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    "# Design robust Mølmer–Sørensen gates with parametric trap drive amplification\n",
    "**Obtaining control solutions for two-qubit gates with modulation of the confining potential**\n",
    "\n",
    "Boulder Opal supports the development of high-performance entangling gates to enhance the quantum volume of trapped-ion quantum computers. Typically, Mølmer–Sørensen-type operations can be used to entangle multiple ions using pairs of lasers with opposite detunings and a fixed phase relationship. This operation can flexibly incorporate multiple entangling operations performed in parallel, or multi-qubit entangling operations using [robust controls](https://docs.q-ctrl.com/boulder-opal/toolkit/apply/trapped-ion-quantum-computing/how-to-optimize-error-robust-molmer-sorensen-gates-for-trapped-ions) designed using Boulder Opal.  In the [Designing robust, configurable, parallel gates for large trapped-ion arrays](https://docs.q-ctrl.com/boulder-opal/toolkit/apply/trapped-ion-quantum-computing/design-robust-configurable-parallel-gates-for-large-trapped-ion-arrays) application note we demonstrate how to combine these various capabilities for complex controls on large multi-ion arrays.  Recent work by [Burd et al.](https://www.nature.com/articles/s41567-021-01237-9) has demonstrated how modulation on the confining potential can be used to realize faster gates.\n",
    "\n",
    "In this application note, we describe how to combine robust-control optimization in Mølmer–Sørensen gates with parametric modulation of the trap drive.  We will cover:\n",
    "- Simulating gate dynamics with a parametrically modulated trap drive\n",
    "- Optimizing Mølmer–Sørensen gates using one and multiple modes\n",
    "- Calculating phase-space trajectories for arbitrary controls\n",
    "- Validating performance of robust gates by calculating quasi-static noise susceptibility\n",
    "\n",
    "Ultimately we demonstrate for the first time that it is possible to combine standard Mølmer–Sørensen-gate optimization techniques with parametric trap-drive modulation in order to achieve fast, robust gates."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports and initialization"
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import qctrlvisualizer as qv\n",
    "\n",
    "import boulderopal as bo\n",
    "\n",
    "plt.style.use(qv.get_qctrl_style())\n",
    "\n",
    "\n",
    "# Helper function for plotting phase space trajectories.\n",
    "def plot_phase_space_trajectories(trajectories):\n",
    "    plot_range = 1.1 * np.max(np.abs(trajectories))\n",
    "    fig, ax = plt.subplots(figsize=(5, 5))\n",
    "    fig.suptitle(\"Phase space trajectories\")\n",
    "    for mode in range(ion_count):\n",
    "        ax.plot(\n",
    "            np.real(trajectories[:, 0, mode]),\n",
    "            np.imag(trajectories[:, 0, mode]),\n",
    "            label=f\"mode {mode % 2}\",\n",
    "        )\n",
    "        ax.plot(\n",
    "            np.real(trajectories[-1, 0, mode]),\n",
    "            np.imag(trajectories[-1, 0, mode]),\n",
    "            \"xk\",\n",
    "            markersize=15,\n",
    "        )\n",
    "    ax.set_xlim(-plot_range, plot_range)\n",
    "    ax.set_ylim(-plot_range, plot_range)\n",
    "    ax.set_aspect(\"equal\")\n",
    "    ax.set_xlabel(\"q\")\n",
    "    ax.set_ylabel(\"p\")\n",
    "    ax.set_title(\"$x$-axis\")\n",
    "    ax.legend()\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "def reflect_signal(graph, moduli, phases, even_total_segment_count):\n",
    "    \"\"\"\n",
    "    Reflect a drive signal about its temporal midpoint\n",
    "    and return the combined signal.\n",
    "    (Milne et al., Phys. Rev. Applied, 2020)\n",
    "    \"\"\"\n",
    "\n",
    "    if even_total_segment_count:\n",
    "        moduli_refl = graph.reverse(moduli, [0])\n",
    "        phases_refl = graph.reverse(phases, [0])\n",
    "\n",
    "    else:\n",
    "        moduli_refl = graph.reverse(moduli[:-1], [0])\n",
    "        phases_refl = graph.reverse(phases[:-1], [0])\n",
    "\n",
    "    moduli_comb = graph.concatenate([moduli, moduli_refl], 0)\n",
    "    phases_comb = graph.concatenate([phases, 2 * phases[-1] - phases_refl], 0)\n",
    "\n",
    "    return moduli_comb, phases_comb\n",
    "\n",
    "\n",
    "def calculate_modulation_parameters(delta, g):\n",
    "    \"\"\"\n",
    "    For a given relative detuning delta and parametric drive strength g,\n",
    "    return the driven relative detuning\n",
    "        delta' = sqrt(delta^2 + g^2),\n",
    "    and the parametric modulation parameter\n",
    "        r = ln((delta + g) / (delta - g)) / 4.\n",
    "    \"\"\"\n",
    "\n",
    "    delta_prime = np.sqrt(delta**2 - g**2)\n",
    "    r = np.log((delta + g) / (delta - g)) / 4\n",
    "    return delta_prime, r"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating Boulder Opal pulses for two-qubit gates mediated by a single mode\n",
    "\n",
    "For standard Mølmer–Sørensen gate-operations, the laser detuning should be close to a motional mode frequency, and the displacements in the phase-space of each mode should be small. At the end of the operation, the phase-space trajectories for each mode should return to zero (for no residual state-motional entanglement) and the phase acquired between each pair of ions should correspond to target entangling phases $\\psi_{jk}$ between each pair of ions ($j$, $k$), indexing from 0:\n",
    "\n",
    "$$ U = \\exp\\left(i \\sum_{j=1}^{N-1} \\sum_{k=0}^{j-1} \\psi_{jk} \\sigma_x^j \\sigma_x^k \\right). $$\n",
    "\n",
    "This operation can incorporate multiple entangling operations performed in parallel, or multi-qubit entangling operations; our [multi-qubit gate application note](https://docs.q-ctrl.com/boulder-opal/toolkit/apply/trapped-ion-quantum-computing/design-robust-configurable-parallel-gates-for-large-trapped-ion-arrays) equips you to obtain and verify such operations.\n",
    "\n",
    "Here, we examine how modulation on the confining potential amplifies the mode-mediated gate interaction to provide faster gates as demonstrated recently by [Burd et al.](https://www.nature.com/articles/s41567-021-01237-9). The trap modulation produces the Hamiltonian:\n",
    "\n",
    "$$H_P = \\hbar g (a_p + a_p^\\dagger)^2 \\cos (\\omega_P t + \\theta)$$\n",
    "\n",
    "where $a_p$ is the annihilation operator for motional mode $p$, $g$ is the parametric drive strength, $\\omega_P$ is the modulation frequency and $\\theta$ is a relative phase term (we set $\\theta = 0$). When $\\omega_P$ is chosen near resonance with the sideband of mode $p$, and for sufficiently small $g$, just a single mode is modified by the parametric drive. A Bogoliubov normal mode transformation can then be applied to map mode $p$ from the *lab frame* to a *transformed frame*. This transformation can recover the usual Mølmer–Sørensen-type Hamiltonian, but now written in terms of the transformed mode operators in addition to a frequency transformation and a drive transformation (when just a single mode mediates the gate).\n",
    "\n",
    "We begin by exploring the impact of the trap modulation on the Mølmer–Sørensen gate time and robustness.\n",
    "\n",
    "First, specify the number and type of ions, as well as other trap and laser characteristics."
   ]
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     "start_time": "2021-07-06T03:25:21.139482Z"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your task (action_id=\"1827933\") is queued.\n",
      "Your task (action_id=\"1827933\") has started.\n",
      "Your task (action_id=\"1827933\") has completed.\n"
     ]
    }
   ],
   "source": [
    "# Define trap and laser characteristics.\n",
    "ion_count = 2\n",
    "center_of_mass_frequencies = [5.9e6, 6.1e6, 1.5e6]\n",
    "wavevector = [(2 * np.pi) / 355e-9, 0, 0]\n",
    "maximum_rabi_rate = 2 * np.pi * 20e3\n",
    "\n",
    "# Calculate Lamb–Dicke parameters as an array of shape [<axis>, <collective_mode>, <ion>]\n",
    "# and mode frequencies as an array of shape [<axis>, <collective_mode>].\n",
    "ion_chain_properties = bo.ions.obtain_ion_chain_properties(\n",
    "    atomic_mass=25,  # Mg ions\n",
    "    ion_count=ion_count,\n",
    "    center_of_mass_frequencies=center_of_mass_frequencies,\n",
    "    wavevector=wavevector,\n",
    ")\n",
    "lamb_dicke_parameters = ion_chain_properties[\"lamb_dicke_parameters\"]\n",
    "mode_frequencies = ion_chain_properties[\"relative_detunings\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, the target two-qubit operation is configured by specifying target relative phases between each ion pair. Element ($j$, $k$) of the target phase array is the relative phase between ions $j$ and $k$, with $k<j$. In the cell below the relative phase for ion pair (1, 0) is set to $\\pi/4$ for maximal two-qubit entanglement."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
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     "start_time": "2021-06-11T06:07:29.732473Z"
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   "outputs": [],
   "source": [
    "target_phases = np.array([[0, 0], [np.pi / 4, 0]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generating standard pulses\n",
    "\n",
    "The fastest gate is performed by a single displacement loop of the mediating motional mode in phase space. Here we demonstrate this standard operation along with the faster operation produced by adding trap modulation. Note that the gate time is specified by $\\pi/\\eta \\Omega_0$, where $\\eta$ is the Lamb Dicke parameter, $\\Omega_0$ is the drive strength, and the laser's relative detuning from the motional frequency is $\\delta_p = 2 \\eta \\Omega_0$. The following parameters have been chosen to satisfy these conditions, taking into account the drive and $\\delta_p$ transformations when $g>0$ (effectively enhancing the drive strength), as well as the further condition that $|g| < |\\delta_p|$."
