{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "63d9235c", "metadata": { "execution": { "iopub.execute_input": "2023-09-26T17:43:25.738999Z", "iopub.status.busy": "2023-09-26T17:43:25.738732Z", "iopub.status.idle": "2023-09-26T17:43:26.900789Z", "shell.execute_reply": "2023-09-26T17:43:26.900061Z" } }, "outputs": [], "source": [ "from pathlib import Path\n", "\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import xarray as xr\n", "from rich import pretty\n", "\n", "from quantify_core.analysis.calibration import rotate_to_calibrated_axis\n", "from quantify_core.analysis.fitting_models import exp_decay_func\n", "from quantify_core.data import handling as dh\n", "from quantify_core.utilities import dataset_examples\n", "from quantify_core.utilities.dataset_examples import (\n", " mk_nested_mc_dataset,\n", " mk_shots_from_probabilities,\n", " mk_surface7_cyles_dataset,\n", ")\n", "from quantify_core.utilities.examples_support import (\n", " mk_iq_shots,\n", " mk_surface7_sched,\n", " round_trip_dataset,\n", ")\n", "from quantify_core.utilities.inspect_utils import display_source_code\n", "\n", "pretty.install()\n", "\n", "dh.set_datadir(Path.home() / \"quantify-data\") # change me!" ] }, { "cell_type": "code", "execution_count": 2, "id": "a41fe29a", "metadata": { "execution": { "iopub.execute_input": "2023-09-26T17:43:26.903583Z", "iopub.status.busy": "2023-09-26T17:43:26.903244Z", "iopub.status.idle": "2023-09-26T17:43:26.977200Z", "shell.execute_reply": "2023-09-26T17:43:26.976451Z" } }, "outputs": [ { "data": { "text/html": [ "
def mk_surface7_sched(num_cycles: int = 3):\n",
       "    """Generates a schedule with some of the feature of a Surface 7 experiment as\n",
       "    portrayed in Fig. 4b of :cite:`marques_logical_qubit_2021`.\n",
       "\n",
       "    Parameters\n",
       "    ----------\n",
       "    num_cycles\n",
       "        The number of times to repeat the main cycle.\n",
       "\n",
       "    Returns\n",
       "    -------\n",
       "    :\n",
       "        A schedule similar to a Surface 7 dance.\n",
       "    """\n",
       "\n",
       "    from quantify_scheduler import Schedule\n",
       "    from quantify_scheduler.operations.gate_library import CZ, Y90, Measure, Reset, X\n",
       "\n",
       "    sched = Schedule("S7 dance")\n",
       "\n",
       "    q_d1, q_d2, q_d3, q_d4 = [f"D{i}" for i in range(1, 5)]\n",
       "    q_a1, q_a2, q_a3 = [f"A{i}" for i in range(1, 4)]\n",
       "    all_qubits = q_d1, q_d2, q_d3, q_d4, q_a1, q_a2, q_a3\n",
       "\n",
       "    sched.add(Reset(*all_qubits))\n",
       "\n",
       "    for cycle in range(num_cycles):\n",
       "        sched.add(Y90(q_d1))\n",
       "        for qubit in [q_d2, q_d3, q_d4]:\n",
       "            sched.add(Y90(qubit), ref_pt="start", rel_time=0)\n",
       "        sched.add(Y90(q_a2), ref_pt="start", rel_time=0)\n",
       "\n",
       "        for qubit in [q_d2, q_d1, q_d4, q_d3]:\n",
       "            sched.add(CZ(qC=qubit, qT=q_a2))\n",
       "\n",
       "        sched.add(Y90(q_d1))\n",
       "        for qubit in [q_d2, q_d3, q_d4]:\n",
       "            sched.add(Y90(qubit), ref_pt="start", rel_time=0)\n",
       "        sched.add(Y90(q_a2), ref_pt="start", rel_time=0)\n",
       "\n",
       "        sched.add(Y90(q_a1), ref_pt="end", rel_time=0)\n",
       "        sched.add(Y90(q_a3), ref_pt="start", rel_time=0)\n",
       "\n",
       "        sched.add(CZ(qC=q_d1, qT=q_a1))\n",
       "        sched.add(CZ(qC=q_d2, qT=q_a3))\n",
       "        sched.add(CZ(qC=q_d3, qT=q_a1))\n",
       "        sched.add(CZ(qC=q_d4, qT=q_a3))\n",
       "\n",
       "        sched.add(Y90(q_a1), ref_pt="end", rel_time=0)\n",
       "        sched.add(Y90(q_a3), ref_pt="start", rel_time=0)\n",
       "\n",
       "        sched.add(Measure(q_a2, acq_index=cycle))\n",
       "        for qubit in (q_a1, q_a3):\n",
       "            sched.add(Measure(qubit, acq_index=cycle), ref_pt="start", rel_time=0)\n",
       "\n",
       "        for qubit in [q_d1, q_d2, q_d3, q_d4]:\n",
       "            sched.add(X(qubit), ref_pt="start", rel_time=0)\n",
       "\n",
       "    # final measurements\n",
       "\n",
       "    sched.add(Measure(*all_qubits[:4], acq_index=0), ref_pt="end", rel_time=0)\n",
       "\n",
       "    return sched\n",
       "
\n" ], "text/latex": [ "\\begin{Verbatim}[commandchars=\\\\\\{\\}]\n", "\\PY{k}{def} \\PY{n+nf}{mk\\PYZus{}surface7\\PYZus{}sched}\\PY{p}{(}\\PY{n}{num\\PYZus{}cycles}\\PY{p}{:} \\PY{n+nb}{int} \\PY{o}{=} \\PY{l+m+mi}{3}\\PY{p}{)}\\PY{p}{:}\n", " \\PY{l+s+sd}{\\PYZdq{}\\PYZdq{}\\PYZdq{}Generates a schedule with some of the feature of a Surface 7 experiment as}\n", "\\PY{l+s+sd}{ portrayed in Fig. 4b of :cite:`marques\\PYZus{}logical\\PYZus{}qubit\\PYZus{}2021`.}\n", "\n", "\\PY{l+s+sd}{ Parameters}\n", "\\PY{l+s+sd}{ \\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}}\n", "\\PY{l+s+sd}{ num\\PYZus{}cycles}\n", "\\PY{l+s+sd}{ The number of times to repeat the main cycle.}\n", "\n", "\\PY{l+s+sd}{ Returns}\n", "\\PY{l+s+sd}{ \\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}}\n", "\\PY{l+s+sd}{ :}\n", "\\PY{l+s+sd}{ A schedule similar to a Surface 7 dance.}\n", "\\PY{l+s+sd}{ \\PYZdq{}\\PYZdq{}\\PYZdq{}}\n", "\n", " \\PY{k+kn}{from} 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"execution": { "iopub.execute_input": "2023-09-26T17:43:26.979753Z", "iopub.status.busy": "2023-09-26T17:43:26.979544Z", "iopub.status.idle": "2023-09-26T17:43:27.028939Z", "shell.execute_reply": "2023-09-26T17:43:27.028196Z" } }, "outputs": [ { "data": { "text/html": [ "
def mk_iq_shots(\n",
       "    num_shots: int = 128,\n",
       "    sigmas: Union[Tuple[float], np.ndarray] = (0.1, 0.1),\n",
       "    centers: Union[Tuple[complex], np.ndarray] = (-0.2 + 0.65j, 0.7 + 4j),\n",
       "    probabilities: Union[Tuple[float], np.ndarray] = (0.4, 0.6),\n",
       "    seed: Union[int, None] = 112233,\n",
       ") -> np.ndarray:\n",
       "    """\n",
