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    "# How to optimize error-robust Mølmer–Sørensen gates for trapped ions\n",
    "**Efficient state preparation using Mølmer–Sørensen-type interactions**\n",
    "\n",
    "Boulder Opal enables you to efficiently prepare quantum states of trapped ion qubits using Mølmer–Sørensen-type interactions.\n",
    "In this notebook we show how to find both optimal and robust control drives to create a (multi-particle) entangled state using specific functions designed for trapped-ion experiments."
   ]
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    "The interaction Hamiltonian for Mølmer–Sørensen-type operations is in the form of\n",
    "$$ H(t) = i\\frac{\\hbar}{2} \\sum_{j=1}^N \\sigma_{x, j} \\sum_{p=1}^{3N} \\eta_{pj} \\left( \n",
    "        \\gamma_{j}(t) e^{i \\delta_p t} a_{p}^\\dagger - \\gamma^\\ast_{j}(t) e^{-i \\delta_p t} a_p \\right)  ,\n",
    "$$\n",
    "where $N$ is the number of ions, $\\sigma_x$ is the Pauli $X$ operator, $a_p$ is the annihilation operator for the mode $p$, $\\eta_{pj}$ is the Lamb–Dicke parameter of mode $p$ for the ion $j$, $\\delta_p=\\nu_p-\\delta$ is the relative detuning of the laser frequency from the motional frequency of mode $p$, and $\\gamma_j(t)$ is the drive applied ion the ion $j$.\n",
    "\n",
    "Mølmer–Sørensen-type operations can be used to entangle multiple ions using pairs of lasers with opposite detunings and a fixed phase relationship.\n",
    "For these 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.\n",
    "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",
    "$$ U = \\exp\\left(i \\sum_{j=1}^{N-1} \\sum_{k=0}^{j-1} \\psi_{jk} \\sigma_x^j \\sigma_x^k \\right). $$\n",
    "\n",
    "The objective of the gate-design process is to find definitions for the different drives $\\gamma_j(t)$ which ensure high-fidelity gates which decouple all motional modes from the qubit degree of freedom.\n",
    "Gates may be designed to accumulate entangling phase either using interaction with only a single mode, or multiple modes.\n",
    "Note that multiple entangling operations can be performed in parallel, including multi-qubit entangling operations."
   ]
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   "source": [
    "## Summary workflow\n",
    "\n",
    "### 1. Define and calculate ion properties\n",
    "\n",
    "To find optimized drives, you first need to know the physical properties of the ions, namely the Lamb–Dicke parameters and mode frequencies.\n",
    "You can calculate these values using the function `boulderopal.ions.obtain_ion_chain_properties` by providing basic parameters of your ion trap:\n",
    "```python\n",
    "bo.ions.obtain_ion_chain_properties(\n",
    "    atomic_mass, ion_count, center_of_mass_frequencies, wavevector, laser_detuning\n",
    ")\n",
    "```\n",
    "\n",
    "The function returns the Lamb–Dicke parameters and relative detunings as a dictionary, which will be used in the phase and displacement calculations for ions."