   ]
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   "execution_count": 4,
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    "ExecuteTime": {
     "end_time": "2021-06-11T06:07:31.121203Z",
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   "source": [
    "# Standard gate and control specifications.\n",
    "all_parameters = {}\n",
    "all_parameters[\"Standard\"] = {\n",
    "    \"no_g\": {\n",
    "        \"g\": 0,  # Hz\n",
    "        \"laser_detuning\": 5.9e6 - 2.93e3,  # Hz\n",
    "        \"duration\": 341.3e-6,  # s\n",
    "    },\n",
    "    \"low_g\": {\n",
    "        \"g\": 2.9e3,  # Hz\n",
    "        \"laser_detuning\": 5.9e6 - 5.005e3,  # Hz\n",
    "        \"duration\": 245.1e-6,  # s\n",
    "    },\n",
    "    \"high_g\": {\n",
    "        \"g\": 50e3,  # Hz\n",
    "        \"laser_detuning\": 5.9e6 - 50.901e3,  # Hz\n",
    "        \"duration\": 104.9e-6,  # s\n",
    "    },\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we optimize the drive control for the given parameters. In the following optimization, the complex drive $\\gamma (t) = \\Omega e^{i \\phi}$ jointly addresses both ions in the chain such that the gate is mediated only by the center-of-mass mode. The optimizer attempts to minimize a particular cost, which is specified below as the infidelity. Infidelity quantifies the solution performance, and is determined by the final mode displacements, and the difference between the realized and target relative phase.\n",
    "\n",
    "Note that setting the parameters for the fastest single-loop gate will result in fixed drives close to the maximum Rabi rate to produce this solution. Valid gates can be found for slower durations by exploiting the freedom of the complex drive and modulating the drive controls over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def optimize_single_drive(\n",
    "    segment_count,\n",
    "    drive_g,\n",
    "    laser_detuning,\n",
    "    duration,\n",
    "    robust,\n",
    "    sample_count=50,\n",
    "    optimization_count=10,\n",
    "):\n",
    "    sample_times = np.linspace(0, duration, sample_count)\n",
    "\n",
    "    # Set up trap modulation transformations.\n",
    "    driven_relative_detunings = np.array(mode_frequencies - laser_detuning)\n",
    "    delta_prime, r = calculate_modulation_parameters(\n",
    "        driven_relative_detunings[0, 1], drive_g\n",
    "    )\n",
    "    driven_relative_detunings[0, 1] = delta_prime\n",
    "\n",
    "    # Optimize the controls.\n",
    "    graph = bo.Graph()\n",
    "\n",
    "    if robust:\n",
    "        free_segment_count = int(np.ceil(segment_count / 2))\n",
    "    else:\n",
    "        free_segment_count = segment_count\n",
    "\n",
    "    moduli = graph.optimization_variable(\n",
    "        count=free_segment_count, lower_bound=0, upper_bound=maximum_rabi_rate\n",
    "    )\n",
    "    phases = graph.optimization_variable(\n",
    "        count=free_segment_count,\n",
    "        lower_bound=0,\n",
    "        upper_bound=2 * np.pi,\n",
    "        is_lower_unbounded=True,\n",
    "        is_upper_unbounded=True,\n",
    "    )\n",
    "    if robust:\n",
    "        moduli, phases = reflect_signal(graph, moduli, phases, segment_count % 2 == 0)\n",
    "\n",
    "    drive = graph.complex_pwc_signal(\n",
    "        moduli=moduli, phases=phases, duration=duration, name=\"ion_drive\"\n",
    "    )\n",
    "\n",
    "    # Drive transformation due to trap modulation.\n",
    "    modulated_ion_drive = drive * np.cosh(r) + graph.conjugate(drive) * np.sinh(r)\n",
    "\n",
    "    # Identical drives applied to each ion.\n",
    "    drives = [modulated_ion_drive] * ion_count\n",
    "\n",
    "    # Calculate Mølmer–Sørensen quantities.\n",
    "    ms_phases = graph.ions.ms_phases(\n",
    "        drives=drives,\n",
    "        lamb_dicke_parameters=lamb_dicke_parameters,\n",
    "        relative_detunings=driven_relative_detunings,\n",
    "        sample_times=sample_times,\n",
    "        name=\"phases\",\n",
    "    )\n",
    "    ms_displacements = graph.ions.ms_displacements(\n",
    "        drives=drives,\n",
    "        lamb_dicke_parameters=lamb_dicke_parameters,\n",
    "        relative_detunings=driven_relative_detunings,\n",
    "        sample_times=sample_times,\n",
    "        name=\"displacements\",\n",
    "    )\n",
    "    infidelity = graph.ions.ms_infidelity(\n",
    "        phases=ms_phases[-1],\n",
    "        displacements=ms_displacements[-1],\n",
    "        target_phases=target_phases,\n",
    "        name=\"infidelity\",\n",
    "    )\n",
    "\n",
    "    # Create cost node.\n",
    "    if robust:\n",
    "        robust_cost_term = graph.ions.ms_dephasing_robust_cost(\n",
    "            drives=drives,\n",
    "            lamb_dicke_parameters=lamb_dicke_parameters,\n",
    "            relative_detunings=driven_relative_detunings,\n",
    "        )\n",
    "        cost = infidelity + robust_cost_term\n",
    "    else:\n",
    "        cost = infidelity + 0.0\n",
    "    cost.name = \"cost\"\n",
    "\n",
    "    # Run optimization.\n",
    "    return bo.run_optimization(\n",
    "        graph=graph,\n",
    "        cost_node_name=\"cost\",\n",
    "        output_node_names=[\"displacements\", \"infidelity\", \"ion_drive\", \"phases\"],\n",
    "        optimization_count=optimization_count,\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your task (action_id=\"1827948\") is queued.\n",
      "Your task (action_id=\"1827948\") has started.\n",
      "Your task (action_id=\"1827948\") has completed.\n",
      "Drive strength: 0.00 kHz\n",
      "Gate duration: 341.30 µs\n",
      "Infidelity: 1.056e-11\n",
      "Cost: 1.056e-11\n",
      "\n",
      "Your task (action_id=\"1827953\") is queued.\n",
      "Your task (action_id=\"1827953\") has started.\n",
      "Your task (action_id=\"1827953\") has completed.\n",
      "Drive strength: 2.90 kHz\n",
      "Gate duration: 245.10 µs\n",
      "Infidelity: 1.229e-09\n",
      "Cost: 1.229e-09\n",
      "\n",
      "Your task (action_id=\"1827971\") is queued.\n",
      "Your task (action_id=\"1827971\") has started.\n",
      "Your task (action_id=\"1827971\") has completed.\n",
      "Drive strength: 50.00 kHz\n",
      "Gate duration: 104.90 µs\n",
      "Infidelity: 2.792e-12\n",
      "Cost: 2.792e-12\n",
      "\n"
     ]
    }
   ],
   "source": [
    "segment_count = 20\n",
    "\n",
    "all_results = {}\n",
    "all_results[\"Standard\"] = {}\n",
    "for key, parameters in all_parameters[\"Standard\"].items():\n",
    "    drive_g = parameters[\"g\"]\n",
    "    laser_detuning = parameters[\"laser_detuning\"]\n",
    "    duration = parameters[\"duration\"]\n",
    "\n",
    "    result = optimize_single_drive(\n",
    "        segment_count, drive_g, laser_detuning, duration, robust=False\n",
    "    )\n",
    "\n",
    "    all_results[\"Standard\"][key] = result\n",
    "    print(f\"Drive strength: {drive_g / 1e3:.2f} kHz\")\n",
    "    print(f\"Gate duration: {duration * 1e6:.2f} µs\")\n",
    "    print(f\"Infidelity: {result['output']['infidelity']['value']:.3e}\")\n",
    "    print(f\"Cost: {result['cost']:.3e}\")\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now extract the optimized controls."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-11T06:08:45.961309Z",
     "start_time": "2021-06-11T06:08:45.405569Z"
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   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "result = all_results[\"Standard\"][\"high_g\"]\n",
    "qv.plot_controls({f\"$\\\\gamma$\": result[\"output\"][\"ion_drive\"]})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above figure displays an exemplary optimized pulse modulus $\\Omega (t)$ and phase $\\phi (t)$ that addresses both ions. This control verifies that the maximum Rabi rate is consistently applied to obtain the fastest gate duration. \n",
    "\n",
    "We can also observe the *transformed* mode displacements: the trajectory of the center of a coherent state in (rotating) optical phase space for each mode."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Sum over ion index to obtain total displacement of the mode.\n",
    "plot_phase_space_trajectories(\n",
    "    np.sum(result[\"output\"][\"displacements\"][\"value\"], axis=3)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above figure displays an example of a phase-space trajectory, chosen for $g=50$ kHz. Here, $q \\equiv q_m = (a_m^\\dagger + a_m)/\\sqrt{2}$ and $p \\equiv p_m = i (a_m^\\dagger - a_m)/\\sqrt{2}$ are the dimensionless quadratures for each mode $m$. Importantly, here only the center-of-mass mode mediates the operation, and the trajectory is displayed for the *transformed* mode. The trajectories are circular for the fastest gate durations and any parametric drive strength.\n",
    "\n",
    "The black cross marks the final displacement for each mode: these are overlapping at zero, indicating no residual state-motional entanglement and no motional heating caused by the operations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generating robust pulses\n",
    "\n",