       "    Generates clusters of (I + 1j*Q) points with a Gaussian distribution with the\n",
       "    specified sigmas and centers according to the probabilities of each cluster\n",
       "\n",
       "    .. admonition:: Examples\n",
       "        :class: dropdown\n",
       "\n",
       "        .. include:: examples/utilities.examples_support.mk_iq_shots.rst.txt\n",
       "\n",
       "    Parameters\n",
       "    ----------\n",
       "    num_shots\n",
       "        The number of shot to generate.\n",
       "    sigma\n",
       "        The sigma of the Gaussian distribution used for both real and imaginary parts.\n",
       "    centers\n",
       "        The center of each cluster on the imaginary plane.\n",
       "    probabilities\n",
       "        The probabilities of each cluster being randomly selected for each shot.\n",
       "    seed\n",
       "        Random number generator seed passed to ``numpy.random.default_rng``.\n",
       "    """\n",
       "    if not len(sigmas) == len(centers) == len(probabilities):\n",
       "        raise ValueError(\n",
       "            f"Incorrect input. sigmas={sigmas}, centers={centers} and "\n",
       "            f"probabilities={probabilities} must have the same length."\n",
       "        )\n",
       "\n",
       "    rng = np.random.default_rng(seed=seed)\n",
       "\n",
       "    cluster_indices = tuple(range(len(centers)))\n",
       "    choices = rng.choice(a=cluster_indices, size=num_shots, p=probabilities)\n",
       "\n",
       "    shots = []\n",
       "    for idx in cluster_indices:\n",
       "        num_shots_this_cluster = np.sum(choices == idx)\n",
       "        i_data = rng.normal(\n",
       "            loc=centers[idx].real,\n",
       "            scale=sigmas[idx],\n",
       "            size=num_shots_this_cluster,\n",
       "        )\n",
       "        q_data = rng.normal(\n",
       "            loc=centers[idx].imag,\n",
       "            scale=sigmas[idx],\n",
       "            size=num_shots_this_cluster,\n",
       "        )\n",
       "        shots.append(i_data + 1j * q_data)\n",
       "    return np.concatenate(shots)\n",
       "
\n" ], "text/latex": [ "\\begin{Verbatim}[commandchars=\\\\\\{\\}]\n", "\\PY{k}{def} \\PY{n+nf}{mk\\PYZus{}iq\\PYZus{}shots}\\PY{p}{(}\n", " \\PY{n}{num\\PYZus{}shots}\\PY{p}{:} \\PY{n+nb}{int} \\PY{o}{=} \\PY{l+m+mi}{128}\\PY{p}{,}\n", " \\PY{n}{sigmas}\\PY{p}{:} \\PY{n}{Union}\\PY{p}{[}\\PY{n}{Tuple}\\PY{p}{[}\\PY{n+nb}{float}\\PY{p}{]}\\PY{p}{,} \\PY{n}{np}\\PY{o}{.}\\PY{n}{ndarray}\\PY{p}{]} \\PY{o}{=} \\PY{p}{(}\\PY{l+m+mf}{0.1}\\PY{p}{,} \\PY{l+m+mf}{0.1}\\PY{p}{)}\\PY{p}{,}\n", " \\PY{n}{centers}\\PY{p}{:} \\PY{n}{Union}\\PY{p}{[}\\PY{n}{Tuple}\\PY{p}{[}\\PY{n+nb}{complex}\\PY{p}{]}\\PY{p}{,} \\PY{n}{np}\\PY{o}{.}\\PY{n}{ndarray}\\PY{p}{]} \\PY{o}{=} \\PY{p}{(}\\PY{o}{\\PYZhy{}}\\PY{l+m+mf}{0.2} \\PY{o}{+} \\PY{l+m+mf}{0.65}\\PY{n}{j}\\PY{p}{,} \\PY{l+m+mf}{0.7} \\PY{o}{+} \\PY{l+m+mi}{4}\\PY{n}{j}\\PY{p}{)}\\PY{p}{,}\n", " \\PY{n}{probabilities}\\PY{p}{:} \\PY{n}{Union}\\PY{p}{[}\\PY{n}{Tuple}\\PY{p}{[}\\PY{n+nb}{float}\\PY{p}{]}\\PY{p}{,} \\PY{n}{np}\\PY{o}{.}\\PY{n}{ndarray}\\PY{p}{]} \\PY{o}{=} \\PY{p}{(}\\PY{l+m+mf}{0.4}\\PY{p}{,} \\PY{l+m+mf}{0.6}\\PY{p}{)}\\PY{p}{,}\n", " \\PY{n}{seed}\\PY{p}{:} \\PY{n}{Union}\\PY{p}{[}\\PY{n+nb}{int}\\PY{p}{,} \\PY{k+kc}{None}\\PY{p}{]} \\PY{o}{=} \\PY{l+m+mi}{112233}\\PY{p}{,}\n", "\\PY{p}{)} \\PY{o}{\\PYZhy{}}\\PY{o}{\\PYZgt{}} \\PY{n}{np}\\PY{o}{.}\\PY{n}{ndarray}\\PY{p}{:}\n", " \\PY{l+s+sd}{\\PYZdq{}\\PYZdq{}\\PYZdq{}}\n", "\\PY{l+s+sd}{ Generates clusters of (I + 1j*Q) points with a Gaussian distribution with the}\n", "\\PY{l+s+sd}{ specified sigmas and centers according to the probabilities of each cluster}\n", "\n", "\\PY{l+s+sd}{ .. admonition:: Examples}\n", "\\PY{l+s+sd}{ :class: dropdown}\n", "\n", "\\PY{l+s+sd}{ .. include:: examples/utilities.examples\\PYZus{}support.mk\\PYZus{}iq\\PYZus{}shots.rst.txt}\n", "\n", "\\PY{l+s+sd}{ Parameters}\n", "\\PY{l+s+sd}{ \\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}}\n", "\\PY{l+s+sd}{ num\\PYZus{}shots}\n", "\\PY{l+s+sd}{ The number of shot to generate.}\n", "\\PY{l+s+sd}{ sigma}\n", "\\PY{l+s+sd}{ The sigma of the Gaussian distribution used for both real and imaginary parts.}\n", "\\PY{l+s+sd}{ centers}\n", "\\PY{l+s+sd}{ The center of each cluster on the imaginary plane.}\n", "\\PY{l+s+sd}{ probabilities}\n", "\\PY{l+s+sd}{ The probabilities of each cluster being randomly selected for each shot.}\n", "\\PY{l+s+sd}{ seed}\n", "\\PY{l+s+sd}{ Random number generator seed passed to ``numpy.random.default\\PYZus{}rng``.}\n", "\\PY{l+s+sd}{ \\PYZdq{}\\PYZdq{}\\PYZdq{}}\n", " \\PY{k}{if} \\PY{o+ow}{not} \\PY{n+nb}{len}\\PY{p}{(}\\PY{n}{sigmas}\\PY{p}{)} \\PY{o}{==} \\PY{n+nb}{len}\\PY{p}{(}\\PY{n}{centers}\\PY{p}{)} \\PY{o}{==} \\PY{n+nb}{len}\\PY{p}{(}\\PY{n}{probabilities}\\PY{p}{)}\\PY{p}{:}\n", " \\PY{k}{raise} \\PY{n+ne}{ValueError}\\PY{p}{(}\n", " \\PY{l+s+sa}{f}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Incorrect input. sigmas=}\\PY{l+s+si}{\\PYZob{}}\\PY{n}{sigmas}\\PY{l+s+si}{\\PYZcb{}}\\PY{l+s+s2}{, centers=}\\PY{l+s+si}{\\PYZob{}}\\PY{n}{centers}\\PY{l+s+si}{\\PYZcb{}}\\PY{l+s+s2}{ and }\\PY{l+s+s2}{\\PYZdq{}}\n", " \\PY{l+s+sa}{f}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{probabilities=}\\PY{l+s+si}{\\PYZob{}}\\PY{n}{probabilities}\\PY{l+s+si}{\\PYZcb{}}\\PY{l+s+s2}{ must have the same length.}\\PY{l+s+s2}{\\PYZdq{}}\n", " \\PY{p}{)}\n", "\n", " \\PY{n}{rng} \\PY{o}{=} \\PY{n}{np}\\PY{o}{.}\\PY{n}{random}\\PY{o}{.}\\PY{n}{default\\PYZus{}rng}\\PY{p}{(}\\PY{n}{seed}\\PY{o}{=}\\PY{n}{seed}\\PY{p}{)}\n", "\n", " \\PY{n}{cluster\\PYZus{}indices} \\PY{o}{=} \\PY{n+nb}{tuple}\\PY{p}{(}\\PY{n+nb}{range}\\PY{p}{(}\\PY{n+nb}{len}\\PY{p}{(}\\PY{n}{centers}\\PY{p}{)}\\PY{p}{)}\\PY{p}{)}\n", " \\PY{n}{choices} \\PY{o}{=} \\PY{n}{rng}\\PY{o}{.}\\PY{n}{choice}\\PY{p}{(}\\PY{n}{a}\\PY{o}{=}\\PY{n}{cluster\\PYZus{}indices}\\PY{p}{,} \\PY{n}{size}\\PY{o}{=}\\PY{n}{num\\PYZus{}shots}\\PY{p}{,} 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\\PY{n}{scale}\\PY{o}{=}\\PY{n}{sigmas}\\PY{p}{[}\\PY{n}{idx}\\PY{p}{]}\\PY{p}{,}\n", " \\PY{n}{size}\\PY{o}{=}\\PY{n}{num\\PYZus{}shots\\PYZus{}this\\PYZus{}cluster}\\PY{p}{,}\n", " \\PY{p}{)}\n", " \\PY{n}{shots}\\PY{o}{.}\\PY{n}{append}\\PY{p}{(}\\PY{n}{i\\PYZus{}data} \\PY{o}{+} \\PY{l+m+mi}{1}\\PY{n}{j} \\PY{o}{*} \\PY{n}{q\\PYZus{}data}\\PY{p}{)}\n", " \\PY{k}{return} \\PY{n}{np}\\PY{o}{.}\\PY{n}{concatenate}\\PY{p}{(}\\PY{n}{shots}\\PY{p}{)}\n", "\\end{Verbatim}\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
