT) interferometry experiment. We invite the interested reader to refer to the\n",
    "    following references for a more in-depth discussion of the HBT effect and correlation measurements:</p>\n",
    "    <ul>\n",
    "        <li><a href=\"https://doi.org/10.1038/177027a0\">Brown, R.H., Twiss, R.Q., 1956.</a> <i>Correlation between Photons in two Coherent Beams of Light.</i> Nature 177, 27–29.</li>\n",
    "        <li><a href=\"https://doi.org/10.1209/0295-5075/1/4/004\">Grangier, P., Roger, G., Aspect, A., 1986.</a> <i>Experimental Evidence for a Photon Anticorrelation Effect on a Beam Splitter: A New Light on Single-Photon Interferences.</i> Europhys. Lett. 1, 173–179.</li>\n",
    "        <li><a href=\"https://doi.org/10.1017/CBO9781139644105\">Mandel, L., Wolf, E., 1995.</a> <i>Optical Coherence and Quantum Optics, 1st ed.</i> Cambridge University Press.</li>\n",
    "        <li><a href=\"https://doi.org/10.1017/CBO9780511791239\">Gerry, C., Knight, P., 2004.</a> <i>Introductory Quantum Optics, 1st ed.</i> Cambridge University Press.</li>\n",
    "    </ul>\n",
    "</div>\n",
    "\n",
    "Correlation functions (of any order) are characteristic of the light source${}^\\dagger$ and provides valuable\n",
    "experimental information about it. For example, in photonics, a dip below $0.5$ of the autocorrelation function at\n",
    "zero time delay indicates that single photons are detected. In the context of color centers, this allows to subsequently\n",
    "conclude that these photons originate from a single color center${}^\\ddagger$.\n",
    "\n",
    "${}^\\dagger$: *also applicable to other types of interferometric resources, such as electrons or atoms.*\n",
    "${}^\\ddagger$: Shuo Li, Wenchao Li, Vladislav V. Yakovlev, Allison Kealy, and Andrew D. Greentree, \"En route to nanoscopic quantum optical imaging: counting emitters with photon-number-resolving detectors,\" Opt. Express 30, 12495-12509 (2022)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5241dca9",
   "metadata": {},
   "source": [
    "## Description of the example experiment\n",
    "\n",
    "In this demo, we use the Qubit Timetag Module (QTM) to perform a $g^2$ measurement.\n",
    "Two input channels of the QTM are used to record the detection events, enabling correlation analysis.\n",
    "A simplified schematic of a possible corresponding setup is shown below:\n",
    "\n",
    "<center><img src=\"./figures/fig_exp_grangier_1986.png\"/></center>\n",
    "\n",
    "A coherent laser beam (in red) is exciting a single photon emitter, for instance a color center in diamond (CC).\n",
    "The emitted photons (following the blue path) are then split by a beam splitter (BS) and detected by two single photon\n",
    "detectors (PD) and timetagged by the QTM. A single photon cannot be detected by both detectors detectors monitoring\n",
    "each output port of the beam splitter, consequently, the crossed correlation function $g^2(\\tau)$ between both\n",
    "channels is expected to be $0$ at $\\tau=0$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8de281e3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-07T16:43:20.066642Z",
     "iopub.status.busy": "2025-05-07T16:43:20.066473Z",
     "iopub.status.idle": "2025-05-07T16:43:21.824026Z",
     "shell.execute_reply": "2025-05-07T16:43:21.823272Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from data_analysis import G2Correlation\n",
    "\n",
    "from quantify_core.data import handling as dh\n",
    "from quantify_scheduler import QuantumDevice, Schedule, SerialCompiler\n",
    "from quantify_scheduler.enums import BinMode, TimeRef\n",
    "from quantify_scheduler.operations import TimetagTrace\n",
    "\n",
    "from utils import initialize_hardware, run_schedule"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "160efa44",
   "metadata": {},
   "source": [
    "## Setting up the hardware for the measurement\n",
    "`!qblox-pnp list`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "43e61360",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-07T16:43:21.827873Z",