   ]
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    "### 2. Optimize drives\n",
    "\n",
    "Next you need to set up the optimization properties.\n",
    "Define drives accessible in your system (for example, individual-ion beams with programmable moduli and phases) as described in the [robust optimization tutorial](https://docs.q-ctrl.com/boulder-opal/toolkit/design/design-error-robust-quantum-logic-gates/learn-to-design-robust-single-qubit-gates-using-computational-graphs).\n",
    "The drives are typically piecewise-constant, where the amplitude and phase take constant values for each pulse segment of the given laser drive.\n",
    "A programmable drive can be specified within a graph as follows:\n",
    "```python\n",
    "ion_drive = graph.complex_optimizable_pwc_signal(\n",
    "    segment_count=segment_count, maximum=drive_moduli_max, duration=duration\n",
    ")\n",
    "```\n",
    "\n",
    "Using the Lamb–Dicke parameters and relative detunings calculated above, particular drives give rise to [relative phases](https://doi.org/10.1002/qute.202000044) and [phase space displacements](https://doi.org/10.1002/qute.202000044) during an operation.\n",
    "You can calculate these gate properties using the `graph.ions.ms_phases` and `graph.ions.ms_displacements` graph operations, which can be obtained for a specified array of `sample_times`:\n",
    "```python\n",
    "ms_phases = graph.ions.ms_phases(\n",
    "    drives, lamb_dicke_parameters, relative_detunings, sample_times\n",
    ")\n",
    "ms_displacements = graph.ions.ms_displacements(\n",
    "    drives, lamb_dicke_parameters, relative_detunings, sample_times\n",
    ")\n",
    "```\n",
    "\n",
    "The [operational infidelity](https://doi.org/10.1002/qute.202000044) of the gate is determined by the relative phases and displacements at the final time of the drives.\n",
    "You can obtain the infidelity using the `graph.ions.ms_infidelity` operation:\n",
    "```python\n",
    "infidelity = graph.ions.ms_infidelity(\n",
    "    phases=ms_phases[-1], displacements=ms_displacements[-1], target_phases=target_phases\n",
    ")\n",
    "```\n",
    "\n",
    "Note that you need to format the target phases as a strictly lower triangular matrix (see the notes of the `graph.ions.ms_phases` operation for details).\n",
    "With this infidelity as the cost function, you can find the optimal drives using the Boulder Opal optimization engine.\n",
    "\n",
    "Visualize your optimized drives using [`plot_controls`](https://docs.q-ctrl.com/references/qctrl-visualizer/qctrlvisualizer/plot_controls) from the Q-CTRL Visualizer package.\n",
    "\n",
    "**Note**: a piecewise-constant control pulse can produce control frequencies that bring undesired transitions into resonance.\n",
    "To suppress these transitions for piecewise-constant controls, a low-pass filter can be applied to a given solution to eliminate these high-frequency components, typically with negligible fidelity loss.\n",
    "Alternatively, the segment durations of piecewise-constant controls can be selected to be multiples of the carrier detuning period.\n",
    "More generally, smooth controls can be optimized such that control frequency components lie below the carrier detuning, for instance using a sinc filter as demonstrated 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, and in the [How to add smoothing and band-limits to control optimization](https://docs.q-ctrl.com/boulder-opal/toolkit/design/design-model-based-controls/how-to-add-smoothing-and-band-limits-to-optimized-controls) user guide.\n"
   ]
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   "cell_type": "markdown",
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   "source": [
    "### 3. Optimize drives for error-robustness\n",
    "\n",
    "You may also seek to produce error-robust gates which exhibit resilience against common noise sources (for example, laser-detuning noise).\n",
    "Robustness to these noise sources is given by imposing a combination of symmetry in each ion's drive, as described in ([Milne et al., Phys. Rev. Applied, 2020](https://journals.aps.org/prapplied/abstract/10.1103/PhysRevApplied.13.024022)), and optimization such that the centre of mass of each mode's trajectory is at zero.\n",
    "A detailed discussion is given in [Bentley et al. (2020)](https://doi.org/10.1002/qute.202000044), and this robust control protocol to create the Bell state has also been recently demonstrated experimentally, as shown in [Milne et al. (2020)](https://doi.org/10.1103/PhysRevApplied.13.024022).\n",
    "\n",
    "You can optimize robust operations by both imposing symmetry in the drives, as demonstrated in the following examples, and by including the optimization cost term `graph.ions.ms_dephasing_robust_cost` to calculate the final displacement of each mode's centre of mass and use it in the Boulder Opal optimization engine to find the robust controls:\n",
    "```python\n",
    "robust_cost_term = graph.ions.ms_dephasing_robust_cost(\n",
    "    drives, lamb_dicke_parameters, relative_detunings,\n",
    ")\n",
    "cost = infidelity + robust_cost_term\n",
    "```\n"
   ]
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    "### 4. Validate performance\n",