def mk_shots_from_probabilities(probabilities: Union[np.ndarray, list], **kwargs):\n",
       "    """Generates multiple shots for a list of probabilities assuming two states.\n",
       "\n",
       "    Parameters\n",
       "    ----------\n",
       "    probabilities\n",
       "        The list/array of the probabilities of one of the states.\n",
       "    **kwargs\n",
       "        Keyword arguments passed to\n",
       "        :func:`~quantify_core.utilities.examples_support.mk_iq_shots`.\n",
       "\n",
       "    Returns\n",
       "    -------\n",
       "    :\n",
       "        Array containing the shots. Shape: (num_shots, len(probabilities)).\n",
       "    """\n",
       "\n",
       "    shots = np.array(\n",
       "        tuple(\n",
       "            mk_iq_shots(probabilities=[prob, 1 - prob], **kwargs)\n",
       "            for prob in probabilities\n",
       "        )\n",
       "    ).T\n",
       "\n",
       "    return shots\n",
       "
\n" ], "text/latex": [ "\\begin{Verbatim}[commandchars=\\\\\\{\\}]\n", "\\PY{k}{def} \\PY{n+nf}{mk\\PYZus{}shots\\PYZus{}from\\PYZus{}probabilities}\\PY{p}{(}\\PY{n}{probabilities}\\PY{p}{:} \\PY{n}{Union}\\PY{p}{[}\\PY{n}{np}\\PY{o}{.}\\PY{n}{ndarray}\\PY{p}{,} \\PY{n+nb}{list}\\PY{p}{]}\\PY{p}{,} \\PY{o}{*}\\PY{o}{*}\\PY{n}{kwargs}\\PY{p}{)}\\PY{p}{:}\n", " \\PY{l+s+sd}{\\PYZdq{}\\PYZdq{}\\PYZdq{}Generates multiple shots for a list of probabilities assuming two states.}\n", "\n", "\\PY{l+s+sd}{ Parameters}\n", "\\PY{l+s+sd}{ \\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}}\n", "\\PY{l+s+sd}{ probabilities}\n", "\\PY{l+s+sd}{ The list/array of the probabilities of one of the states.}\n", "\\PY{l+s+sd}{ **kwargs}\n", "\\PY{l+s+sd}{ Keyword arguments passed to}\n", "\\PY{l+s+sd}{ :func:`\\PYZti{}quantify\\PYZus{}core.utilities.examples\\PYZus{}support.mk\\PYZus{}iq\\PYZus{}shots`.}\n", "\n", "\\PY{l+s+sd}{ Returns}\n", "\\PY{l+s+sd}{ \\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}}\n", "\\PY{l+s+sd}{ :}\n", "\\PY{l+s+sd}{ Array containing the shots. Shape: (num\\PYZus{}shots, len(probabilities)).}\n", "\\PY{l+s+sd}{ \\PYZdq{}\\PYZdq{}\\PYZdq{}}\n", "\n", " \\PY{n}{shots} \\PY{o}{=} \\PY{n}{np}\\PY{o}{.}\\PY{n}{array}\\PY{p}{(}\n", " \\PY{n+nb}{tuple}\\PY{p}{(}\n", " \\PY{n}{mk\\PYZus{}iq\\PYZus{}shots}\\PY{p}{(}\\PY{n}{probabilities}\\PY{o}{=}\\PY{p}{[}\\PY{n}{prob}\\PY{p}{,} \\PY{l+m+mi}{1} \\PY{o}{\\PYZhy{}} \\PY{n}{prob}\\PY{p}{]}\\PY{p}{,} \\PY{o}{*}\\PY{o}{*}\\PY{n}{kwargs}\\PY{p}{)}\n", " \\PY{k}{for} \\PY{n}{prob} \\PY{o+ow}{in} \\PY{n}{probabilities}\n", " \\PY{p}{)}\n", " \\PY{p}{)}\\PY{o}{.}\\PY{n}{T}\n", "\n", " \\PY{k}{return} \\PY{n}{shots}\n", "\\end{Verbatim}\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
def mk_surface7_cyles_dataset(num_cycles: int = 3, **kwargs) -> xr.Dataset:\n",
       "    """\n",
       "    See also :func:`quantify_core.utilities.examples_support.mk_surface7_sched`.\n",
       "\n",
       "    Parameters\n",
       "    ----------\n",
       "    num_cycles\n",
       "        The number of repeating cycles before the final measurement.\n",
       "    **kwargs\n",
       "        Keyword arguments passed to :func:`~.mk_shots_from_probabilities`.\n",
       "    """\n",
       "\n",
       "    cycles = range(num_cycles)\n",
       "\n",
       "    mock_data = mk_shots_from_probabilities(\n",
       "        probabilities=[np.random.random() for _ in cycles], **kwargs\n",
       "    )\n",
       "\n",
       "    mock_data_final = mk_shots_from_probabilities(\n",
       "        probabilities=[np.random.random()], **kwargs\n",
       "    )\n",
       "\n",
       "    # %%\n",
       "    data_vars = {}\n",
       "\n",
       "    # NB same random data is used for all qubits only for the simplicity of the mock!\n",
       "    for qubit in (f"A{i}" for i in range(3)):\n",
       "        data_vars[f"{qubit}_shots"] = (\n",
       "            ("repetitions", "dim_cycle"),\n",
       "            mock_data,\n",
       "            mk_main_var_attrs(\n",
       "                unit="V", long_name=f"IQ amplitude {qubit}", has_repetitions=True\n",
       "            ),\n",
       "        )\n",
       "\n",
       "    for qubit in (f"D{i}" for i in range(4)):\n",
       "        data_vars[f"{qubit}_shots"] = (\n",
       "            ("repetitions", "dim_final"),\n",
       "            mock_data_final,\n",
       "            mk_main_var_attrs(\n",
       "                unit="V", long_name=f"IQ amplitude {qubit}", has_repetitions=True\n",
       "            ),\n",
       "        )\n",
       "\n",
       "    cycle_attrs = mk_main_coord_attrs(long_name="Surface code cycle number")\n",
       "    final_msmt_attrs = mk_main_coord_attrs(long_name="Final measurement")\n",
       "    coords = dict(\n",
       "        cycle=("dim_cycle", cycles, cycle_attrs),\n",
       "        final_msmt=("dim_final", [0], final_msmt_attrs),\n",
       "    )\n",
       "\n",
       "    dataset = xr.Dataset(\n",
       "        data_vars=data_vars,\n",
       "        coords=coords,\n",
       "        attrs=mk_dataset_attrs(),\n",
       "    )\n",
       "\n",
       "    return dataset\n",
       "