    "Robustness to pulse timing errors and mode or laser frequency noise [can be obtained](https://doi.org/10.1002/qute.202000044) by imposing additional conditions on the system dynamics. Robustness to these noise sources is given by a combination of symmetry in each ion's drive, as described in [Milne et al.](https://journals.aps.org/prapplied/abstract/10.1103/PhysRevApplied.13.024022), and optimization such that the center of mass of each mode's trajectory is at zero. You can do this using the result of `graph.ions.ms_dephasing_robust_cost` as an extra term in the cost function. For more information, see the [How to optimize error-robust Mølmer–Sørensen gates for trapped ions](https://docs.q-ctrl.com/boulder-opal/toolkit/apply/trapped-ion-quantum-computing/how-to-optimize-error-robust-molmer-sorensen-gates-for-trapped-ions) user guide.\n",
    "\n",
    "It is important to note that satisfying these robustness conditions necessarily requires longer gate durations.\n",
    "Additionally, since the mediating motional mode is transformed according to the trap parametric drive, the center of mass condition is calculated below for the *transformed* mode and does not precisely represent robustness to fluctuations in the *lab-frame* mode frequency. Using the robust optimization that follows, you can assess the impact of *lab frame* mode frequency fluctuations in the following section.\n",
    "\n",
    "In the following cell, the parametric drive and laser detunings are specified as for the standard gates above, with the gate durations extended to accommodate robust solutions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your task (action_id=\"1827979\") is queued.\n",
      "Your task (action_id=\"1827979\") has started.\n",
      "Your task (action_id=\"1827979\") has completed.\n",
      "Drive strength: 0.00 kHz\n",
      "Gate duration: 390.00 µs\n",
      "Infidelity: 1.093e-03\n",
      "Cost: 2.435e-03\n",
      "\n",
      "Your task (action_id=\"1827981\") is queued.\n",
      "Your task (action_id=\"1827981\") has started.\n",
      "Your task (action_id=\"1827981\") has completed.\n",
      "Drive strength: 2.90 kHz\n",
      "Gate duration: 295.00 µs\n",
      "Infidelity: 1.794e-03\n",
      "Cost: 7.383e-03\n",
      "\n",
      "Your task (action_id=\"1827985\") is queued.\n",
      "Your task (action_id=\"1827985\") has started.\n",
      "Your task (action_id=\"1827985\") has completed.\n",
      "Drive strength: 50.00 kHz\n",
      "Gate duration: 150.00 µs\n",
      "Infidelity: 5.641e-05\n",
      "Cost: 9.172e-05\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Robust gate and control specifications.\n",
    "all_parameters[\"Robust\"] = {\n",
    "    \"no_g\": {\n",
    "        \"g\": 0,  # Hz\n",
    "        \"laser_detuning\": 5.9e6 - 2.93e3,  # Hz\n",
    "        \"duration\": 390e-6,  # s\n",
    "    },\n",
    "    \"low_g\": {\n",
    "        \"g\": 2.9e3,  # Hz\n",
    "        \"laser_detuning\": 5.9e6 - 5.005e3,  # Hz\n",
    "        \"duration\": 295e-6,  # s\n",
    "    },\n",
    "    \"high_g\": {\n",
    "        \"g\": 50e3,  # Hz\n",
    "        \"laser_detuning\": 5.9e6 - 50.901e3,  # Hz\n",
    "        \"duration\": 150e-6,  # s\n",
    "    },\n",
    "}\n",
    "\n",
    "all_results[\"Robust\"] = {}\n",
    "for key, parameters in all_parameters[\"Robust\"].items():\n",
    "    drive_g = parameters[\"g\"]\n",
    "    laser_detuning = parameters[\"laser_detuning\"]\n",
    "    duration = parameters[\"duration\"]\n",
    "\n",
    "    result = optimize_single_drive(\n",
    "        segment_count, drive_g, laser_detuning, duration, robust=True\n",
    "    )\n",
    "\n",
    "    all_results[\"Robust\"][key] = result\n",
    "    print(f\"Drive strength: {drive_g / 1e3:.2f} kHz\")\n",
    "    print(f\"Gate duration: {duration * 1e6:.2f} µs\")\n",
    "    print(f\"Infidelity: {result['output']['infidelity']['value']:.3e}\")\n",
    "    print(f\"Cost: {result['cost']:.3e}\")\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We extract the controls and *transformed* phase-space displacement plots, as above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-11T06:09:59.412046Z",
     "start_time": "2021-06-11T06:09:58.864092Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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gjXHjK3wcwJ0P3cafy9excc1mfvtlBXq9nuCQIJq3auYcd6/Maz5f3UaTkbEvjWHhzF/5ZdZi5zzkOx+6tcw85Iuh0+t45NkHWb7oD9av2kRmWgZeRi9CwkJocWVzdPrSvxYaNW3AX1t2snzRShx2B3XCgrnptsFlhthqCo0s4SQ8wZkTQ8Y89xDNWjZRuxxVzf9hIVvWbeO9L95SuxThYtJDFsJDnDkhYue23dRrVFftckQVkIN6QniIw/uPMm3yDOqEBp0z31zUDDJkIYQQbkJ6yEII4SYkkIUQwk1IIAshhJuQQBaims346ie+PGuOtxBnyEE9IapZUWERioLzetRCnCGBLIQQbkJODBG11l9bdvLdF9/zygcvEBwSDMDcGT/zz997efLVx53LOJ1t7659/LZwBUkJyWg0ENcgjmG3DSEiunQpobzcfN578QO69+5G/5tKl7ZKPHGKD1//mDsfvJW2na9kxlc/UZBfwEPj7gNKr1e8cOavnEpIRqvVEhYZym333UzU6WtXi9pDxpBFrXVlx9ZExkaybGHp9Zv/WLyK7Rv/4uFnHig3jKF0Dbhefa/h6Tee4PEXx+DtbeKrSVOx2UqvG+3n78vtD9zC77+s4NiheIqLi5n2+Qzad2l7zsX1oXQZoykf/Y8GTRrw/PinGff6WHr1uabGXoBdVEx6yKLW0mg0DBxxI19O/JqQsBCW/7KCR194+LyrlcDp5a7Octv9N/PMAy9y/MgJ58KxzVs34+rrujH9i+9p1KwhthIbI+68qdznsxRZKCosomXbK5yrMte0hTtF5Ukgi1qteaum1K0fx+K5S3ngqXup2yAOKF1zb+a3c5ztHn7mARo1bUBaSjqL5y3l+JET5Ofm41AUFEUhKyOrzPMOGjWQvbsOsGXdNp569fEyC8+ezexrpvPVHfn8gyk0uaIxTVs05sqObQgOCaq6Fy3clgSyqNUO/HOIxBOnUBSlzDBFq3YtqNcoznn7zEXev5o0lcCgQEbdPYLAoAC0Oi3jn38fm63sysqZ6ZlkZ2Sj0WhIT8uo8GJAtz9wCz37XMO+3fvZveMfFs1Zwv1P3EPz1s1c/GqFu5OBKlFrJRxPZOon3zL8zqG0bt+SX2Yvdt5n8jYRGh7q/Ofl5UVBXgEpp1K5YdC1NGvZhIjocKwWKw67o8zz2m12pn/+PS3btWDILQOZPW0emelZ/919GTF1o7l+wLWlKzA3b8TmdVur5DUL9yaBLGqlzPRMvvjwa3r360HXHp3pf1NfDuw5yKF9h8/7GG+zN75+Zjas3kRaShqH9h1m5rdz0OrK/hgtmreU/Lx8Ro0eTs8+11CvYRwzvvoRh8NxznOmp2awcNYijh48RmZ6Jgf3HipdNirq4pdEEp5PAlnUOgX5BXz+wRRatb2CfkNLp6ZFxUZyZac2ZXrJ/6XVahk95k5OnUjinRc+YM70+QwY1s+5Dh2UXkh/5dLV3PHgrfiYSxd+vf2BW0hOTGHFopXnPKeX0YvU5DT+99l03nrmXb6f8hMdurY/78KqomaTE0OEEMJNSA9ZCCHchASyEEK4CQlkIYRwExLIQgjhJiSQhRDCTciZehdw6GQaBr1O7TKEEDVEic1O49jyr5cigXwBBr2OepHBapchhKgh4pMyz3ufDFkIIYSbkEAWQgg3IYEshBBuQgJZCCHchASyEEK4CZllcRkURSE7v4j8QisOh4JcpUl9GsDLoCc0yBe9TvobwrNIIF+G1Kx8NEBEHX/0Oi0ajUbtkmo9RVHILbCQlpVPZIi/2uUIcVGkC3EZLNYSQoJ8Meh1EsZuQqPR4G82UVxiU7sUIS6ax/eQC/IL+HHqLPbvPojZz8ygkf3p0K19uW1Pxicw7/sFnIxPwGj04oZB19GzzzWXvG8F0EoQux2NRiPDR8IjeXwgz54+H51ezzuT3yDheCJfTpxKdFw0kTFll8DJz8vn8wlTuOm2wVzZqQ12m43szByVqhZCiHN59JCF1WJl59ZdDBjWF6PJSMOmDWjVrgVb1m87p+3KpWto3ropHbu3x2DQY/I2EREdrkLVQghRPo/uIacmp6HVaQmLDHNui46N4vD+I+e0jT98nKjYSCa98SlpKenUaxjHiLuGERwSVJ0l1zqzp88jKSGZsS+NUbsUIdyeRwey1VqMydtUZpvJx4TFYj2nbXZWDgnHExjz3ENExUSycOavTPt8Bk+9+vg5bdev3Mj61RsB6H/bcJCLC6nu7607WTx3Gemp6YSEhTBgRD/adGitdllCuJRHB7LR6IWlyFJmm6XIislkPKetwaCndftW1G0QB0C/oX14/pFXKCoswtvHu0zb7r270r13V6DiKzOJ6nHsUDzffjaD/jf1oU2H1uzctov//d93PPnKY9RrVFft8oRwGY8O5LCIUBx2B6nJaYRFlF5fNPHEKSL+c0APIDouquzUtFo8OeKT8ZOJiArHYDSw+c8taLVa+gy+nu69u/HzDwvZtnE7Jm8TA4b3p9NVHZyPO3XyFPN+WMixg8cweBlo2bYlw+8Y4vyF5nA4WDhzERvXbAag89UdUBxl5zsoisIfi1exftVGcrJyCAkP4foBvenYvQPns+q3P2ncvBF9Bl8PQET09Rzcd5hVv/3J3Y3ucPXbI4RqPDqQjSYjbTq0YvG8Zdx670gST5xi94495Q5DdL66E998Oo0eN1xNZHQEyxYsp0GT+uf0ji/Xw36pLn2+yvoiL+zCjc6ybcN2evXrwbjXn2D3jn+Y9/0C9u7azxWtm/HMm0+yee02fvpmFk1bNiEg0B+rxcrkCVOo2yCOp994goL8Qn763xx++Hom9429G4CVS1azYfUmbrl3JFGxkaxdsZ5tG7YTUy/Gud9Fc5fy95adjLjrJsIiwog/HM9P38zB2+xDyyuvKLfW+MPxXHP9VWW2NW/VlD+Xr7/Id0kI9+bRsywARo4eRklxCS+OeY1pn89g1OhhRMZEcPjAUcbd97yzXdMWjRk4oj9fTvyaF8a8SnpKOqMfuV3FytUVERNB/5v6EhYRSu9+PfD1M6PT6ejZ5xpCw0PpN+QGFAWOHjwGwLaNOyi2FnPnQ7cSFRtF4+aNuOWeEezctpu0lDSgtCd73Y29aNf5SiKiwhl2+xD8Av89W85qsbJq6WpuuW8UV7RuTkhYHTp0a0+3Xp1Zu3zdeWvNzc7DL8CvzDa/AD/ycnKr4J0RQj0e3UMGMPuaeeDJe87Z3qhpAyZOfa/Mtquv687V13Wv0noutqeqlujYSOfXGo0GX39fos7aptPr8DF7k5ebD0DKqVSiYiPLHESt37geGo2G5MQUfP18yc3OpV6jes77tVot9RrGkZWRDUDyqRRKSmx8MWFKmSEjh91OcIgcOBXC4wNZXBqtruw6gRo06P57MR6NBkVxVOLZKjcgf2Y8+YGn7iU4JLDMfTrd+dct9A/0Iy8nr8y2vJw8/ALkWhWiZvH4IQtRPcKjwkhKSCozq+XYoXgURSEiOgxvH2/8A/2JP3Lceb+iKBw/csJ5OyI6HL1BT1ZGFqHhoWX+VdRDrteoHgf2HCyz7cCeg9RvXM91L1AINyCBLCqlY7f2GLy8mPHVj5w6eYrD+48w839zaNOhFaHhpTNceva5mj8Wr+SvLTtJSUpl3vcLyM3+d5zX5G3i2n49+fmnX9i4ZjNpKWkkHE9k3R8bWL9y43n33fOGqzm49zC///oHyadS+P2XFRzcd5hel3EdEiHckQxZiErxMnox5tkHmPf9Qj587WP0BgOt2pVOezujd7+e5Gbn8dM3swDo2L0DHbq1J/lUirPNjcP74Rfgx8olq5k9bS4mbxPRcdFcd2Ov8+67QZP6jB5zB4vmLmXJvGWEhNfh7jF3yhxkUeNoFEWRC2NVID4pk3rnOVOvovuEuuR7I9xVRZ9NGbIQQgg3IYEshBBuQgJZCCHchASyEEK4CQlkIYRwExLIQgjhJiSQhRDCTUggCyGEm5BAFkIINyGBLKrU7Onz+GT8ZLXLEMIjyLUshNtLSkhmyfxlnIxPICMtk35Db6D/TX3VLksIl5MesnB7xcXFBIcEM2B4P+qEyvUpRM0lPeRayNMWOa3bIM65Wvjvv/zh6rfDLSiKAjYbDmsxSnExSrEVR3ExyunbDqv19PbSr7U+PnjFRGOIjETr5aV2+cJFJJBd7EDv61XZb9OVyy+qvSctcuqpFLsd67FjWPbuo2jfPkqSklGsxThKilGs1n/D9nTQ4qjM6iz/odViCA/HKzYGQ3Q0XjHReMXE4BUbgz40FE0FK7EI9yOBXEudWeQUoHe/HqxY9IdzkVOAfkNuYMWilRw9eIy2ndqUWeT0zLp6t9wzgk/f+Zy0lDRCw0PLLHIKMOz2IezbfcC5zzOLnD7y3EM0atoAgJCwOhw/eoK1y9d5fCDbMjMp2rvPGcCWAwdRLJYLP/AMnQ6NlxdaoxGNl9fpr0v/1xiNaE9v03h54cjPp/hkAiUpKZQkJVGSlARsLfN0GoOhTEgbzoR1TAy6oEA0msotvSWqjwSyi11sT1Utssjp5XEUF2M9coSif/Zh2bePor37sKWknNPOEBWF6YrmeDdvhlfdumi9TaVB62VEY/QqG8CX0Jt1FBeXBnJCIsUJiRQnJDj/2TMyKY6Ppzg+/pzHac0+p0M6Bp8r2+B39VXo/GWNQrVJINdSnrTIqdoURcGWklra6z3d+7UeOoxSUlKmncbbG+/mzTA1b473Fc0xNW+GPjCwSmvTenlhrFsXY91zV09xFBaWhnRiaVCXnEwovX3yJI6CAiwHDmI5cJC8P1aS8vGnmDu0x69XT3y7d0NnNldp3aJ8EsiiUsKjwtj052YsRRZnL/l8i5w2bdEY+HeRU//A0p7X2YucnmnjrhSHg8LtO8hZspTC3XuwZ2ae08arbt3S4L2iNIC94uLcasxW6+ODqUljTE3KvteKomDPySntSR89Rt669RTu+IuCzVso2LwFjcGAuXMn/Hr3wrdLZ7Qm03n2IFxNAllUSsdu7Vky/zdmfPUjNw7rS2FBUbmLnC7/9Q/CIkKdB/Vys3OdgXz2IqeKotCoWQOslmLiDx9Ho9HQvXfXcvdts9lITiwdDigpKSE3J4+E44kYTV7OfbuKPb+A3N9+I2vhL5QkJDq3a/398G5+OnybN8fUrCk6X1+X7ru6aDQa9IGB6AMD8WnZksBBA7FlZZH35zryVq2iaPce8tetJ3/dejQmE75du+DXuxfmjh1kRkcVk0AWlaLmIqc5Wbm8//JE5+30lRtZv3IjjZo1ZOxLY1zy+qzHjpG14Bdyl69wHojTh4USOGgQfldfhSEmukYfBNMHBRE0eCBBgwdSkpZO3po/yVu1Csu+/eStWk3eqtVozWZ8r+qOf6+e+LRri0Yv8eFqssjpBcgip56pMt8bxWYjf/0Gshb+QtHfO53bfdq1JXDIYHy7dnGrIQg1FCclkbd6DXmrVmM9fMS5Xefvj2+Pq/Hv1QvvVi1r/ft0MSr6bEogX4AEsmeq6Htjy8wiZ/ESsn9dhC09HSg9IBdww/UEDh6Esd65B8gEFJ84Se7q1eStXEXxiZPO7brgYPx6XkPQkMF4xcRU8AwCJJAviwSyZ/rv90ZRFCz79pG14BfyVq8Bmw0Ar7hYAocMxv/662RmQSUpioL16DHyVq0ib9Wa03OgAZ2OoCGDqXPHbTKFrgISyJdBAtkznfneOKxW8latJmvBQqwHD5XeqdXi27UrgUMH4dO2bY0eG65qiqJgOXCQ7IW/kPv7clAUtH5+1LnjdoIGD0RjMKhdotuRQL4MEsieKT4xHfPiBeQsWYo9NxcoHfcMuLE/gQMHYIgIV7nCmsdy+DBpX3xF4V9/A2CIjiL0gfvxvaq7/NI7iwTyZajozTuelElcRJB82NyIoijYcnI5Hp+E/anHADA2aUzQ0CH49eop07aqmKIoFGzaTNpXU5zjzN6tWxH2yEOYmjRRuTr3IIF8GSp68xJTs6kT6IvJS6b/uANHSQm2lFSKCwpJTs/B+Ms8QkbfialZM/mlWc0Um43sRYvJmPad8y8U/+uvI+S+ezCEunbuuKep0YFckF/Aj1NnsX/3Qcx+ZgaN7E+Hbu3P295ms/HeSx9iLbLy1qevXfD5K3rzCoqsZOYWERrki9Ggkx96lZSeeZaLLS0NxeEgw65Bl59PRMcr5XuiMnt+Phk//ET2/J9RSkrQGI0EjxxO8M2j0Hp7q12eKirKFI/v2s2ePh+dXs87k98g4XgiX06cSnRcNJExEeW2/2PxKnz9fLEWWS9732ZvIwAZ2fnY7A48+jebp7LbsefmoliLAdAYvfAJ9CeieSMJYzeg8/Ul7MH7CRw4gPSvp5K35k8yZvxA9uKlhNwzmoA+N8gc5rN4dCBbLVZ2bt3Fi+8+g9FkpGHTBrRq14It67cxeNSAc9qnp2awdcN2ht46mJnfzHZJDWZvozOYRfVRHA6yf/mVtClTUSwWdP7+hD3+KH69ekoQuyGvqEiiXnuFwj17SPviKyz79pPy4SSy5y8g9OEHMbdvp3aJbsGjAzk1OQ2tTktYZJhzW3RsFIf3Hym3/dwZPzNwRH+8vCqeirN+5UbWr94IQP/bhoPMpHArxYmJJH8wkaJduwHw69mDsMfGoA8KUrkycSE+LVsS99mn5K1aTdqUqViPHiXhmecwd+5E6EMPlHvVutrEowPZai0uc31eAJOPCYvl3OGIndt24XA4aNOhNYf2Ha7webv37uq80E180rlX+RLqUOx2sub/TPr/pqFYreiCgggf+xh+11ytdmniImg0Gvx798K3ezey5v1M5o8/lV5pbvsOIp56goC+fdQuUTUevcip0eiFpajsigyWIismU9khBKvFysKZixh+x9DqLE+4kPX4cU6MfZK0L75CsVrxv/466n87VcLYg2mNRurcejP1Z0wjoF9fsNlInvAhqV99jWK3q12eKjy6hxwWEYrD7iA1OY2wiNKpNIknThHxnwN6aSnpZKRn8vHbnwFgt9koKrTw4qOvMe61sbKScTWw5eRw6pXX8OvZk6CbhlT6cYrdTuas2WRMn4FSUoI+JITwp57At0vnqitWVCt9UBARz4zD1KwpKZ9+Rtas2RQfP0HUyy+g9fFRu7xq5fHT3r797DvQaLj13pEknjjFFx9+zVOvPl5mloXdbqcgr8B5++iheOZ8N5/n3noKX39ftNrz/6EgZ+O5RvJHH5Pz62Kg8stcWY4cJXnCh1gPlZ7yHHBjf0IffACdr1xzoqYq/OtvEl9/E0deHl716xEz/i0MEeXPmPJUFWWKRw9ZAIwcPYyS4hJeHPMa0z6fwajRw4iMieDwgaOMu+95oHR5IP9Af+c/s68PWo0G/0D/CsNYuE7hth3OryvTB8hd8QfHHx6D9dAh9OHhxHzwPhHjnpQwruF82l5J3c//D6+4WIqPxXP84Ucp3L1H7bKqjcf3kKua9JAvn/X4ceLvvs95u+HcWeiDz/+eZi9eQsqkj0FRCBg4gLCHHqi1JxHUVvb8fE69NZ7CrdtAr69RB/tqdA9ZuL+8lavK3D5zDeLyZM3/mZSJH4GiEHL/vUQ8OVbCuBbS+foS887bBN009N+DfV9OqfEH+ySQRZVSFIXcP84KZK0WQ3R0uW0zfpxJ6mefAxD26Bjq3HJzdZQo3JRGpyPs0UcIf+oJ0OnImj2HxFdex15QcMHHeioJZFGlrEePUnLqlPO2sVHDcy4ErygK6d9OI33qN6DRED7uyYuaiSFqtsABNxL7wfto/f0o2LSJE489QfGZi+LXMBLIokpZjxwtc9undesytxVFIe3LKWTM+AG0WiJfeI7AG/tXZ4nCA/hc2Ya6k08f7IuP58Qjj1F4+kzNmkQCWVSp4pMny9z2bt3K+bXicJD6yf+RNWcu6PVEvfoy/tddW90lCg/hFR1N3Gf/h7lTR+w5OZx8+llyli5TuyyXkkAWVersxTABvFu1BEpP+Ej+YCLZv/yKxmAg+s3X5aw7cUE6XzPR498iaNhNpQf7PphI6hdf1ZiDfRLIokqdHche9eqhDwhAsdlIeuc9cn/7HY3JRPS7b8uZd6LSNDodYWMeJvypJ0sP9s2ZS9J7Eyo1v93dSSCLKqPY7RQnJjpv+7RuhaO4mFNvvEXeqtVofXyIef9dzO3k0ovi4gUO6E/sh++j9fEh74+VZMz4Xu2SLpsEsqgytrR0sNmct01Nm5D48qvkr9+A1s+PmA8n4HN6CEOIS+HTpg2RL78IGg0Z074jb/UatUu6LBLIosro6wSj9fV13k6f/h2F27ajCwwkdtKHeDdrqmJ1oqbw7dKZ0AfvByDp/Q+wHDyockWXTgJZVBmNwYBv967O27bUNPR16hD78URMDRuoWJmoaYJGDMe/bx8Uq5XEl1/DlpGhdkmXRAJZVCm/Hj2cX+vDw4n9eBLGuDgVKxI1kUajIfyJx/Fu1RJbejqJL7+Gw3r562ZWNwlkUaXOXist4qkn8IqOUrEaUZNpvbyIeuM1DBERWA4cIHnChx4380ICWVQpjcGAf58bACjat0/lakRNpw8MJHr8m2i8vclbtZrM739Uu6SLIoEsqpxfj2sAyFvzp8qViNrAWL8+UadnXqR/O428P9eqXVKlSSCLKmdu3w6try/Fx+KxHj+udjmiFvDt2oXQ+0uvwZ303gQshype2NhdSCCLKlc626IbIL1kUX2CRo3Av8/1KBYLiS+/6hEzLySQRbXw61k62yJvtQSyqB4ajYbwJ5/Au2ULbGlpJL76Oo7iYrXLqpAEsqgW5nZtS4ct4mXYQlSfMzMv9OHhWPbtJ/mDiW4980ICWVQLjcGA71XdARm2ENVLHxRE9NunZ178sZLMH39Su6TzkkAW1cY520KGLUQ1MzVsQNRLL5TOvPjmW/LWrlO7pHJJIItqY27XFq2fX+mwRbwMW4jq5dutKyH33wtA8oeTsOfmqlzRuSSQRbWR2RZCbcGjRuLT9koceXmly4a5GQlkUa38z8y2kEAWKtBoNIQ+/CBoNGQtWEjxyQS1SypDAllUK5+zhy2OHlO7HFELmRo1IqBvH7DbSZsyVe1yypBAFtVKo9c75ySnfzdD5WpEbRVy911oTCby16+n8O+dapfjJIEsql2d229DYzSS/+daCnfvUbscUQvpQ0IIvmUUQOkiqQ6HyhWVkkAW1c4QGkLwyOEApLnRD4OoXYJHDEcfEoL10CFyV/yhdjmABLJQSfDNo9AFB2PZv5+8VavVLkfUQlqTiZD77gEgfer/cBQVqVyRBLJQidbbm5C77wIgber/3P4aA6Jm8r/uWoxNGmNLTydzzly1y5FAFuoJ6NsHrwb1saWkkDVvvtrliFpIo9US9vBDAGTOnI0tPV3VevSq7t0FCvIL+HHqLPbvPojZz8ygkf3p0K39Oe1WLF7JlrXbyMzIwuxr5urrunHdjb1VqFicodHpCHvwARKee4HMH38ioF9f9IGBapclahmfNq3xvao7+evWk/a/aUQ++7RqtXh8D3n29Pno9HremfwGdz18G7OmzSMpIfnchgrc8dCtvP/l2zzy7AP8uXw92zf+Vf0FizLMHTtg7tQRR0EhGdO/U7scUUuFPnAf6PXk/va7qhez9+hAtlqs7Ny6iwHD+mI0GWnYtAGt2rVgy/pt57S9bkBvYuvFoNPpCI8Mo3W7Fhw9JCcmuIPQBx8ArZbsXxfLpTmFKrxiYggaMhgUpXTmj0qX6PToQE5NTkOr0xIWGebcFh0bRXJ5PeSzKIrCkYPHiIyOqOoSRSUY69cjoH8/cDhI+8q9zpwStUedO25D6+9H4d9/U7Bhoyo1eHQgW63FmLxNZbaZfExYLNYKH7dk/m84HA46X9Op3PvXr9zIhFcnMeHVSeTn5rusXnF+IaPvQuPtTcGmTRTskKEkUf10fn6E3HkHAKlfTUEpKan2Gjw6kI1GLyxFljLbLEVWTCbjeR+zZvlatqzbxkNP34/BUP4xze69u/Lsm0/x7JtP4evv69KaRfn0wUHUOX3mVNqXX6HY7SpXJGqjwEEDMcTEUJKQSPavi6p9/x4dyGERoTjsDlKT05zbEk+cIiKm/KGIjWs2s+LXlTz2wsMEBQdWU5WisoKGD0MfGor18BFyl69QuxxRC2n0esIevB+A9OkzsBcUVOv+PTqQjSYjbTq0YvG8ZVgtVo4ePMbuHXvo1L3DOW23rt/Or3OWMOa5hwgJq6NCteJCtCYTIffeDUD6N9+6xZlTovYxd+uK6YorcOTlkb9+Q7Xu26MDGWDk6GGUFJfw4pjXmPb5DEaNHkZkTASHDxxl3H3PO9stmruUgvwCPnjtI8bd9zzj7nuemd/OUbFyUR7nmVMZGW5x5pSofTQaDQE3XAdA3spV1btvxZ2XYHUD8UmZ1IsMVruMWqVw505OPvk0GpOJ+tO/xRAaonZJopaxZWdzZPgo0GhoNHc2ugB/lz13RZni8T1kUfP4tGmDb/fuKBYLp958S65zIaqdPjAQn3ZtwW4nb131LYgqgSzcUviTj6MPDcXyz15SPvpYtYn6ovby79UToFqvRiiBLNySPjiY6LffRGMykfvbcrJmy3iyqF6+V3UHvZ7Cv3diy8yqln1KIAu3ZWrciMjnnwUgbcrX5G/arHJFojbR+flh7tABHA7y1q6tln1KIAu35nfN1dS5+y5QFJLefgfrsXi1SxK1iF+v06ukr1xdLfuTQBZur87tt+HXqyeOwkISX34VW06O2iWJWsK3W1c0BgNFe/ZQklb110qWQBZuT6PREPHMOIxNGlOSlMSp199U5ToDovbRmc2Yu3QGRSFvzZ9Vvj8JZOERtCYT0W+9ia5OMEU7d5Hyf5Nl5oWoFn69egKQt6rqTxKRQBYewxAaQvSbb6Dx8iJn0WKyf16odkmiFvDt3AmNyYRl335Kkiu+tO/lkkAWHsW7eTMinhkHQOrnX1Cw9dzFCIRwJa23N75duwCQu3pN1e6rSp9diCrgf21vgm+7FRwOTr35NsUnTqpdkqjhnMMWVTzbQgJZeKSQu+/Ct3t3HAUFJLz8Kva8PLVLEjWYuVNHtGYfrIcPU5yQUGX7kUAWHkmj1RL54nMYGzagJCGBU2++LRe1F1VG6+WFb/fuAOStqrphCwlk4bG03t6lMy+CAincvoPUz79UuyRRg/n1LD1JJLcKr20hgSw8miEinOg3XkdjMJD98wJSP/tcesqiSpjbt0Pr70dxfDzW+KpZHV0CWXg875YtiHj+WdDryZr/M4kvvYI9v3qX3hE1n8ZgwNy+PQCW/furZB8SyKJG8O/Vk9gPJ6Dz96dgy1ZOPDaW4lNJapclahiv2FiAKjuwJ4Esagyf1q2I+/wzvOrWpfj4cU488iiFO3epXZaoQbxiYwAoPimBLMQFeUVFEvfZJ5g7dcSem8vJZ54jZ+kytcsSNYQzkKWHLETl6Mxmose/RdCwm8BmI/mDiaR+OUUO9onLZoiOBqAkIRHF4XD580sgixpJo9MRNuZhwp96AnQ6smbPIfHV13EUFqpdmvBgOrMZXXAwSkkJttQ0lz+/BLKo0QIH3EjshPfQ+vlRsHETxx9/gpLkFLXLEh7s33Fk15+yL4EsajyftldSd/KneMXGUnz0GMfHPErRP3vVLkt4KK+YqhtHlkAWtYJXTAxxkz/Fp3077FnZnHzqaXKWr1C7LOGB/g3kRJc/twSyqDV0vr7EvPcOgYMHoZSUkPzu+6R9878qOTgjai4ZshDCRTQ6HeFjHyPs8UdBqyXzh5849fqbOIqK1C5NeIgzPeQS6SEL4RpBQwYT8947aM1m8tet59hd95Dz+3LpLYsLMkRGgFZLSUoKjuJilz63BLKotcwd2hM3+VOMjRtjS08n+b0JnBjzGIW7dqtdmnBjGoOhNJQVhZJE1/aSJZBFrWaMi6PuF58R8fyz6OvUwXLgICefeIrE19+kOPHUOe0nTJjAqktY7HLVqlVMmDDBFSULN+C8poWLT6GWQBa1nkarJeCG66n/3bfUuetONCYT+X+uJf6e+0j9cgr2/HygNIz1ej0jR468qFCeNGkSAwYMoGPHjlX1EkQ1q6qZFhLIQpym9fYm5K47qD/9W/z73IBis5E1ew7Hbr+LrAUL6dCuHe+++y4vvPBCpUN50qRJPP3007z11lv06tWrGl6FqA5eMaWnULt6LrIEshD/YQgNIfK5Z6j7xWS827TGnptL6qefUf/7H5n++huVDuUzYfzhhx/y1FNPVeMrEFXt3yEL105907v02VRQkF/Aj1NnsX/3Qcx+ZgaN7E+Hbu3PaacoCr/MWsSGNZsB6NajM4NGDUCj0VR3ycJDmJo0JnbSh+Sv30DaV1MoPnGShidO8lnva3l0/HheeOklRo4cyezZs8/p/UoY12yGKpr6dkk9ZKvFitVidWkhl2r29Pno9HremfwGdz18G7OmzSMpIfmcdutXbWTX9j08P/5pXhj/NHv+2sv6lRtVqFh4Eo1Gg99V3an/v6mEPvwQWl9frkzL4MO6DRj/6qu89MQT5/SUJYxrPn1IHTQmE/acHOcxBpc878U0XrVsDauWrSE7MweAgKAAevXtQa++16jS07RarOzcuosX330Go8lIw6YNaNWuBVvWb2PwqAFl2m5Zu43e/XoSFBwIQO9+PdiwehNXXdut2usWnkMpKcGem4s9JxdTo4aE3ncPKZ99TpfAQCY1bsqTb7zBRy1aUfTSK2zv1Ys1dYIkjGsBjUaDxsuAYrG49LKulQ7kBT/9yobVG7m2fy/qNaoHQPzheJYt+J3c7FyG3DLQZUVVVmpyGlqdlrDIMOe26NgoDu8/ck7bpMRkouOi/m0XF01SYvlX/Vq/ciPrV5f2nvvfNhwig11cuVCbw2KhaM8/lJw65Qxce07Ov1/n5mDPya3wcp1dgoL5qEUrnvxnNx+1aMXmuXN48+ABJk6cKGFcCyjW0pNCtF5eLnvOSgfyxjWbuOXeUbTt1Ma5rWmLxoRFhjHz2zmqBLLVWozJ21Rmm8nHhKWc4RSrxYrJx1SmndViRVGUc3r33Xt3pXvvrgDEJ2VWQeWiuil2O5ZDhyncvp3C7X9R9M8/KCUlF36gVosuIACdvz+6AP9zvr7h8BE+mg2j/97hfEjbtm2r8JUId6AoCsrps/Q0RqPLnveihiyiYyPL3aY4FJcVdDGMRi8sRZYy2yxFVkymc98go8lYpq2lyILRZJSDejWUcvosqoLtf1G4YweFf/2N4+yxPo0GY5PGmBo1QhcYcFbQBpSGrX/p/1qz+YKfkQwvPZwO5IkTJ573QJ+oOZSSElAUNAYDGq3rJqtVOpA7XdWBP1esZ/gdQ8tsX/vHBjp1P3dWQ3UIiwjFYXeQmpxGWEQoAIknThERE3FO28joCBJPnKJew7rOdpHR4dVar6hatqwsCv/6m8IdOyjY/he2lLJDUobISHzat8Pcri0+bduiC/C/7H1OmjSJp595hokTJ9K2bVtGjhzpnBInoVxzOXvHXgaXPm+lA9lWYmfbxh3s233AGWrHjx4nJyuXDt3aMfe7+c62w++8yaVFno/RZKRNh1YsnreMW+8dSeKJU+zesYenXn38nLadrurAqmVraNGmORqNhpVLV9Pj+qurpU5RNRwWC0W7d5f2grdvx3rkaJn7tf5+mNu2xad9e3zatcUr6ty/8C5HebMpZs+eLaFcCyjW0mFRjZfrhivgIgI5JSmF2HqlZ6dkZZSOq/oH+OEf4EfKqbN7ItU7BDBy9DB++HoWL455DbOfD6NGDyMyJoLDB47yxQdTmDj1PaB0XDg9NYN3X/wAgK49ujjHiYXnsOfnk7N4KfmbN2P5Z2+ZcWCNlxferVqe7gW3w9iooUv/nDzb+aa29erVS0K5FnCcOaDnwvFjAI2iKOoMAHuI+KRM6sksC9XZMjPJmjuf7F9+/Xfmg0aDqUljfNq1w6d9W7xbtnTpEe/zqcw841WrVjlD+d1335VQrmGsx+KJv/d+vOrGUf/bby7qsRVlisefqSdqtuKkJLJmzSFn6TJnb9in7ZUEDByA2UXjwBejsid9SE+5ZlOKVR6yEKI6WY/Fk/nTTHJXroLTF4337d6d4FtH4d28uSo1rVq1ildffbXSJ338N5S3bt0qgVxD/Dtk4dq/yCSQhVsp2ruXzB9nkr/h9GntWi3+119H8M2jMNavp2ptW7du5ddff72oUD0Tylu3buXZZ5+twupEdXIe1HPxGLIEslCdoigUbt9B5o8zKfz7b6D0AF1Av74EjxqBIeLcaYxquNRA7dWrl/SMaxiHc9qb9JBFDaE4HOSvW0/Gjz9hPXgIAK3Zh8BBgwgadhP64CCVKxSifGd6yK6eZSGBLKqdYrORu+IPMmfOovhE6fVkdYGBBA2/icBBg9D5mlWuUIiK/TtkIT1k4aEUh4OcxUvI+OFHbKlpAOjDwggeNYKAfn3RmkwXeAYh3MO/QxbSQxYeyJaZSdL7H1C4dRsAXnXjCL7lZvx790Kjl4+h8CzOIQsZQxaeJn/zFpInfIA9Kxudvz9hYx/Dr8c1VXYWnRBV7cylN2XIQngMR3Ex6VOmkjX/Z6D0hI6I55/DEBqicmVCXB5HsUx7Ex7Eevw4SW+/U3rBH52OkHtGEzxqpPSKRY2gnL6Ur1bGkIU7UxSFnMVLSJ38BYrViiEqisiXX8S7WVO1SxPCZSxHS68saIiJdunzSiALl7Hn5pI88SPy164DwP+G6wl//FG0Pj4qVyaE6yh2O5b9BwDwvsK1p/FLIAuXKNy5k6R33seWlobW7EP4E2Pxv7a32mUJ4XLWY8dQLBYMkZHog1x78pIEsrgsis1Gxnffk/HDj6AomK5oTuRLL+AV6dqLwQvhLor+2QeAqcUVLn9uCWRxyYqTkkga/y6WvftAo6HO7bdR587bZV6xqNEse/cC4N28mcufW35yxCXJ/WMlKR9/gqOgEH1oKJEvPodPmzYXfqAQHq5oX2kP2Vt6yEJtjuJiUj76mNzflgPge/VVRIx7Ep1/9V4oXgg12HJyKElIRGM0YmzQwOXPL4EsKk2x20l6+x3y161HYzQSNuYRAm7sh0ZTvesoCqEWy97T48dNm1TJ0JwEsqgUxeEg+f0PyF+3Hq2vL7ETP8DUuJHaZQlRrYrOjB+7eLrbGXLalLggRVFI+eT/yF3xBxqTiZj3xksYi1rJ2UO+wvXjxyCBLC5AURTSpkwl59dFaAwGot9+E+8q+jAK4c4Uu50i5wkhEshCBZnf/0jWrNmg0xH1xmuY27VVuyQhVGGNj0cpKsIQGVFlq9lIIIvzypw7n/Rvp4FWS+RLL+DbpbPaJQmhGss/pePHVTVcARLI4jyylywl7fMvAIgY9yT+PXuoXJEQ6io6PX7s3bxqDuiBBLIoR+7KVaRM/AiAsEcfIaBfX5UrEkJ9zkBuUXWBLNPehJP1WDzx997vvB1yz2iCbhqqYkVCuAd7Ti4lCQmlJ4Q0bFhl+5EesnA69ebbzq8Dhw4h+LZbVaxGCPdx5nRpU5OqOSHkDAlkAUDh7j0UHz/uvG1q2kTOwBPitCLnAb2qG64ACWQBWA4d5uTYJ8tsy1myVKVqhHAviqKQv249AD6tWlbpviSQazl7fgEJz7/gvB0w4EY0JhNFu3ZTnJCgYmVCuIfCv/6m+Phx9HXqYO7UsUr35bEH9QryC/hx6iz27z6I2c/MoJH96dCtfbltVyxeyZa128jMyMLsa+bq67px3Y2ymgVAwbZt2LOynbfNHTuglJSQ+9vv5Cz9jdD771WvOCHcQPbPCwEIGDSgyq/17bGBPHv6fHR6Pe9MfoOE44l8OXEq0XHRRMZEnNtYgTseupWo2EjSUzOY/P5XBAUH0b6rnHVWuG17mdumxo3RBQaWBvLvvxNyz2g0Op1K1QmhrpLkFPI3bgS9nsAb+1f5/jxyyMJqsbJz6y4GDOuL0WSkYdMGtGrXgi3rt5Xb/roBvYmtF4NOpyM8MozW7Vpw9NCxaq7a/SiKQsFZgaz180MfHoZ3yxZ4xcZiz8ikYPMWFSsUQl3Zv/wCDgd+PXugDw6u8v15ZCCnJqeh1WkJiwxzbouOjSI5IfmCj1UUhSMHjxEZXU5PupYpSUjElprqvG1q3AiNRoNGo8G/Xx8AcpYuU6s8IVTlsFrJPn1wO2jo4GrZp0cOWVitxZi8TWW2mXxMWCzWCz52yfzfcDgcdL6m03nbrF+5kfWrNwLQ/7bhEFn1vxnVULC97HCFsXFj59cBN1xP+tT/kb9xE7bMzGrpHQjhTvJWrsKRm4epadMqPV36bG4ZyJ+Mn8zh/UfKva9Bk/oMv2MoliJLme2WIismk7HC512zfC1b1m3jiVcexWA4/0vv3rsr3Xt3BSA+KfMiq/ccZ04FPcPU6N8zkPTBwfh27UL++g3k/L6cOjePqu7yhFCNoihk/bwAgMBq6h2Dmwby2JfGVHi/1WLFYXeQmpxGWEQoAIknThFR3gG90zau2cyKX1cy9uVHCQoOdGW5HsueWfaXjemsHjJAQL++pYG8ZBnBo0bKiSKi1ija8w/Ww0fQBQbiV40X1vLIMWSjyUibDq1YPG8ZVouVowePsXvHHjp171Bu+63rt/PrnCWMee4hQsLqVHO17suek+P8WmMyYYiJLnO/uXMndHWCKUlIoGjPP9VdnhCqyT7dOw64sT9aL69q269HBjLAyNHDKCku4cUxrzHt8xmMGj3MOeXt8IGjjLvveWfbRXOXUpBfwAevfcS4+55n3H3PM/PbOWqV7jbs2f8Gsl/PHmi0ZT8OGp2OgBtuAOTMPVF7lKSlk7d2HWi1BA4aUK371iiKolTrHj1MfFIm9WrgQT1FUTh4bWnYotfTcOYP5R64K05I4Nidd6MxmWg4ZyY6s7maKxWieqV/O42MGT/ge83VRL/+qsufv6JM8dgesrg8jsJC59ehD9x/3lkUXjExeLduhWKxkLdqTXWVJ4QqHMXFZC9aDEDQ0CHVvn8J5FpKazSiDwvFKzaWoCGDKmwb0L8fIMMWoubLW/Mn9qxsjA0a4N26VbXvXwK5ltLo9TSYMZ1630y54Pn5ftdcjdbsg2X/fqzH5AxHUXOduW5F4NDBqswqkkCuxTQGQ6UulqI1mfC/9loA0qfPqOqyhFBF0b79WPbvR+vnh/+16lx8TAJZVErwbbeg8fYm/8+15G/arHY5Qrhc9oLTV3Xr1xetyXSB1lVDAllUiiE0lJDRdwKQ+ulnOCyWCzxCCM9RnJhI3uo1oNEQOHiganVIIItKC7ppKMaGDShJTibj+x/ULkcIl1AcDpInTEQpKcH/+uvwioxUrRYJZFFpGp2O8CfHgkZD5qw5WI/Fq12SEJcte8EvFO3ejS4oiLBHHlK1FglkcVG8r7iCgAH9wW4n5eNPkfOKhCcrPpVE2tRvAAh/ciw6f39V65FAFhct9L570QUGUrR7N7m//a52OUJcEsXhIPnDiSgWC369euJ3VXe1S5JAFhdP5+dH6MMPApD25RTsObkqVyTExcv+dTFFf+9EFxhI2OOPql0OIIEsLpH/ddfic+WV2HNzSfv6a7XLEeKilCQnk/bVFADCxz6GPiBA5YpKSSCLS6LRaAh/4nE0BgM5S5ZRuGeP2iUJUSmKopD84aTSoYoe1+DX4xq1S3KSQBaXzCsuluCbRwKQ8tEnKDabyhUJcWE5i5dQuOMvdAEBbjNUcYYEsrgswbfegiEqiuJj8WTNna92OUJUqCQllbQvS4cqwh4bgz4oSOWKypJAFpdFazQSPra0l5H+3QxKklNUrkiI8imKQvKkj3AUFuJ7VXf8evVUu6RzSCCLy2bu2BG/nj1QLBZSP5usdjlClCt32W8Ubt2G1t+v9PiHG64RKYEsXCLskYfQ+viQv2Ejees3qF2OEGWUpKWR+vmXAIQ/Oua8CzKoTQJZuIQ+JISQe+4GIPX/PiuzIokQalIUhZRJH+MoKMDctQt+Kl1aszIkkIXLBA4eiLFJY2ypaSS+/iZKSYnaJQlB7vIVFGzegtbXl4gnx7rlUMUZEsjCZTQ6HVGvvIQuMJDCbdtJ+mAiisOhdlmiFrOlp5P62ecAhI15GH1IiMoVVUwCWbiUV3Q0Me+NR+PtTd6KP0j7copcgEiowl5QQOIrr+PIz8fcuRP+N1yvdkkXJIEsXM7UpAnRb7wGej1Zc+eRNWuO2iWJWsZRVETiiy9jOXAAQ2QEEU8/5dZDFWdIIIsqYe7QnsjnngEgbcrX5Py+XOWKRG3hKC4m8ZXXKNq9B31oKLETP0Bfp47aZVWKBLKoMv7X9iZszMMAJE/4UNbiE1VOKSnh1Otvlp4aHRxM7MQJGCIi1C6r0iSQRZUKGnYTwTePAoeDU2++TdG+fWqXJGooxW7n1Ph3Kdi0GZ2/P7EfvI9XTIzaZV0UCWRR5ULuvxf/PtejWCwkvvAy1hMn1C5J1DCK3U7S+x+Q/+datGYzMR+8j7F+PbXLumgSyKLKaTQaIsY9hblLZ+y5uSQ8+wIlaelqlyVqCEVRSPnoE/JW/IHG25uY99/B1LiR2mVdEglkUS00ej1Rr76M6Yrm2FJTSXj+Bex5eWqXJTycoiikTv6cnCVL0RiNxLzzFt5XXKF2WZdMAllUG63JRMz4t/GKi6X4WDyJL7+Gw2pVuyzhoRRFIX3q/8ievwCNwUD0m6/j06aN2mVdFglkUa10Af7EvP8e+pAQinbvJuntd1HsdrXLEh4o8/sfyfxpJuh0RL32CuaOHdQu6bJJIItqZwgPI+b9d9H6+pK/fj0pH38qZ/OJi5I5ew7p304DrZbIl17At1tXtUtyCb3aBVyOgvwCfpw6i/27D2L2MzNoZH86dGtf4WNsNhvvvfQh1iIrb336WjVVKv7LWL8e0ePfIuGZ58hZvASdny8h992LRit9BFGxrIW/OFf9iHhmHP49e6hcket49Kd/9vT56PR63pn8Bnc9fBuzps0jKSG5wsf8sXgVvn6+1VShqIhPq5ZEvfISaLVkzpxN4kuvYM/NVbss4cZylv1G6if/B0D4E48T0OcGlStyLY8NZKvFys6tuxgwrC9Gk5GGTRvQql0Ltqzfdt7HpKdmsHXDdq4feG01Vioq4tu9G9Hj30Lr70fB5i3EP/gIRfv2q12WcDOKopC9ZCnJH04CIPThhwgcNFDlqlzPYwM5NTkNrU5LWGSYc1t0bBTJFfSQ5874mYEj+uPlZajwudev3MiEVycx4dVJ5Ofmu6xmUT7fzp2o9+UXmJo1w5aSwomxT5L18wIZVxYA2PMLSBr/LikfTgKHg5C7RxM8YpjaZVUJjx1DtlqLMXmbymwz+ZiwWMqfRrVz2y4cDgdtOrTm0L7DFT53995d6d679CBBfFKmawoWFTJEhBP3ySRSv5xC9s8LSP2/yRTt+YeIcU+i9fFRuzyhksI9e0ga/x62lBQ0JhPhj40hoF9ftcuqMm4byJ+Mn8zh/UfKva9Bk/oMv2MoliJLme2WIismk/Gc9laLlYUzF/HQ0/dXSa3CNTQGA+GPjcG7ZQuSP5xE3qrVWA8fIer1VzDWr692eaIaKXY7GTN+IOP7H8DhwNS0CZEvveBx16a4WG4byGNfGlPh/VaLFYfdQWpyGmERoQAknjhFRMy5V3ZKS0knIz2Tj9/+DAC7zUZRoYUXH32Nca+NpU6oey54WFv59+qJqWFDEt94k+Jj8Rx/5DHCnxxLgAdcYFxcvpLkZE6NfxfLP3tBoyH4lpsJGX0nGkPFQ401gUbx4IG6bz/7DjQabr13JIknTvHFh1/z1KuPE/mfULbb7RTkFThvHz0Uz5zv5vPcW0/h6++LtoKpVvFJmdSLlMBWg8NiIeXjT8k9fS3lgBv7E/bYGLReXipXJqpK7h8rSfn4ExwFhehDQoh84Tl82l6pdlkuVVGmeOxBPYCRo4dRUlzCi2NeY9rnMxg1epgzjA8fOMq4+54HQKfT4R/o7/xn9vVBq9HgH+hfYRgLdWlNJiKee4bwcU+iMRjIWbyEE48+TnHiKbVLEy7mKCwk6b0JJI1/F0dBIb5Xdafe11/VuDC+EI/uIVcH6SG7B8uhw5x64y1KTp1CazYT8dwz+F3VXe2yhAsU7dtH0tvvUpKUhMZoJGzMwwTc2N8jlly6FBVligTyBUgguw97fgHJEz4gf916AIJGDCf0/nvR6N32UIiogGK3k/nTTNKnfQcOB8ZGDYl8+UWMcXFql1alJJAvgwSye1EUhay580ibMhXsdrxbtiDylZcwhIaqXZq4CCWpqSS98x5Fu3YDpb9cQ+69u1YcH5BAvgwSyO6pcM8ekt4cjy09HY3RSOCQwQSPGoE+MFDt0sQF5K35k+SJH+HIz0cXHEzkc8/UiCu1VZYE8mWQQHZftuxsUj76hPy16wDQmEwE3TSE4BEj0AX4q1yd+K+S5GTSp39H7m+ls2bMXboQ8ey4WvdLVAL5Mkgguz/LgQOkT59BwelVrbU+PgQNG0rQ8GHo/PxUrk5Yjx0j86dZ5K5cBQ4HGi8vQh96gMDBg2rsgbuKSCBfBglkz1G0bx/p076jcGvpBaa0ZjNBI4YRdNNN6HzNKldX+xT9s5eMH3+iYOOm0g1aLf7X9qbObbfiFRerbnEqkkC+DBLInqdwzx4ypn1H4Y6/AND6+RE8cjhBQ4fIdTGqmKIoFG7dRsZPMynauQsAjZcXAf37ETxyOIaIc8+krW0kkC+DBLLnKty5k/RvpzuP5Ov8/Qm6eSRBgweh9fZWubqaRbHbyftzLZk/zcR6uPQaNFqzmcAhgwi6aSj6oCCVK3QfEsiXQQLZsymKQuFff5H+7fTSayMAuqBAgm8eReDAAWhNpgs8g6iIo7iY3N9XkDlrFiWnz6DUBQcTNPwmAgcOQGeWoaL/kkC+DBLINYOiKBRu20b6tO+wnL4Avi44mDq33kzAgBtrxfxXV3IUFpL962Iy587FnlF6iVpDZCTBN4/Ev88N8n5WQAL5Mkgg1yyKolCweQvp06ZjPXgIAH1ICP59bsC3a2dMTZui0elUrtJ92bKzyZ6/gKwFC3Hkly7eYGzQgOBbb8avxzXy3lWCBPJlkECumRRFoWDDxtJgPnLUuV0XGIi5cyd8u3bBp307+ZMbcBQVUbR7D/kbN5Kz7HcUa+kiEN6tWhF8yyjMnTvVyulrl0oC+TJIINdsisNB4V9/k79hIwUbN1GSfNYSYHo9Pm1a49u1C+YuXfCKilSv0Gqk2GxY9h+gYMcOCnf8RdHefWCzOe83d+lC8K2j8GnZUsUqPZcE8mWQQK49FEWhOP44+Zs2UbBpM0X/7AWHw3m/V926+HbtjLlLF7xbXFFj/jwvfd3xFGz/i8Idf1G4cydKUdG/DbRaTI0b49OuLX69e2Fq2EC9YmsACeTLIIFce9lycijYspWCjZso2LoVR0Gh8z6tvx/mTp3w7dIZc6eO6Hx9Vaz04pWkpFK4YwcFO0pD2J6VVeZ+r9hYfNq1Lf13ZRs549GFJJAvgwSygNI/40vHUTeRv3Gjc4oXAFot3q1b4dulM8ZGjTCEhqIPqeNWc53tObkU/v336QDeUbZ+QFcnGHO7ds4QlqvnVR0J5MsggSzKU3wygfxNm8jfuKn0xJOzhjbO0JrN6E+Hsz4kBENICPrQEPQhp/+FhqALCLjkA2KK3Y49Px9HTi723FzsOTml/+fmnf6/dFtJcgrWI0fgrB91rdkHnyuvxKdtW3zat8UrLk4OzFUTCeTLIIEsLsSen0/B1m0UbNlKSVIStrR0bOnpKCUlF3ysxmBAX6fOWUFdGt76oCAcRUWnQzX334B1Bm1u6bSzSv74agwGvFu0wKfdlfi0b4epSZMaMwbuaSSQL4MEsrgUiqLgyM2jJD3tdEBnYEtPx5aehi09g5L0dGxp6Tjy8i5rP1o/P3T+fuj8/f/9FxBQdltQEKamTeSsRDdRUabI2jdCVAGNRoMuwL/0uswNG563ncNiwZaR4exVl6SX/m/Pykbr7X1OwGoDzgpePz/p5dYwEshCqEhrMuEVHY1XdLTapQg3oFW7ACGEEKUkkIUQwk1IIAshhJuQQBZCCDchgSyEEG5CAlkIIdyEBLIQQrgJCWQhhHATEshCCOEmJJCFEMJNeOyp0wX5Bfw4dRb7dx/E7Gdm0Mj+dOjW/rztT8YnMO/7BZyMT8Bo9OKGQdfRs8811VixEEJUzGMDefb0+ej0et6Z/AYJxxP5cuJUouOiiYyJOKdtfl4+n0+Ywk23DebKTm2w22xkZ+aoULUQQpyfRw5ZWC1Wdm7dxYBhfTGajDRs2oBW7VqwZf22ctuvXLqG5q2b0rF7ewwGPSZvExHR4dVctRBCVMwje8ipyWlodVrCIsOc26Jjozi8/0i57eMPHycqNpJJb3xKWko69RrGMeKuYQSHBFVXyUIIcUEeGchWazEm77IX2zb5mLBYrOW2z87KIeF4AmOee4iomEgWzvyVaZ/P4KlXHy+3/fqVG1m/eiMA/W8bDnKBeiFENXDLQP5k/OTz9nYbNKnP8DuGYimylNluKbJiMhnLfYzBoKd1+1bUbRAHQL+hfXj+kVcoKizC2+fchSi79+5K995dgdKr+wshRHVwy0Ae+9KYCu+3Wqw47A5Sk9MIiyhdHTfxxCkiyjmgBxAdF1V2AUdZy1EI4YY88qCe0WSkTYdWLJ63DKvFytGDx9i9Yw+duncot33nqzuxc9tuEo4nYrfZWbZgOQ2a1C+3dyyEEGrx2EVOC/IL+OHrWRzYcxCznw+DRt7onId8+MBRvvhgChOnvudsv3bFen77ZTnF1hIaNqnPyNHDCKpz4YN6ssipEMKVZNXpyyCBLIRwpYoyxSOHLIQQoiaSQBZCCDchgSyEEG5CAlkIIdyEBLIQQrgJCWQhhHATEshCCOEmJJCFEMJNuOW1LNxJic1+SRcYys/Nx9fftwoqci1PqNMTagTPqNMTagTPqPNSayyx2c9/pyKqxPuvTFS7hErxhDo9oUZF8Yw6PaFGRfGMOquiRhmyEEIINyGBLIQQbkICuYp079lV7RIqxRPq9IQawTPq9IQawTPqrIoa5WpvQgjhJqSHLIQQbkICWQgh3ITMQ3aRgvwCfpw6i/27D2L2MzNoZH/nCiblORmfwLzvF3AyPgGj0YsbBl1Hzz7XuF2dADabjfde+hBrkZW3Pn3NrWpcsXglW9ZuIzMjC7Ovmauv68Z1N/ZWtS5FUfhl1iI2rNkMQLcenRk0akDZdR2rSGVrrM737XLqPKO6P4MXW6Orfp4lkF1k9vT56PR63pn8BgnHE/ly4lSi46KJLGfh1fy8fD6fMIWbbhvMlZ3aYLfZyM7Mcbs6z/hj8Sp8/XyxFlndr0YF7njoVqJiI0lPzWDy+18RFBxE+65tVatr/aqN7Nq+h+fHP40GmPz+V9QJrcNV13ZzeU2XWmN1vm+XVedp1f0ZvJgaXfnzLEMWLmC1WNm5dRcDhvXFaDLSsGkDWrVrwZb128ptv3LpGpq3bkrH7u0xGPSYvE1ERIe7XZ0A6akZbN2wnesHXlvl9V1KjdcN6E1svRh0Oh3hkWG0bteCo4eOqVrXlrXb6N2vJ0HBgQQGB9K7Xw82r93i8poup8bqet8ut06o/s/gxdboyp9nCWQXSE1OQ6vTEhYZ5twWHRtFckJyue3jDx/Hx+zDpDc+5YVHXuWriVPJTM9yuzoB5s74mYEj+uPlZajy+uDSajxDURSOHDxGZPT5e/vVUVdSYjLRcVH/touLJikxxeU1XU6NZ6vK9608F1tndX8G4eJqdOXPswSyC1itxZi8TWW2mXxMWCzl/3mVnZXDlnVbGXbHEN78+BXqhAYz7fMZblfnzm27cDgctOnQusprO+Niazzbkvm/4XA46HxNJ1XrslqsmHxMZdpZLVaqeobppb53Vfm+ledi6lTjMwgXV6Mrf55lDLkSPhk/mcP7j5R7X4Mm9Rl+x1AsRZYy2y1FVkwmY7mPMRj0tG7firoN4gDoN7QPzz/yCkWFRXj7eLtFnVaLlYUzF/HQ0/dfcj1VXePZ1ixfy5Z123jilUcxGFz/sTYavSpdl9FkLNPWUmTBaDJW+UG9i6nxjKp+38pT2Tqr6jNYGRfzXrry51kCuRLGvjSmwvutFisOu4PU5DTCIkIBSDxxiojzHKCIjosq+8Ppop9TV9aZlpJORnomH7/9GQB2m42iQgsvPvoa414bS53Q8pcxr84az9i4ZjMrfl3J2JcfJSg48JLqupCwiNBK1xUZHUHiiVPUa1jX2S6yGo4RXEyNUD3v2+XUWVWfQVfWCK79eZYhCxcwmoy06dCKxfOWYbVYOXrwGLt37KFT9w7ltu98dSd2bttNwvFE7DY7yxYsp0GT+pfVO3Z1nZExEbz18as8//Y4nn97HLfcOwq/AD+ef3scQXUC3aJGgK3rt/PrnCWMee4hQsLquEVdna7qwKpla8jOzCYnK4eVS1fT+eqqHw64mBqr6327nDrV+gxeTI3g2p9nOXXaRQryC/jh61kc2HMQs58Pg0be6JyzePjAUb74YAoTp77nbL92xXp++2U5xdYSGjapz8jRwwiqE+R2dZ5xaN9hvvvih2qbh1zZGl978m2ys7LR6//9Y69j9/bcfPeIaqvrvzUpisLCmYvYuGYTAF17dGHwzdU3D7kyNVbn+3Y5dZ6tOj+DF1ujq36eJZCFEMJNyJCFEEK4CQlkIYRwExLIQgjhJiSQhRDCTUggCyGEm5BAFkIINyGBLIQQbkICWQgh3IQEshBCuAkJZCGEcBMSyEII4SYkkIUQwk1IIAshhJuQQBZCCDchgSyEEG5CAlkIIdyEBLIQQrgJCWQhhHATEshCCOEmJJCFEMJN6C/cRAhxPsXWYmZNm8ffW3diNHrRo881HDsUj9nXzB0P3qJ2ecLDSCALcRl+/ukXDvxzgPseH01AUABLf/6dI/uP0LpDa7VLEx5IhiyEuERWi5VNazYzeNRAmrduRlRsJLc/cDMarUbt0oSHkkAW4hKlpaRjs9mp37iec5vRZCQyJlK9ooRHk0AWQgg3IYEsxCUKDQ9Bp9MRfzjeuc1qsZKUkKxeUcKjyUE9IS6R0WSka4/OLJy1GF8/39KDegt+R3E41C5NeCgJZCEuw5BbBmK1FvP1J9/i5eVFjxuuotharHZZwkNpFEVR1C5CiJrky4lTZR6yuCQyhiyEEG5CAlkIIdyEDFkIIYSbkB6yEEK4CQlkIYRwExLIQgjhJiSQhRDCTUggCyGEm5BAFkIIN/H/ByBMkkR/8LgAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "result = all_results[\"Robust\"][\"high_g\"]\n",