     "iopub.status.busy": "2025-05-07T16:43:21.826993Z",
     "iopub.status.idle": "2025-05-07T16:43:21.831423Z",
     "shell.execute_reply": "2025-05-07T16:43:21.830893Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data will be saved in:\n",
      "/root/quantify-data\n"
     ]
    }
   ],
   "source": [
    "# Setup quantify output directory\n",
    "dh.set_datadir(dh.default_datadir())\n",
    "# Load and define the Hardware configuration\n",
    "device_path = \"devices/nv_center1q.json\"\n",
    "config_path = \"configs/nv_center_qrm.json\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36eb3a16",
   "metadata": {},
   "source": [
    "## Quantum device settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e3ae0fc4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-07T16:43:21.833052Z",
     "iopub.status.busy": "2025-05-07T16:43:21.832895Z",
     "iopub.status.idle": "2025-05-07T16:43:21.839977Z",
     "shell.execute_reply": "2025-05-07T16:43:21.839490Z"
    }
   },
   "outputs": [],
   "source": [
    "quantum_device = QuantumDevice.from_json_file(device_path)\n",
    "quantum_device.hardware_config.load_from_json_file(config_path)\n",
    "\n",
    "qubit = quantum_device.get_element(\"qe0\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "014c6f0a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-07T16:43:21.841489Z",
     "iopub.status.busy": "2025-05-07T16:43:21.841327Z",
     "iopub.status.idle": "2025-05-07T16:43:22.482400Z",
     "shell.execute_reply": "2025-05-07T16:43:22.481730Z"
    }
   },
   "outputs": [],
   "source": [
    "cluster_ip = None  # Fill in the ip to run this tutorial on hardware,\n",
    "measctrl, ic, cluster = initialize_hardware(quantum_device, cluster_ip)\n",
    "compiler = SerialCompiler(name=\"compiler\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e3ff47a",
   "metadata": {},
   "source": [
    "## Schedule for $g^{(2)}$-correlation using `TimetagTrace` acquisition\n",
    "\n",
    "To time-stamp the incoming photons on any channel of QTM, we use `TimetagTrace` acquisition in Quantify, where it can record a stream of timetags within a defined acquisition window (`duration`) defined as how long photon arrivals are acquired.\n",
    "Every time there is a rising edge detected, a timetag is recorded.\n",
    "\n",
    "Here, we add the `TimetagTrace` in channel 0 and channel 1, with each acquisition window of $50~\\mu s$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5ceb5c5f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-07T16:43:22.485015Z",
     "iopub.status.busy": "2025-05-07T16:43:22.484852Z",
     "iopub.status.idle": "2025-05-07T16:43:22.492210Z",
     "shell.execute_reply": "2025-05-07T16:43:22.491741Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'name': '3dc1ac96-e709-4bc0-a016-25b535b464ac', 'operation_id': '1664845176180229549', 'timing_constraints': [{'rel_time': 0, 'ref_schedulable': None, 'ref_pt_new': None, 'ref_pt': 'start'}], 'label': '3dc1ac96-e709-4bc0-a016-25b535b464ac'}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ACQUISITION_TIME = 50e-6  # 50 us\n",
    "\n",
    "main_schedule = Schedule(\"g2_schedule\")\n",
    "\n",
    "# Adding TimetagTrace on channel 0\n",
    "main_schedule.add(\n",
    "    TimetagTrace(\n",
    "        duration=ACQUISITION_TIME,\n",
    "        port=\"qtm0:in\",\n",
    "        clock=\"digital\",\n",
    "        acq_channel=0,\n",
    "        bin_mode=BinMode.APPEND,\n",
    "        time_ref=TimeRef.START,\n",
    "    ),\n",
    "    ref_pt=\"start\",\n",
    ")\n",
    "\n",
    "# Adding TimetagTrace on channel 1\n",
    "main_schedule.add(\n",
    "    TimetagTrace(\n",
    "        duration=ACQUISITION_TIME,\n",
    "        port=\"qtm1:in\",\n",
    "        clock=\"digital\",\n",
    "        acq_channel=1,\n",
    "        bin_mode=BinMode.APPEND,\n",
    "        time_ref=TimeRef.START,\n",
    "    ),\n",
    "    ref_pt=\"start\",\n",