\n" ], "text/latex": [ "\\begin{Verbatim}[commandchars=\\\\\\{\\}]\n", "\\PY{k}{def} \\PY{n+nf}{mk\\PYZus{}surface7\\PYZus{}cyles\\PYZus{}dataset}\\PY{p}{(}\\PY{n}{num\\PYZus{}cycles}\\PY{p}{:} \\PY{n+nb}{int} \\PY{o}{=} \\PY{l+m+mi}{3}\\PY{p}{,} \\PY{o}{*}\\PY{o}{*}\\PY{n}{kwargs}\\PY{p}{)} \\PY{o}{\\PYZhy{}}\\PY{o}{\\PYZgt{}} \\PY{n}{xr}\\PY{o}{.}\\PY{n}{Dataset}\\PY{p}{:}\n", " \\PY{l+s+sd}{\\PYZdq{}\\PYZdq{}\\PYZdq{}}\n", "\\PY{l+s+sd}{ See also :func:`quantify\\PYZus{}core.utilities.examples\\PYZus{}support.mk\\PYZus{}surface7\\PYZus{}sched`.}\n", "\n", "\\PY{l+s+sd}{ Parameters}\n", "\\PY{l+s+sd}{ \\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}}\n", "\\PY{l+s+sd}{ num\\PYZus{}cycles}\n", "\\PY{l+s+sd}{ The number of repeating cycles before the final measurement.}\n", "\\PY{l+s+sd}{ **kwargs}\n", "\\PY{l+s+sd}{ Keyword arguments passed to :func:`\\PYZti{}.mk\\PYZus{}shots\\PYZus{}from\\PYZus{}probabilities`.}\n", "\\PY{l+s+sd}{ \\PYZdq{}\\PYZdq{}\\PYZdq{}}\n", "\n", " \\PY{n}{cycles} \\PY{o}{=} \\PY{n+nb}{range}\\PY{p}{(}\\PY{n}{num\\PYZus{}cycles}\\PY{p}{)}\n", "\n", " \\PY{n}{mock\\PYZus{}data} \\PY{o}{=} \\PY{n}{mk\\PYZus{}shots\\PYZus{}from\\PYZus{}probabilities}\\PY{p}{(}\n", " \\PY{n}{probabilities}\\PY{o}{=}\\PY{p}{[}\\PY{n}{np}\\PY{o}{.}\\PY{n}{random}\\PY{o}{.}\\PY{n}{random}\\PY{p}{(}\\PY{p}{)} \\PY{k}{for} \\PY{n}{\\PYZus{}} \\PY{o+ow}{in} \\PY{n}{cycles}\\PY{p}{]}\\PY{p}{,} \\PY{o}{*}\\PY{o}{*}\\PY{n}{kwargs}\n", " \\PY{p}{)}\n", "\n", " \\PY{n}{mock\\PYZus{}data\\PYZus{}final} \\PY{o}{=} \\PY{n}{mk\\PYZus{}shots\\PYZus{}from\\PYZus{}probabilities}\\PY{p}{(}\n", " \\PY{n}{probabilities}\\PY{o}{=}\\PY{p}{[}\\PY{n}{np}\\PY{o}{.}\\PY{n}{random}\\PY{o}{.}\\PY{n}{random}\\PY{p}{(}\\PY{p}{)}\\PY{p}{]}\\PY{p}{,} \\PY{o}{*}\\PY{o}{*}\\PY{n}{kwargs}\n", " \\PY{p}{)}\n", "\n", " \\PY{c+c1}{\\PYZsh{} \\PYZpc{}\\PYZpc{}}\n", " \\PY{n}{data\\PYZus{}vars} \\PY{o}{=} \\PY{p}{\\PYZob{}}\\PY{p}{\\PYZcb{}}\n", "\n", " \\PY{c+c1}{\\PYZsh{} NB same random data is used for all qubits only for the simplicity of the mock!}\n", " \\PY{k}{for} \\PY{n}{qubit} \\PY{o+ow}{in} \\PY{p}{(}\\PY{l+s+sa}{f}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{A}\\PY{l+s+si}{\\PYZob{}}\\PY{n}{i}\\PY{l+s+si}{\\PYZcb{}}\\PY{l+s+s2}{\\PYZdq{}} \\PY{k}{for} \\PY{n}{i} \\PY{o+ow}{in} \\PY{n+nb}{range}\\PY{p}{(}\\PY{l+m+mi}{3}\\PY{p}{)}\\PY{p}{)}\\PY{p}{:}\n", " \\PY{n}{data\\PYZus{}vars}\\PY{p}{[}\\PY{l+s+sa}{f}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+si}{\\PYZob{}}\\PY{n}{qubit}\\PY{l+s+si}{\\PYZcb{}}\\PY{l+s+s2}{\\PYZus{}shots}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{]} \\PY{o}{=} \\PY{p}{(}\n", " \\PY{p}{(}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{repetitions}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{dim\\PYZus{}cycle}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\\PY{p}{,}\n", " \\PY{n}{mock\\PYZus{}data}\\PY{p}{,}\n", " \\PY{n}{mk\\PYZus{}main\\PYZus{}var\\PYZus{}attrs}\\PY{p}{(}\n", " 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\\PY{p}{(}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{repetitions}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{dim\\PYZus{}final}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\\PY{p}{,}\n", " \\PY{n}{mock\\PYZus{}data\\PYZus{}final}\\PY{p}{,}\n", " \\PY{n}{mk\\PYZus{}main\\PYZus{}var\\PYZus{}attrs}\\PY{p}{(}\n", " \\PY{n}{unit}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{V}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{long\\PYZus{}name}\\PY{o}{=}\\PY{l+s+sa}{f}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{IQ amplitude }\\PY{l+s+si}{\\PYZob{}}\\PY{n}{qubit}\\PY{l+s+si}{\\PYZcb{}}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{has\\PYZus{}repetitions}\\PY{o}{=}\\PY{k+kc}{True}\n", " \\PY{p}{)}\\PY{p}{,}\n", " \\PY{p}{)}\n", "\n", " \\PY{n}{cycle\\PYZus{}attrs} \\PY{o}{=} \\PY{n}{mk\\PYZus{}main\\PYZus{}coord\\PYZus{}attrs}\\PY{p}{(}\\PY{n}{long\\PYZus{}name}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Surface code cycle number}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\n", " \\PY{n}{final\\PYZus{}msmt\\PYZus{}attrs} \\PY{o}{=} \\PY{n}{mk\\PYZus{}main\\PYZus{}coord\\PYZus{}attrs}\\PY{p}{(}\\PY{n}{long\\PYZus{}name}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Final measurement}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\n", " \\PY{n}{coords} \\PY{o}{=} \\PY{n+nb}{dict}\\PY{p}{(}\n", " \\PY{n}{cycle}\\PY{o}{=}\\PY{p}{(}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{dim\\PYZus{}cycle}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{cycles}\\PY{p}{,} \\PY{n}{cycle\\PYZus{}attrs}\\PY{p}{)}\\PY{p}{,}\n", " \\PY{n}{final\\PYZus{}msmt}\\PY{o}{=}\\PY{p}{(}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{dim\\PYZus{}final}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{p}{[}\\PY{l+m+mi}{0}\\PY{p}{]}\\PY{p}{,} \\PY{n}{final\\PYZus{}msmt\\PYZus{}attrs}\\PY{p}{)}\\PY{p}{,}\n", " \\PY{p}{)}\n", "\n", " \\PY{n}{dataset} \\PY{o}{=} \\PY{n}{xr}\\PY{o}{.}\\PY{n}{Dataset}\\PY{p}{(}\n", " \\PY{n}{data\\PYZus{}vars}\\PY{o}{=}\\PY{n}{data\\PYZus{}vars}\\PY{p}{,}\n", " \\PY{n}{coords}\\PY{o}{=}\\PY{n}{coords}\\PY{p}{,}\n", " \\PY{n}{attrs}\\PY{o}{=}\\PY{n}{mk\\PYZus{}dataset\\PYZus{}attrs}\\PY{p}{(}\\PY{p}{)}\\PY{p}{,}\n", " \\PY{p}{)}\n", "\n", " \\PY{k}{return} \\PY{n}{dataset}\n", "\\end{Verbatim}\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# mock data parameters\n", "num_shots = 128 # NB usually >~1000 in real experiments\n", "ground = -0.2 + 0.65j\n", "excited = 0.7 + 4j\n", "centroids = ground, excited\n", "sigmas = [0.1] * 2\n", "\n", "display_source_code(mk_iq_shots)\n", "display_source_code(mk_shots_from_probabilities)\n", "display_source_code(mk_surface7_cyles_dataset)" ] }, { "cell_type": "code", "execution_count": 4, "id": "dc0576e3", "metadata": { "execution": { "iopub.execute_input": "2023-09-26T17:43:27.031430Z", "iopub.status.busy": "2023-09-26T17:43:27.031222Z", "iopub.status.idle": "2023-09-26T17:43:27.211761Z", "shell.execute_reply": "2023-09-26T17:43:27.210983Z" } }, "outputs": [ { "data": { "text/html": [ "
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       "Dimensions:     (repetitions: 128, dim_cycle: 3, dim_final: 1)\n",
       "Coordinates:\n",
       "    cycle       (dim_cycle) int64 0 1 2\n",