    "qv.plot_controls({f\"$\\\\gamma$\": result[\"output\"][\"ion_drive\"]})\n",
    "\n",
    "# Sum over ion index to obtain total displacement of the mode.\n",
    "plot_phase_space_trajectories(\n",
    "    np.sum(result[\"output\"][\"displacements\"][\"value\"], axis=3)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The complex control for the larger parametric drive (50 kHz) displayed above (chosen as an example) exhibits a Rabi rate close to the maximum value for most of its duration, while the phase exhibits more modulation over time. This control produces the symmetric *transformed* phase-space trajectory that satisfies the robustness condition for the transformed mode."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Verifying robustness to dephasing errors with a quasi-static scan\n",
    "\n",
    "We assess the robustness of the 'Robust' optimized pulses in comparison to the 'Standard' optimized pulses using [1D quasi-static error-susceptibility scans](https://docs.q-ctrl.com/boulder-opal/toolkit/design/design-error-robust-quantum-logic-gates/how-to-evaluate-control-susceptibility-to-quasi-static-noise). This involves calculating the infidelity of the controls over a systematic scan of the relative detunings (which corresponds to shifting the laser detuning). Note that this shift is applied to the lab-frame modes, and the effect of this noise can then be calculated by proceeding with the mode transformations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-11T06:10:25.879522Z",
     "start_time": "2021-06-11T06:10:14.353772Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your task (action_id=\"1827988\") is queued.\n",
      "Your task (action_id=\"1827988\") has started.\n",
      "Your task (action_id=\"1827988\") has completed.\n",
      "Your task (action_id=\"1827990\") has started.\n",
      "Your task (action_id=\"1827990\") has completed.\n",
      "Your task (action_id=\"1827993\") has started.\n",
      "Your task (action_id=\"1827993\") has completed.\n",
      "Your task (action_id=\"1827995\") has started.\n",
      "Your task (action_id=\"1827995\") has completed.\n",
      "Your task (action_id=\"1827996\") has started.\n",
      "Your task (action_id=\"1827996\") has completed.\n",
      "Your task (action_id=\"1827997\") has started.\n",
      "Your task (action_id=\"1827997\") has completed.\n"
     ]
    }
   ],
   "source": [
    "maxshift = 500  # Hz\n",
    "scan_point_count = 31\n",
    "shifts = np.linspace(-maxshift, maxshift, scan_point_count)\n",
    "\n",
    "all_scans = {}\n",
    "for robustness in all_parameters.keys():\n",
    "    all_scans[robustness] = {}\n",
    "\n",
    "    for key in all_parameters[robustness].keys():\n",
    "        parameters = all_parameters[robustness][key]\n",
    "        result = all_results[robustness][key]\n",
    "\n",
    "        drive_g = parameters[\"g\"]\n",
    "        laser_detuning = parameters[\"laser_detuning\"]\n",
    "        duration = parameters[\"duration\"]\n",
    "\n",
    "        output_node_names = [\n",
    "            f\"{robustness}_{key}_{number}\" for number in range(scan_point_count)\n",
    "        ]\n",
    "\n",
    "        graph = bo.Graph()\n",
    "\n",
    "        for shift, infidelity_name in zip(shifts, output_node_names):\n",
    "            # Set up trap modulation transformations.\n",
    "            driven_relative_detunings = mode_frequencies - laser_detuning + shift\n",
    "            delta_prime, r = calculate_modulation_parameters(\n",
    "                driven_relative_detunings[0, 1], drive_g\n",
    "            )\n",
    "            driven_relative_detunings[0, 1] = delta_prime\n",
    "\n",
    "            # Extract optimized drive.\n",
    "            drive = graph.pwc(**result[\"output\"][\"ion_drive\"])\n",
    "            ion_drive_mod = drive * np.cosh(r) + graph.conjugate(drive) * np.sinh(r)\n",
    "            drives = [ion_drive_mod] * ion_count\n",
    "\n",
    "            # Calculate Mølmer–Sørensen quantities.\n",
    "            ms_phases = graph.ions.ms_phases(\n",
    "                drives=drives,\n",
    "                lamb_dicke_parameters=lamb_dicke_parameters,\n",
    "                relative_detunings=driven_relative_detunings,\n",
    "            )\n",
    "\n",
    "            ms_displacements = graph.ions.ms_displacements(\n",
    "                drives=drives,\n",
    "                lamb_dicke_parameters=lamb_dicke_parameters,\n",
    "                relative_detunings=driven_relative_detunings,\n",
    "            )\n",
    "\n",
    "            ms_infidelity = graph.ions.ms_infidelity(\n",
    "                phases=ms_phases,\n",
    "                displacements=ms_displacements,\n",
    "                target_phases=target_phases,\n",
    "                name=infidelity_name,\n",
    "            )\n",
    "\n",
    "        # Run quasi-static scan.\n",
    "        dephasing_scan = bo.execute_graph(graph, output_node_names)\n",
    "        all_scans[robustness][key] = np.array(\n",
    "            [dephasing_scan[\"output\"][name][\"value\"] for name in output_node_names]\n",
    "        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x360 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 3, figsize=(20, 5))\n",
    "fig.suptitle(\"Quasi-static dephasing scans\")\n",
    "\n",
    "for robustness in [\"Robust\", \"Standard\"]:\n",
    "    for k, key in enumerate([\"no_g\", \"low_g\", \"high_g\"]):\n",
    "        drive_g = all_parameters[robustness][key][\"g\"]\n",
    "        duration = all_parameters[robustness][key][\"duration\"]\n",
    "        label = f\"{robustness} {duration*1e6:.1f} µs\"\n",
    "        axs[k].plot(shifts / 1e3, all_scans[robustness][key], label=label)\n",
    "        axs[k].set_ylim((-0.05, 1.05))\n",
    "        axs[k].set_title(f\"Parametric drive strength {drive_g/1e3:.2f} kHz\")\n",
    "        axs[k].set_xlabel(\"Laser detuning shift $\\\\Delta \\\\delta$ (kHz)\")\n",
    "        axs[k].set_ylabel(\"Infidelity\")\n",
    "        axs[k].legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The broader high-fidelity region indicates the benefit of the robust optimized pulses when there is quasi-static dephasing noise, for each of the parametric drive strengths. The parametric drive impacts the mediating mode, and is treated by a transformation on the mode. Higher parametric drive strengths reduce the robustness to detuning (or similarly mode frequency) noise. This can be understood by the robustness condition using the *transformed* mode providing less robustness with respect to the *lab-frame* mode as the parametric drive grows.\n",
    "\n",
    "Finally, notice from the legends that the robust gates are each faster than their standard counterparts that have lower (or no) parametric drive. Depending on the trap specifics and noise levels, the parametric drive strength could be chosen to enhance the gate time while giving rise only to acceptable noise susceptibility."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating Boulder Opal pulses for two-qubit gates mediated by multiple modes\n",
    "\n",
    "Mølmer–Sørensen-type operations can be mediated by more than a single mode; indeed this is necessary for more [complex multi-ion or parallel gate operations](https://docs.q-ctrl.com/boulder-opal/toolkit/apply/trapped-ion-quantum-computing/design-robust-configurable-parallel-gates-for-large-trapped-ion-arrays). This may also involve controls optimized individually for each ion to provide the necessary control degrees of freedom to perform the target operation. In this section, you can generate ion-specific modulated control drives that perform the target entangling gate mediated by multiple motional modes *in the presence of confining potential modulation*.\n",
    "\n",
    "As before, consider a parametric trap drive modifying a single motional mode. Applying a normal mode transformation for this driven mode can again give rise to Mølmer–Sørensen dynamics, when combined with other transformations. As for the single-mode-mediated gate, the relative detunings are transformed, however the ion drives are not specific to the modified mode (the drives are coupled to each mode) and cannot be freely transformed. Instead, by enforcing that the ion drives are real, the Lamb–Dicke parameter terms associated with the modified motional mode can be transformed to recover the Mølmer–Sørensen Hamiltonian form. \n",