    "    rel_time=0,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31e02ed2",
   "metadata": {},
   "source": [
    "We compile the schedule."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a9e8dcaa",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-07T16:43:22.493709Z",
     "iopub.status.busy": "2025-05-07T16:43:22.493560Z",
     "iopub.status.idle": "2025-05-07T16:43:22.506904Z",
     "shell.execute_reply": "2025-05-07T16:43:22.506414Z"
    }
   },
   "outputs": [],
   "source": [
    "compiled_schedule = compiler.compile(\n",
    "    schedule=main_schedule,\n",
    "    config=quantum_device.generate_compilation_config(),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1cb3c4ae",
   "metadata": {},
   "source": [
    "And we execute it on the hardware.\n",
    "To illustrate the behavior of a single color center, we present a set of simulated time-tags that replicate the expected $g^{(2)}$ correlation signature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3a010e18",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-07T16:43:22.508865Z",
     "iopub.status.busy": "2025-05-07T16:43:22.508709Z",
     "iopub.status.idle": "2025-05-07T16:43:22.515021Z",
     "shell.execute_reply": "2025-05-07T16:43:22.514416Z"
    }
   },
   "outputs": [],
   "source": [
    "if cluster_ip is not None:  # if running on hardware\n",
    "    acquisition = run_schedule(compiled_schedule, quantum_device)\n",
    "    time_tags1 = acquisition.isel()[0].values[0][0]\n",
    "    time_tags2 = acquisition.isel()[1].values[0][0]\n",
    "else:\n",
    "    # in case of dummy cluster, we use the simulated time tags for single photon emission\n",
    "    time_tags1, time_tags2 = np.load(\"simulated_timetags.npz\").values()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "331df201",
   "metadata": {},
   "source": [
    "### Measuring $g^{(2)} (\\tau)$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "947a99cb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-07T16:43:22.517122Z",
     "iopub.status.busy": "2025-05-07T16:43:22.516686Z",
     "iopub.status.idle": "2025-05-07T16:43:22.755216Z",
     "shell.execute_reply": "2025-05-07T16:43:22.754743Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x621 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate g2 for the obtained time-tags\n",
    "g2_corr_experiment = G2Correlation(time_tags1, time_tags2, 10)\n",
    "g2_corr_experiment.plot_data(\"Experiment\", plot_fit=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d173131",
   "metadata": {},
   "source": [
    "In the example above we exhibit the second-order _cross_ correlations between two simulated photo-detection events,\n",
    "as might be observed at the output of a beam splitter fed by a single-photon source (e.g., a color center in diamond).\n",
    "This experiment has been first described and performed in the following reference:\n",
    "[Grangier, P., Roger, G., Aspect, A., 1986.](https://doi.org/10.1209/0295-5075/1/4/004).\n",
    "Therefore, in the observed plot above, $g^{(2)}(\\tau=0)=0$, verifying the presence of a single quantum emitter.\n",
    "\n",
    "Of course, the technical implementation of Quantify and Qblox hardware shown in this application example is also suitable\n",
    "for any other kind of experiment involving the measurement of the second-order correlation function, out of two\n",
    "separated detection channels. For instance, the [Hanbury Brown and Twiss experiment is another famous example of such\n",
    "setup.](https://doi.org/10.1038/1781046a0)"
   ]
  }
 ],
 "metadata": {
  "files_to_bundle_in_zip_file": [
   "utils.py",
   "data_analysis.py",
   "devices/nv_center1q.json",
   "configs/nv_center_qrm.json",
   "figures/Banner.jpeg",
   "figures/fig_exp_grangier_1986.png",
   "simulated_timetags.npz"
  ],
  "jupytext": {
   "notebook_metadata_filter": "files_to_bundle_in_zip_file"
  },
  "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": "3.9.20"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}