       "    final_msmt  (dim_final) int64 0\n",
       "Dimensions without coordinates: repetitions, dim_cycle, dim_final\n",
       "Data variables:\n",
       "    A0_shots    (repetitions, dim_cycle) complex128 (-0.23630343679164473+0.6...\n",
       "    A1_shots    (repetitions, dim_cycle) complex128 (-0.23630343679164473+0.6...\n",
       "    A2_shots    (repetitions, dim_cycle) complex128 (-0.23630343679164473+0.6...\n",
       "    D0_shots    (repetitions, dim_final) complex128 (-0.23630343679164473+0.6...\n",
       "    D1_shots    (repetitions, dim_final) complex128 (-0.23630343679164473+0.6...\n",
       "    D2_shots    (repetitions, dim_final) complex128 (-0.23630343679164473+0.6...\n",
       "    D3_shots    (repetitions, dim_final) complex128 (-0.23630343679164473+0.6...\n",
       "Attributes:\n",
       "    tuid:                      20230926-194327-034-b9eeb7\n",
       "    dataset_name:              \n",
       "    dataset_state:             None\n",
       "    timestamp_start:           None\n",
       "    timestamp_end:             None\n",
       "    quantify_dataset_version:  2.0.0\n",
       "    software_versions:         {}\n",
       "    relationships:             []\n",
       "    json_serialize_exclude:    []
" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataset = mk_surface7_cyles_dataset(\n", " num_shots=num_shots, sigmas=sigmas, centers=centroids\n", ")\n", "\n", "assert dataset == round_trip_dataset(dataset) # confirm read/write\n", "\n", "dataset" ] }, { "cell_type": "code", "execution_count": 5, "id": "da6fd112", "metadata": { "execution": { "iopub.execute_input": "2023-09-26T17:43:27.215008Z", "iopub.status.busy": "2023-09-26T17:43:27.214796Z", "iopub.status.idle": "2023-09-26T17:43:27.223769Z", "shell.execute_reply": "2023-09-26T17:43:27.223076Z" } }, "outputs": [ { "data": { "text/html": [ "
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<xarray.Dataset>\n",
       "Dimensions:     (cycle: 3, final_msmt: 1, repetitions: 128)\n",
       "Coordinates:\n",
       "  * cycle       (cycle) int64 0 1 2\n",
       "  * final_msmt  (final_msmt) int64 0\n",
       "Dimensions without coordinates: repetitions\n",
       "Data variables:\n",
       "    A0_shots    (repetitions, cycle) complex128 (-0.23630343679164473+0.61492...\n",
       "    A1_shots    (repetitions, cycle) complex128 (-0.23630343679164473+0.61492...\n",
       "    A2_shots    (repetitions, cycle) complex128 (-0.23630343679164473+0.61492...\n",
       "    D0_shots    (repetitions, final_msmt) complex128 (-0.23630343679164473+0....\n",
       "    D1_shots    (repetitions, final_msmt) complex128 (-0.23630343679164473+0....\n",
       "    D2_shots    (repetitions, final_msmt) complex128 (-0.23630343679164473+0....\n",
       "    D3_shots    (repetitions, final_msmt) complex128 (-0.23630343679164473+0....\n",
       "Attributes:\n",
       "    tuid:                      20230926-194327-034-b9eeb7\n",
       "    dataset_name:              \n",
       "    dataset_state:             None\n",
       "    timestamp_start:           None\n",
       "    timestamp_end:             None\n",
       "    quantify_dataset_version:  2.0.0\n",
       "    software_versions:         {}\n",
       "    relationships:             []\n",
       "    json_serialize_exclude:    []
" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataset_gridded = dh.to_gridded_dataset(\n", " dataset, dimension=\"dim_cycle\", coords_names=[\"cycle\"]\n", ")\n", "dataset_gridded = dh.to_gridded_dataset(\n", " dataset_gridded, dimension=\"dim_final\", coords_names=[\"final_msmt\"]\n", ")\n", "dataset_gridded" ] }, { "cell_type": "code", "execution_count": 7, "id": "3b152168", "metadata": { "execution": { "iopub.execute_input": "2023-09-26T17:43:27.294086Z", "iopub.status.busy": "2023-09-26T17:43:27.293880Z", "iopub.status.idle": "2023-09-26T17:43:27.491841Z", "shell.execute_reply": "2023-09-26T17:43:27.491051Z" } }, "outputs": [ { "data": { "image/png": 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\n" }, "metadata": { "filenames": { "image/png": "/home/slavoutich/code/orangeqs/quantify-core/docs/_build/jupyter_execute/technical_notes/dataset_design/Quantify dataset - advanced examples_6_0.png" } }, "output_type": "display_data" } ], "source": [ "dataset_gridded.A0_shots.real.mean(\"repetitions\").plot(marker=\"o\", label=\"I-quadrature\")\n", "dataset_gridded.A0_shots.imag.mean(\"repetitions\").plot(marker=\"^\", label=\"Q-quadrature\")\n", "_ = plt.gca().legend()" ] }, { "cell_type": "code", "execution_count": 8, "id": "c11e3021", "metadata": { "execution": { "iopub.execute_input": "2023-09-26T17:43:27.494367Z", "iopub.status.busy": "2023-09-26T17:43:27.494152Z", "iopub.status.idle": "2023-09-26T17:43:28.117606Z", "shell.execute_reply": "2023-09-26T17:43:28.117016Z" } }, "outputs": [ { "data": { "image/png": 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\n" }, "metadata": { "filenames": { "image/png": "/home/slavoutich/code/orangeqs/quantify-core/docs/_build/jupyter_execute/technical_notes/dataset_design/Quantify dataset - advanced examples_7_0.png" } }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "rng = np.random.default_rng(seed=112244) # random number generator\n", "\n", "num_t1_datasets = 7\n", "t1_times = np.linspace(0, 120e-6, 30)\n", "\n", "for tau in rng.uniform(10e-6, 50e-6, num_t1_datasets):\n", " probabilities = exp_decay_func(\n", " t=t1_times, tau=tau, offset=0, n_factor=1, amplitude=1\n", " )\n", " dataset = dataset_examples.mk_t1_av_with_cal_dataset(t1_times, probabilities)\n", "\n", " round_trip_dataset(dataset) # confirm read/write\n", " dataset_g = dh.to_gridded_dataset(\n", " dataset, dimension=\"main_dim\", coords_names=[\"t1_time\"]\n", " )\n", " # rotate the iq data\n", " rotated_and_normalized = rotate_to_calibrated_axis(\n", " dataset_g.q0_iq_av.values, *dataset_g.q0_iq_av_cal.values\n", " )\n", " rotated_and_normalized_da = xr.DataArray(dataset_g.q0_iq_av)\n", " rotated_and_normalized_da.values = rotated_and_normalized\n", " rotated_and_normalized_da.attrs[\"long_name\"] = \"|1> Population\"\n", " rotated_and_normalized_da.attrs[\"units\"] = \"\"\n", " rotated_and_normalized_da.real.plot(ax=ax, label=dataset.tuid, marker=\".\")\n", "ax.set_title(\"Results from repeated T1 experiments\\n(different datasets)\")\n", "_ = ax.legend()" ] }, { "cell_type": "code", "execution_count": 9, "id": "0c420123", "metadata": { "execution": { "iopub.execute_input": "2023-09-26T17:43:28.120826Z", "iopub.status.busy": "2023-09-26T17:43:28.120323Z", "iopub.status.idle": "2023-09-26T17:43:28.149373Z", "shell.execute_reply": "2023-09-26T17:43:28.148707Z" } }, "outputs": [ { "data": { "text/html": [ "