    "\n",
    "In this section, we use Boulder Opal to explore the impact of the trap modulation on two-ion gates mediated by both modes. \n",
    "\n",
    "First, specify the number and type of ions, as well as other trap and laser characteristics. Note that the maximum Rabi rate has been increased from the previous section to enhance the coupling to both modes, and this will also result in faster gates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-11T06:10:55.731424Z",
     "start_time": "2021-06-11T06:10:51.881542Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your task (action_id=\"1827998\") has completed.\n"
     ]
    }
   ],
   "source": [
    "# Define trap and laser characteristics.\n",
    "ion_count = 2\n",
    "center_of_mass_frequencies = [5.9e6, 6.1e6, 1.5e6]\n",
    "wavevector = [(2 * np.pi) / 355e-9, 0, 0]\n",
    "\n",
    "maximum_rabi_rate = 2 * np.pi * 20e3 * 10\n",
    "\n",
    "\n",
    "# Collect Lamb–Dicke parameters as an array of shape [<axis>, <collective_mode>, <ion>]\n",
    "# and mode frequencies as an array of shape [<axis>, <collective_mode>].\n",
    "ion_chain_properties = bo.ions.obtain_ion_chain_properties(\n",
    "    atomic_mass=25,  # Mg ions\n",
    "    ion_count=ion_count,\n",
    "    center_of_mass_frequencies=center_of_mass_frequencies,\n",
    "    wavevector=wavevector,\n",
    ")\n",
    "lamb_dicke_parameters = ion_chain_properties[\"lamb_dicke_parameters\"]\n",
    "relative_detunings = ion_chain_properties[\"relative_detunings\"]\n",
    "\n",
    "# Target operation.\n",
    "target_phases = np.array([[0, 0], [np.pi / 4, 0]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generating standard pulses\n",
    "\n",
    "As above, here we demonstrate the fastest single-displacement-loop gate operations modified by the trap modulation. The following parameters have been chosen to characterize this operation, taking into account the transformations when a parametric drive with strength $g>0$ modifies a particular mode $p$. Here we specify that $p$ is the $x$-axis COM mode."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-11T06:10:55.737392Z",
     "start_time": "2021-06-11T06:10:55.734084Z"
    }
   },
   "outputs": [],
   "source": [
    "# Standard gate and control specifications.\n",
    "all_parameters = {}\n",
    "all_parameters[\"Standard\"] = {\n",
    "    \"no_g\": {\n",
    "        \"g\": 0,  # Hz\n",
    "        \"laser_detuning\": 5.9e6 - 29.3e3,  # Hz\n",
    "        \"duration\": 34.2e-6,  # s\n",
    "    },\n",
    "    \"with_g\": {\n",
    "        \"g\": 25e3,  # Hz\n",
    "        \"laser_detuning\": 5.9e6 - 46.75e3,  # Hz\n",
    "        \"duration\": 25.4e-6,  # s\n",
    "    },\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we optimize the drive control for the given parameters. In the following optimization, the real drives $\\gamma_j (t) = \\Omega_j (t)$ are separately optimized and individually address each ion $j$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def optimize_multiple_drives(\n",
    "    segment_count,\n",
    "    drive_g,\n",
    "    laser_detuning,\n",
    "    duration,\n",
    "    robust,\n",
    "    sample_count=50,\n",
    "    optimization_count=4,\n",
    "):\n",
    "    sample_times = np.linspace(0, duration, sample_count)\n",
    "    drive_names = [f\"ion_drive_{number}\" for number in range(ion_count)]\n",
    "\n",
    "    # Set up trap modulation transformations\n",
    "    driven_relative_detunings = mode_frequencies - laser_detuning\n",
    "    delta_prime, r = calculate_modulation_parameters(\n",
    "        driven_relative_detunings[0, 1], drive_g\n",
    "    )\n",
    "    driven_relative_detunings[0, 1] = delta_prime\n",
    "\n",
    "    # Transform the x-axis COM mode\n",
    "    driven_lamb_dicke_parameters = lamb_dicke_parameters.copy()\n",
    "    driven_lamb_dicke_parameters[0, 1] *= np.exp(r)  # sinh(r) + cosh(r)\n",
    "\n",
    "    # Optimize the controls\n",
    "    graph = bo.Graph()\n",
    "\n",
    "    if robust:\n",
    "        free_segment_count = int(np.ceil(segment_count / 2))\n",
    "        drives = [\n",
    "            graph.symmetrize_pwc(\n",
    "                graph.real_optimizable_pwc_signal(\n",
    "                    segment_count=free_segment_count,\n",
    "                    duration=duration / 2,\n",
    "                    maximum=maximum_rabi_rate,\n",
    "                    minimum=-maximum_rabi_rate,\n",
    "                ),\n",
    "                name=drive_name,\n",
    "            )\n",
    "            for drive_name in drive_names\n",
    "        ]\n",
    "    else:\n",
    "        drives = [\n",
    "            graph.real_optimizable_pwc_signal(\n",
    "                segment_count=segment_count,\n",
    "                duration=duration,\n",
    "                maximum=maximum_rabi_rate,\n",
    "                minimum=-maximum_rabi_rate,\n",
    "                name=drive_name,\n",
    "            )\n",
    "            for drive_name in drive_names\n",
    "        ]\n",
    "\n",
    "    ms_phases = graph.ions.ms_phases(\n",
    "        drives=drives,\n",
    "        lamb_dicke_parameters=driven_lamb_dicke_parameters,\n",
    "        relative_detunings=driven_relative_detunings,\n",
    "        sample_times=sample_times,\n",
    "        name=\"phases\",\n",
    "    )\n",
    "    ms_displacements = graph.ions.ms_displacements(\n",
    "        drives=drives,\n",
    "        lamb_dicke_parameters=driven_lamb_dicke_parameters,\n",
    "        relative_detunings=driven_relative_detunings,\n",
    "        sample_times=sample_times,\n",
    "        name=\"displacements\",\n",
    "    )\n",
    "    infidelity = graph.ions.ms_infidelity(\n",
    "        phases=ms_phases[-1],\n",
    "        displacements=ms_displacements[-1],\n",
    "        target_phases=target_phases,\n",
    "        name=\"infidelity\",\n",
    "    )\n",
    "\n",
    "    if robust:\n",
    "        robust_cost_term = graph.ions.ms_dephasing_robust_cost(\n",
    "            drives=drives,\n",
    "            lamb_dicke_parameters=driven_lamb_dicke_parameters,\n",
    "            relative_detunings=driven_relative_detunings,\n",
    "        )\n",
    "        cost = infidelity + robust_cost_term\n",
    "    else:\n",
    "        cost = infidelity + 0.0\n",
    "    cost.name = \"cost\"\n",
    "\n",
    "    return bo.run_optimization(\n",
    "        graph=graph,\n",
    "        cost_node_name=\"cost\",\n",
    "        output_node_names=[\"displacements\", \"infidelity\", \"phases\"] + drive_names,\n",
    "        optimization_count=optimization_count,\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-11T06:11:24.329276Z",
     "start_time": "2021-06-11T06:11:16.657825Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your task (action_id=\"1827999\") is queued.\n",
      "Your task (action_id=\"1827999\") has started.\n",
      "Your task (action_id=\"1827999\") has completed.\n",
      "Drive strength: 0.00 kHz\n",
      "Gate duration: 34.20 µs\n",
      "Infidelity: 3.762e-11\n",
      "Cost: 3.762e-11\n",
      "\n",
      "Your task (action_id=\"1828000\") has started.\n",
      "Your task (action_id=\"1828000\") has completed.\n",
      "Drive strength: 25.00 kHz\n",
      "Gate duration: 25.40 µs\n",
      "Infidelity: 1.080e-05\n",
      "Cost: 1.080e-05\n",
      "\n"
     ]
    }
   ],
   "source": [
    "segment_count = 20\n",
    "\n",
    "all_results = {}\n",
    "all_results[\"Standard\"] = {}\n",
    "for key, parameters in all_parameters[\"Standard\"].items():\n",
    "    drive_g = parameters[\"g\"]\n",
    "    laser_detuning = parameters[\"laser_detuning\"]\n",
    "    duration = parameters[\"duration\"]\n",
    "\n",
    "    result = optimize_multiple_drives(\n",
    "        segment_count, drive_g, laser_detuning, duration, robust=False\n",
    "    )\n",
    "\n",
    "    all_results[\"Standard\"][key] = result\n",
    "    print(f\"Drive strength: {drive_g / 1e3:.2f} kHz\")\n",
    "    print(f\"Gate duration: {duration * 1e6:.2f} µs\")\n",
    "    print(f\"Infidelity: {result['output']['infidelity']['value']:.3e}\")\n",
    "    print(f\"Cost: {result['cost']:.3e}\")\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-11T06:11:56.087746Z",
     "start_time": "2021-06-11T06:11:55.266649Z"
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "qv.plot_controls(\n",
    "    {\n",
    "        f\"$\\\\gamma_{k}$\": all_results[\"Standard\"][\"with_g\"][\"output\"][f\"ion_drive_{k}\"]\n",
    "        for k in range(ion_count)\n",
    "    }\n",
    ")\n",
    "\n",
    "# Sum over ion index to obtain total displacement of the mode.\n",
    "plot_phase_space_trajectories(\n",
    "    np.sum(\n",
    "        all_results[\"Standard\"][\"with_g\"][\"output\"][\"displacements\"][\"value\"], axis=3\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The real control with a parametric drive (25 kHz) is displayed above as an example. The control is close to the maximal Rabi rate throughout the gate, as expected for the minimal gate duration specified above. The *transformed* closed phase-space trajectory is also displayed for this control.\n",
    "\n",
    "Although both modes can be displaced to mediate the gate, the minimal gate duration exploits the maximally-coupling (COM) mode to perform the operation as fast as possible. In the next section, we explore longer gate durations for robust operations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generating robust pulses\n",
    "\n",
    "As for the gate mediated by a single mode, we optimize the gate operation to satisfy robustness conditions. The conditions apply to the transformed mode as well as any additional displaced modes. In the following cell, the parametric drive and laser detunings are specified as for the standard gates above, with the gate durations extended to accommodate robust solutions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-11T06:14:00.391863Z",
     "start_time": "2021-06-11T06:13:52.655522Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your task (action_id=\"1828001\") has started.\n",
      "Your task (action_id=\"1828001\") has completed.\n",
      "Drive strength: 0.00 kHz\n",
      "Gate duration: 50.00 µs\n",
      "Infidelity: 9.921e-12\n",
      "Cost: 2.991e-10\n",
      "\n",
      "Your task (action_id=\"1828002\") has started.\n",
      "Your task (action_id=\"1828002\") has completed.\n",
      "Drive strength: 25.00 kHz\n",
      "Gate duration: 40.00 µs\n",
      "Infidelity: 1.230e-10\n",
      "Cost: 3.103e-10\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Robust gate and control specifications.\n",
    "all_parameters[\"Robust\"] = {\n",
    "    \"no_g\": {\n",
    "        \"g\": 0,  # Hz\n",
    "        \"laser_detuning\": 5.9e6 - 29.3e3,  # Hz\n",
    "        \"duration\": 50e-6,  # s\n",
    "    },\n",
    "    \"with_g\": {\n",
    "        \"g\": 25e3,  # Hz\n",
    "        \"laser_detuning\": 5.9e6 - 46.75e3,  # Hz\n",
    "        \"duration\": 40e-6,  # s\n",
    "    },\n",
    "}\n",
    "\n",
    "all_results[\"Robust\"] = {}\n",
    "for key, parameters in all_parameters[\"Robust\"].items():\n",
    "    drive_g = parameters[\"g\"]\n",
    "    laser_detuning = parameters[\"laser_detuning\"]\n",
    "    duration = parameters[\"duration\"]\n",
    "\n",
    "    result = optimize_multiple_drives(\n",
    "        segment_count, drive_g, laser_detuning, duration, robust=True\n",
    "    )\n",
    "\n",
    "    all_results[\"Robust\"][key] = result\n",
    "    print(f\"Drive strength: {drive_g / 1e3:.2f} kHz\")\n",
    "    print(f\"Gate duration: {duration * 1e6:.2f} µs\")\n",
    "    print(f\"Infidelity: {result['output']['infidelity']['value']:.3e}\")\n",
    "    print(f\"Cost: {result['cost']:.3e}\")\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-11T06:14:08.834704Z",
     "start_time": "2021-06-11T06:14:07.975138Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "qv.plot_controls(\n",
    "    {\n",
    "        f\"$\\\\gamma_{k}$\": all_results[\"Robust\"][\"with_g\"][\"output\"][f\"ion_drive_{k}\"]\n",
    "        for k in range(ion_count)\n",
    "    }\n",
    ")\n",
    "\n",
    "# Sum over ion index to obtain total displacement of the mode.\n",
    "plot_phase_space_trajectories(\n",
    "    np.sum(all_results[\"Robust\"][\"with_g\"][\"output\"][\"displacements\"][\"value\"], axis=3)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The real control (the phase takes values of 0 or $\\pi$) with a parametric drive (25 kHz) is displayed above as an example. Here both modes exhibit displacement and thus mediate the gate, with symmetry imposed by the robustness conditions on both the transformed mode (mode 1) and the lab-frame mode (mode 0). The gate is still primarily mediated by the transformed and more strongly-coupled COM mode. Both modes are restored to zero displacement at the end of the operation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Verifying robustness to dephasing errors with a quasi-static scan\n",
    "\n",
    "We assess the robustness of the 'Robust' optimized pulses in comparison to the 'Standard' optimized pulses using 1D quasi-static scans. This involves calculating the infidelity of the controls over a systematic scan of the relative detunings (which corresponds to shifting the laser detuning). Note that this shift is applied to the lab-frame modes, and the effect of this noise can then be calculated by proceeding with the mode transformations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-11T06:14:32.846660Z",
     "start_time": "2021-06-11T06:14:25.137143Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your task (action_id=\"1828003\") has started.\n",
      "Your task (action_id=\"1828003\") has completed.\n",
      "Your task (action_id=\"1828004\") has started.\n",
      "Your task (action_id=\"1828004\") has completed.\n",
      "Your task (action_id=\"1828005\") has started.\n",
      "Your task (action_id=\"1828005\") has completed.\n",
      "Your task (action_id=\"1828006\") has started.\n",
      "Your task (action_id=\"1828006\") has completed.\n"
     ]
    }
   ],
   "source": [
    "maxshift = 1500  # Hz\n",
    "scan_point_count = 31\n",
    "shifts = np.linspace(-maxshift, maxshift, scan_point_count)\n",
    "\n",
    "all_scans = {}\n",
    "for robustness in all_parameters.keys():\n",
    "    all_scans[robustness] = {}\n",
    "\n",
    "    for key in all_parameters[robustness].keys():\n",
    "        graph = bo.Graph()\n",
    "\n",
    "        parameters = all_parameters[robustness][key]\n",
    "        result = all_results[robustness][key]\n",
    "\n",
    "        drive_g = parameters[\"g\"]\n",
    "        laser_detuning = parameters[\"laser_detuning\"]\n",
    "        duration = parameters[\"duration\"]\n",
    "\n",
    "        output_node_names = [\n",
    "            f\"{robustness}_{key}_{number}\" for number in range(scan_point_count)\n",
    "        ]\n",
    "\n",
    "        for shift, infidelity_name in zip(shifts, output_node_names):\n",
    "            # Set up trap modulation transformations.\n",
    "            driven_relative_detunings = mode_frequencies - laser_detuning + shift\n",
    "            delta_prime, r = calculate_modulation_parameters(\n",
    "                driven_relative_detunings[0, 1], drive_g\n",
    "            )\n",
    "            driven_relative_detunings[0, 1] = delta_prime\n",
    "\n",
    "            # Transform the x-axis COM mode.\n",
    "            driven_lamb_dicke_parameters = lamb_dicke_parameters.copy()\n",
    "            driven_lamb_dicke_parameters[0, 1] *= np.exp(r)  # sinh(r) + cosh(r)\n",
    "\n",
    "            # Extract optimized drives.\n",
    "            drives = [\n",
    "                graph.pwc(**result[\"output\"][f\"ion_drive_{k}\"])\n",
    "                for k in range(ion_count)\n",
    "            ]\n",
    "\n",
    "            # Calculate Mølmer–Sørensen quantities.\n",
    "            ms_phases = graph.ions.ms_phases(\n",
    "                drives=drives,\n",
    "                lamb_dicke_parameters=driven_lamb_dicke_parameters,\n",
    "                relative_detunings=driven_relative_detunings,\n",
    "            )\n",
    "            ms_displacements = graph.ions.ms_displacements(\n",
    "                drives=drives,\n",
    "                lamb_dicke_parameters=driven_lamb_dicke_parameters,\n",
    "                relative_detunings=driven_relative_detunings,\n",
    "            )\n",
    "            ms_infidelity = graph.ions.ms_infidelity(\n",
    "                phases=ms_phases,\n",
    "                displacements=ms_displacements,\n",
    "                target_phases=target_phases,\n",
    "                name=infidelity_name,\n",
    "            )\n",
    "\n",
    "        # Run quasi-static scan.\n",
    "        dephasing_scan = bo.execute_graph(graph, output_node_names)\n",
    "        all_scans[robustness][key] = np.array(\n",
    "            [dephasing_scan[\"output\"][name][\"value\"] for name in output_node_names]\n",
    "        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 2, figsize=(15, 5))\n",
    "fig.suptitle(\"Quasi-static dephasing scans\")\n",
    "\n",
    "for robustness in [\"Robust\", \"Standard\"]:\n",
    "    for k, key in enumerate([\"no_g\", \"with_g\"]):\n",
    "        drive_g = all_parameters[robustness][key][\"g\"]\n",
    "        duration = all_parameters[robustness][key][\"duration\"]\n",
    "        label = f\"{robustness} {duration*1e6:.1f} µs\"\n",
    "        axs[k].plot(shifts / 1e3, all_scans[robustness][key], label=label)\n",
    "        axs[k].set_title(f\"Parametric drive strength {drive_g/1e3:.2f} kHz\")\n",
    "        axs[k].set_xlabel(\"Laser detuning shift $\\\\Delta \\\\delta$ (kHz)\")\n",
    "        axs[k].legend()\n",
    "    axs[0].set_ylabel(\"Infidelity\")\n",
    "plt.show()"
   ]
  },
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   "source": [
    "The broader high-fidelity region indicates the benefit of the robust optimized pulses when there is quasi-static dephasing noise, both with and without parametric trap modulation. This robustness is achieved by amplitude modulation of the ion drives in this case. Here the impact of the parametric drive on the gate robustness is minimal, and the higher-amplitude ion drives and faster gates have greatly enhanced the robustness over the previous quasi-static scans above. The parametric drive permits faster high-fidelity standard and robust solutions, as for the gate mediated by a single motional mode."
   ]
  }
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