def mk_nested_mc_dataset(\n",
       "    num_points: int = 12,\n",
       "    flux_bias_min_max: tuple = (-0.04, 0.04),\n",
       "    resonator_freqs_min_max: tuple = (7e9, 7.3e9),\n",
       "    qubit_freqs_min_max: tuple = (4.5e9, 5.0e9),\n",
       "    t1_values_min_max: tuple = (20e-6, 50e-6),\n",
       "    seed: Optional[int] = 112233,\n",
       ") -> xr.Dataset:\n",
       "    """\n",
       "    Generates a dataset with dataset references and several coordinates that serve to\n",
       "    index the same variables.\n",
       "\n",
       "    Note that the each value for ``resonator_freqs``, ``qubit_freqs`` and ``t1_values``\n",
       "    would have been extracted from other dataset corresponding to individual experiments\n",
       "    with their own dataset.\n",
       "\n",
       "    Parameters\n",
       "    ----------\n",
       "    num_points\n",
       "        Number of datapoints to generate (used for all variables/coordinates).\n",
       "    flux_bias_min_max\n",
       "        Range for mock values.\n",
       "    resonator_freqs_min_max\n",
       "        Range for mock values.\n",
       "    qubit_freqs_min_max\n",
       "        Range for mock values.\n",
       "    t1_values_min_max\n",
       "        Range for mock random values.\n",
       "    seed\n",
       "        Random number generator seed passed to ``numpy.random.default_rng``.\n",
       "    """\n",
       "    rng = np.random.default_rng(seed=seed)  # random number generator\n",
       "\n",
       "    flux_bias_vals = np.linspace(*flux_bias_min_max, num_points)\n",
       "    resonator_freqs = np.linspace(*resonator_freqs_min_max, num_points)\n",
       "    qubit_freqs = np.linspace(*qubit_freqs_min_max, num_points)\n",
       "    t1_values = rng.uniform(*t1_values_min_max, num_points)\n",
       "\n",
       "    resonator_freq_tuids = [dh.gen_tuid() for _ in range(num_points)]\n",
       "    qubit_freq_tuids = [dh.gen_tuid() for _ in range(num_points)]\n",
       "    t1_tuids = [dh.gen_tuid() for _ in range(num_points)]\n",
       "\n",
       "    coords = dict(\n",
       "        flux_bias=(\n",
       "            "main_dim",\n",
       "            flux_bias_vals,\n",
       "            mk_main_coord_attrs(long_name="Flux bias", unit="A"),\n",
       "        ),\n",
       "        resonator_freq_tuids=(\n",
       "            "main_dim",\n",
       "            resonator_freq_tuids,\n",
       "            mk_main_coord_attrs(\n",
       "                long_name="Dataset TUID resonator frequency", is_dataset_ref=True\n",
       "            ),\n",
       "        ),\n",
       "        qubit_freq_tuids=(\n",
       "            "main_dim",\n",
       "            qubit_freq_tuids,\n",
       "            mk_main_coord_attrs(\n",
       "                long_name="Dataset TUID qubit frequency", is_dataset_ref=True\n",
       "            ),\n",
       "        ),\n",
       "        t1_tuids=(\n",
       "            "main_dim",\n",
       "            t1_tuids,\n",
       "            mk_main_coord_attrs(long_name="Dataset TUID T1", is_dataset_ref=True),\n",
       "        ),\n",
       "    )\n",
       "\n",
       "    data_vars = dict(\n",
       "        resonator_freq=(\n",
       "            "main_dim",\n",
       "            resonator_freqs,\n",
       "            mk_main_var_attrs(long_name="Resonator frequency", unit="Hz"),\n",
       "        ),\n",
       "        qubit_freq=(\n",
       "            "main_dim",\n",
       "            qubit_freqs,\n",
       "            mk_main_var_attrs(long_name="Qubit frequency", unit="Hz"),\n",
       "        ),\n",
       "        t1=(\n",
       "            "main_dim",\n",
       "            t1_values,\n",
       "            mk_main_var_attrs(long_name="T1", unit="s"),\n",
       "        ),\n",
       "    )\n",
       "    dataset_attrs = mk_dataset_attrs()\n",
       "\n",
       "    dataset = xr.Dataset(data_vars=data_vars, coords=coords, attrs=dataset_attrs)\n",
       "\n",
       "    return dataset\n",
       "
\n" ], "text/latex": [ "\\begin{Verbatim}[commandchars=\\\\\\{\\}]\n", "\\PY{k}{def} \\PY{n+nf}{mk\\PYZus{}nested\\PYZus{}mc\\PYZus{}dataset}\\PY{p}{(}\n", " \\PY{n}{num\\PYZus{}points}\\PY{p}{:} \\PY{n+nb}{int} \\PY{o}{=} \\PY{l+m+mi}{12}\\PY{p}{,}\n", " \\PY{n}{flux\\PYZus{}bias\\PYZus{}min\\PYZus{}max}\\PY{p}{:} \\PY{n+nb}{tuple} \\PY{o}{=} \\PY{p}{(}\\PY{o}{\\PYZhy{}}\\PY{l+m+mf}{0.04}\\PY{p}{,} \\PY{l+m+mf}{0.04}\\PY{p}{)}\\PY{p}{,}\n", " \\PY{n}{resonator\\PYZus{}freqs\\PYZus{}min\\PYZus{}max}\\PY{p}{:} \\PY{n+nb}{tuple} \\PY{o}{=} \\PY{p}{(}\\PY{l+m+mf}{7e9}\\PY{p}{,} \\PY{l+m+mf}{7.3e9}\\PY{p}{)}\\PY{p}{,}\n", " \\PY{n}{qubit\\PYZus{}freqs\\PYZus{}min\\PYZus{}max}\\PY{p}{:} \\PY{n+nb}{tuple} \\PY{o}{=} \\PY{p}{(}\\PY{l+m+mf}{4.5e9}\\PY{p}{,} \\PY{l+m+mf}{5.0e9}\\PY{p}{)}\\PY{p}{,}\n", " \\PY{n}{t1\\PYZus{}values\\PYZus{}min\\PYZus{}max}\\PY{p}{:} \\PY{n+nb}{tuple} \\PY{o}{=} \\PY{p}{(}\\PY{l+m+mf}{20e\\PYZhy{}6}\\PY{p}{,} \\PY{l+m+mf}{50e\\PYZhy{}6}\\PY{p}{)}\\PY{p}{,}\n", " \\PY{n}{seed}\\PY{p}{:} \\PY{n}{Optional}\\PY{p}{[}\\PY{n+nb}{int}\\PY{p}{]} \\PY{o}{=} \\PY{l+m+mi}{112233}\\PY{p}{,}\n", "\\PY{p}{)} \\PY{o}{\\PYZhy{}}\\PY{o}{\\PYZgt{}} \\PY{n}{xr}\\PY{o}{.}\\PY{n}{Dataset}\\PY{p}{:}\n", " \\PY{l+s+sd}{\\PYZdq{}\\PYZdq{}\\PYZdq{}}\n", "\\PY{l+s+sd}{ Generates a dataset with dataset references and several coordinates that serve to}\n", "\\PY{l+s+sd}{ index the same variables.}\n", "\n", "\\PY{l+s+sd}{ Note that the each value for ``resonator\\PYZus{}freqs``, ``qubit\\PYZus{}freqs`` and ``t1\\PYZus{}values``}\n", "\\PY{l+s+sd}{ would have been extracted from other dataset corresponding to individual experiments}\n", "\\PY{l+s+sd}{ with their own dataset.}\n", "\n", "\\PY{l+s+sd}{ Parameters}\n", "\\PY{l+s+sd}{ \\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}}\n", "\\PY{l+s+sd}{ num\\PYZus{}points}\n", "\\PY{l+s+sd}{ Number of datapoints to generate (used for all variables/coordinates).}\n", "\\PY{l+s+sd}{ 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"display_source_code(mk_nested_mc_dataset)" ] }, { "cell_type": "code", "execution_count": 10, "id": "e2f25a71", "metadata": { "execution": { "iopub.execute_input": "2023-09-26T17:43:28.151746Z", "iopub.status.busy": "2023-09-26T17:43:28.151537Z", "iopub.status.idle": "2023-09-26T17:43:28.242947Z", "shell.execute_reply": "2023-09-26T17:43:28.242231Z" } }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.Dataset>\n",
       "Dimensions:               (main_dim: 7)\n",
       "Coordinates:\n",
       "    flux_bias             (main_dim) float64 -0.04 -0.02667 ... 0.02667 0.04\n",
       "    resonator_freq_tuids  (main_dim) <U26 '20230926-194328-153-9cd349' ... '2...\n",
       "    qubit_freq_tuids      (main_dim) <U26 '20230926-194328-153-890276' ... '2...\n",
       "    t1_tuids              (main_dim) <U26 '20230926-194328-153-8d4807' ... '2...\n",
       "Dimensions without coordinates: main_dim\n",
       "Data variables:\n",
       "    resonator_freq        (main_dim) float64 7e+09 7.05e+09 ... 7.25e+09 7.3e+09\n",
       "    qubit_freq            (main_dim) float64 4.5e+09 4.583e+09 ... 5e+09\n",
       "    t1                    (main_dim) float64 4.238e-05 3.867e-05 ... 4.154e-05\n",
       "Attributes:\n",
       "    tuid:                      20230926-194328-154-79ecfe\n",
       "    dataset_name:              \n",
       "    dataset_state:             None\n",
       "    timestamp_start:           None\n",
       "    timestamp_end:             None\n",
       "    quantify_dataset_version:  2.0.0\n",
       "    software_versions:         {}\n",
       "    relationships:             []\n",
       "    json_serialize_exclude:    []
" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataset = mk_nested_mc_dataset(num_points=num_t1_datasets)\n", "assert dataset == round_trip_dataset(dataset) # confirm read/write\n", "dataset" ] }, { "cell_type": "code", "execution_count": 11, "id": "6f3d9e79", "metadata": { "execution": { "iopub.execute_input": "2023-09-26T17:43:28.245456Z", "iopub.status.busy": "2023-09-26T17:43:28.245244Z", "iopub.status.idle": "2023-09-26T17:43:28.881606Z", "shell.execute_reply": "2023-09-26T17:43:28.880840Z" } }, "outputs": [ { "data": { "image/png": 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\n" }, "metadata": { "filenames": { "image/png": "/home/slavoutich/code/orangeqs/quantify-core/docs/_build/jupyter_execute/technical_notes/dataset_design/Quantify dataset - advanced examples_10_0.png" } }, "output_type": "display_data" } ], "source": [ "fig, axs = plt.subplots(3, 1, figsize=(10, 10), sharex=True)\n", "\n", "_ = dataset.t1.plot(x=\"flux_bias\", marker=\"o\", ax=axs[0].twiny(), color=\"C0\")\n", "x = \"t1_tuids\"\n", "_ = dataset.t1.plot(x=x, marker=\"o\", ax=axs[0], color=\"C0\")\n", "_ = dataset.resonator_freq.plot(x=x, marker=\"o\", ax=axs[1], color=\"C1\")\n", "_ = dataset.qubit_freq.plot(x=x, marker=\"o\", ax=axs[2], color=\"C2\")\n", "for tick in axs[2].get_xticklabels():\n", " tick.set_rotation(15) # avoid tuid labels overlapping" ] }, { "cell_type": "code", "execution_count": 12, "id": "ba43b5a7", "metadata": { "execution": { "iopub.execute_input": "2023-09-26T17:43:28.884249Z", "iopub.status.busy": "2023-09-26T17:43:28.884034Z", "iopub.status.idle": "2023-09-26T17:43:28.905480Z", "shell.execute_reply": "2023-09-26T17:43:28.904796Z" } }, "outputs": [ { "data": { "text/html": [ "
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<xarray.Dataset>\n",
       "Dimensions:               (main_dim: 7)\n",
       "Coordinates:\n",
       "  * main_dim              (main_dim) object MultiIndex\n",
       "  * flux_bias             (main_dim) float64 -0.04 -0.02667 ... 0.02667 0.04\n",
       "  * resonator_freq_tuids  (main_dim) <U26 '20230926-194328-153-9cd349' ... '2...\n",
       "  * qubit_freq_tuids      (main_dim) <U26 '20230926-194328-153-890276' ... '2...\n",
       "  * t1_tuids              (main_dim) <U26 '20230926-194328-153-8d4807' ... '2...\n",
       "Data variables:\n",
       "    resonator_freq        (main_dim) float64 7e+09 7.05e+09 ... 7.25e+09 7.3e+09\n",
       "    qubit_freq            (main_dim) float64 4.5e+09 4.583e+09 ... 5e+09\n",
       "    t1                    (main_dim) float64 4.238e-05 3.867e-05 ... 4.154e-05\n",
       "Attributes:\n",
       "    tuid:                      20230926-194328-154-79ecfe\n",
       "    dataset_name:              \n",
       "    dataset_state:             None\n",
       "    timestamp_start:           None\n",
       "    timestamp_end:             None\n",
       "    quantify_dataset_version:  2.0.0\n",
       "    software_versions:         {}\n",
       "    relationships:             []\n",
       "    json_serialize_exclude:    []
" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataset_multi_indexed = dataset.set_index({\"main_dim\": tuple(dataset.t1.coords.keys())})\n", "dataset_multi_indexed" ] }, { "cell_type": "code", "execution_count": 13, "id": "e7b67282", "metadata": { "execution": { "iopub.execute_input": "2023-09-26T17:43:28.907811Z", "iopub.status.busy": "2023-09-26T17:43:28.907604Z", "iopub.status.idle": "2023-09-26T17:43:28.918745Z", "shell.execute_reply": "2023-09-26T17:43:28.918263Z" } }, "outputs": [ { "data": { "text/html": [ "
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<xarray.DataArray 'qubit_freq' (main_dim: 1)>\n",
       "array([4.66666667e+09])\n",
       "Coordinates:\n",
       "  * main_dim              (main_dim) object MultiIndex\n",
       "  * flux_bias             (main_dim) float64 -0.01333\n",
       "  * resonator_freq_tuids  (main_dim) <U26 '20230926-194328-153-10360a'\n",
       "  * t1_tuids              (main_dim) <U26 '20230926-194328-153-ef9cab'\n",
       "    qubit_freq_tuids      <U26 '20230926-194328-153-7765a5'\n",
       "Attributes:\n",
       "    unit:                    Hz\n",
       "    long_name:               Qubit frequency\n",
       "    is_main_var:             True\n",
       "    uniformly_spaced:        True\n",
       "    grid:                    True\n",
       "    is_dataset_ref:          False\n",
       "    has_repetitions:         False\n",
       "    json_serialize_exclude:  []
" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "index = 2\n", "dataset_multi_indexed.qubit_freq.sel(\n", " qubit_freq_tuids=dataset_multi_indexed.qubit_freq_tuids.values[index]\n", ")" ] }, { "cell_type": "code", "execution_count": 14, "id": "9550b905", "metadata": { "execution": { "iopub.execute_input": "2023-09-26T17:43:28.920915Z", "iopub.status.busy": "2023-09-26T17:43:28.920711Z", "iopub.status.idle": "2023-09-26T17:43:28.932209Z", "shell.execute_reply": "2023-09-26T17:43:28.931522Z" } }, "outputs": [ { "data": { "text/html": [ "
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<xarray.DataArray 'qubit_freq' (main_dim: 1)>\n",
       "array([4.66666667e+09])\n",
       "Coordinates:\n",
       "  * main_dim              (main_dim) object MultiIndex\n",
       "  * flux_bias             (main_dim) float64 -0.01333\n",
       "  * resonator_freq_tuids  (main_dim) <U26 '20230926-194328-153-10360a'\n",
       "  * qubit_freq_tuids      (main_dim) <U26 '20230926-194328-153-7765a5'\n",
       "    t1_tuids              <U26 '20230926-194328-153-ef9cab'\n",
       "Attributes:\n",
       "    unit:                    Hz\n",
       "    long_name:               Qubit frequency\n",
       "    is_main_var:             True\n",
       "    uniformly_spaced:        True\n",
       "    grid:                    True\n",
       "    is_dataset_ref:          False\n",
       "    has_repetitions:         False\n",
       "    json_serialize_exclude:  []
" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataset_multi_indexed.qubit_freq.sel(t1_tuids=dataset.t1_tuids.values[index])" ] }, { "cell_type": "code", "execution_count": 15, "id": "e175f8f0", "metadata": { "execution": { "iopub.execute_input": "2023-09-26T17:43:28.934450Z", "iopub.status.busy": "2023-09-26T17:43:28.934246Z", "iopub.status.idle": "2023-09-26T17:43:28.941640Z", "shell.execute_reply": "2023-09-26T17:43:28.940977Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "variable 'main_dim' is a MultiIndex, which cannot yet be serialized to netCDF files (https://github.com/pydata/xarray/issues/1077). Use reset_index() to convert MultiIndex levels into coordinate variables instead.\n" ] } ], "source": [ "try:\n", " assert dataset_multi_indexed == round_trip_dataset(dataset_multi_indexed)\n", "except NotImplementedError as exp:\n", " print(exp)" ] }, { "cell_type": "code", "execution_count": 16, "id": "accfcb8f", "metadata": { "execution": { "iopub.execute_input": "2023-09-26T17:43:28.943964Z", "iopub.status.busy": "2023-09-26T17:43:28.943763Z", "iopub.status.idle": "2023-09-26T17:43:28.961350Z", "shell.execute_reply": "2023-09-26T17:43:28.960705Z" } }, "outputs": [ { "data": { "text/html": [ "
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<xarray.Dataset>\n",
       "Dimensions:               (main_dim: 7)\n",
       "Coordinates:\n",
       "    main_dim              (main_dim) object MultiIndex\n",
       "    flux_bias             (main_dim) float64 -0.04 -0.02667 ... 0.02667 0.04\n",
       "    resonator_freq_tuids  (main_dim) <U26 '20230926-194328-153-9cd349' ... '2...\n",
       "    qubit_freq_tuids      (main_dim) <U26 '20230926-194328-153-890276' ... '2...\n",
       "    t1_tuids              (main_dim) <U26 '20230926-194328-153-8d4807' ... '2...\n",
       "Data variables:\n",
       "    resonator_freq        (main_dim) float64 7e+09 7.05e+09 ... 7.25e+09 7.3e+09\n",
       "    qubit_freq            (main_dim) float64 4.5e+09 4.583e+09 ... 5e+09\n",
       "    t1                    (main_dim) float64 4.238e-05 3.867e-05 ... 4.154e-05\n",
       "Attributes:\n",
       "    tuid:                      20230926-194328-154-79ecfe\n",
       "    dataset_name:              \n",
       "    dataset_state:             None\n",
       "    timestamp_start:           None\n",
       "    timestamp_end:             None\n",
       "    quantify_dataset_version:  2.0.0\n",
       "    software_versions:         {}\n",
       "    relationships:             []\n",
       "    json_serialize_exclude:    []
" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataset_multi_indexed.reset_index(dims_or_levels=\"main_dim\")" ] }, { "cell_type": "code", "execution_count": 17, "id": "ace203aa", "metadata": { "execution": { "iopub.execute_input": "2023-09-26T17:43:28.963656Z", "iopub.status.busy": "2023-09-26T17:43:28.963441Z", "iopub.status.idle": "2023-09-26T17:43:28.974205Z", "shell.execute_reply": "2023-09-26T17:43:28.973487Z" } }, "outputs": [ { "data": { "text/html": [ "
True\n",
       "
\n" ], "text/plain": [ "\u001b[3;92mTrue\u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "all(dataset_multi_indexed.reset_index(\"main_dim\").t1_tuids == dataset.t1_tuids)" ] }, { "cell_type": "code", "execution_count": 18, "id": "4395bc49", "metadata": { "execution": { "iopub.execute_input": "2023-09-26T17:43:28.976538Z", "iopub.status.busy": "2023-09-26T17:43:28.976321Z", "iopub.status.idle": "2023-09-26T17:43:28.982268Z", "shell.execute_reply": "2023-09-26T17:43:28.981612Z" } }, "outputs": [ { "data": { "text/html": [ "
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       "
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