    "#### Calculate phase-space displacement, relative phase, and infidelity dynamics\n",
    "Given the controls, you can visualize the trajectory of the center of a coherent state in (rotating) optical phase space for each mode.\n",
    "The closure of these trajectories is a necessary condition for an effective operation.\n",
    "\n",
    "You can return the mode displacements from the `graph.ions.ms_displacements` operation within your optimization, for the specified `sample_times`.\n",
    "The results of the `graph.ions.ms_displacements` operation describe contributions to each mode's displacement from each ion.\n",
    "To get the overall displacement for each mode, you need to sum over the ion dimension (see the notes of `graph.ions.ms_displacements` operation for details):\n",
    "```python\n",
    "trajectories = np.sum(result[\"output\"][\"displacements\"][\"value\"], axis=3)\n",
    "```\n",
    "\n",
    "To change the `sample_times` with a given optimized control, you can calculate the `graph.ions.ms_displacements` function within our flexible `qctrl.functions.calculate_graph` function, as shown in the [How to calculate system dynamics for arbitrary Mølmer–Sørensen gates](https://docs.q-ctrl.com/boulder-opal/toolkit/apply/trapped-ion-quantum-computing/how-to-calculate-system-dynamics-for-arbitrary-molmer-sorensen-gates) user guide.\n",
    "\n",
    "The same procedures can be applied to track how relative phases for each ion pair and infidelity change during the gate operational time, using `graph.ions.ms_phases` and `graph.ions.ms_infidelity`, respectively.\n",
    "For high-fidelity operations, the relative phase obtained at the end of the operation should match the target phase for each ion pair.\n",
    "\n",
    "#### Calculate robustness to dephasing and pulse timing errors with quasi-static scans\n",
    "\n",
    "You can assess the robustness of the 'Robust' optimized pulses in comparison to the 'Standard' optimized pulses using 1D quasi-static scans.\n",
    "By introducing a quasi-static offset of your choice, such as detuning, you can adapt and calculate the `graph.ions.ms_displacements` and `graph.ions.ms_phases` terms in the graph, which determine the error-dependent `graph.ions.ms_infidelity`."
   ]
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    "## Example: Optimal and robust Mølmer–Sørensen gates for two-qubit gates\n",
    "Here we explore a simple example in which we define optimal and robust control solutions implementing a Mølmer–Sørensen gate on a pair of Ytterbium ions.\n",
    "\n",
    "### Define and calculate ion properties\n",
    "\n",
    "For multi-qubit operations, first specify the number and type of ions, as well as other trap and laser characteristics, and collect the Lamb–Dicke parameters and relative detunings from the result.\n",
    "These will be used in the phase and displacement calculations for ions."
   ]
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   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import qctrlvisualizer as qv\n",
    "import boulderopal as bo\n",
    "\n",
    "plt.style.use(qv.get_qctrl_style())"
   ]
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      "Your task (action_id=\"1828728\") has completed.\n"
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    "# Define trap and laser characteristics.\n",
    "ion_count = 2\n",
    "maximum_rabi_rate = 2 * np.pi * 100e3\n",
    "laser_detuning = 1.6e6 + 4.7e3\n",
    "\n",
    "# Collect Lamb–Dicke parameters as an array of shape [<axis>, <collective_mode>, <ion>]\n",
    "# and relative detunings as an array of shape [<axis>, <collective_mode>].\n",
    "ion_chain_properties = bo.ions.obtain_ion_chain_properties(\n",
    "    atomic_mass=171,  # Yb ions\n",
    "    ion_count=ion_count,\n",
    "    center_of_mass_frequencies=[1.6e6, 1.5e6, 0.3e6],\n",
    "    wavevector=[(2 * np.pi) / 355e-9, (2 * np.pi) / 355e-9, 0],\n",
    "    laser_detuning=laser_detuning,\n",
    ")\n",
    "lamb_dicke_parameters = ion_chain_properties[\"lamb_dicke_parameters\"]\n",
    "relative_detunings = ion_chain_properties[\"relative_detunings\"]"
   ]
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    "# Operation duration in seconds.\n",
    "duration = 2e-4\n",
    "\n",
    "# Target phases: element (j,k) is the relative entanglement for ions j and k (k<j).\n",
    "target_phases = np.array([[0, 0], [np.pi / 4, 0]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we establish the graph that represents this system, which is used in the optimization itself.\n",
    "Note that in this example we have established functions to calculate both optimal and robust gates in the same codeblock."
   ]
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    "# Helper function for robustness.\n",
    "def reflect_signal(graph, moduli, phases, even_total_segment_count):\n",
    "    \"\"\"\n",
    "    This function reflects a drive signal about its temporal midpoint\n",
    "    (Milne et al., Phys. Rev. Applied, 2020).\n",
    "    The combined signal is returned.\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",
    "# Helper function for optimization with identical drives in all ions.\n",
    "def optimization_with_identical_drives(\n",
    "    ion_count,\n",
    "    segment_count,\n",
    "    duration,\n",
    "    maximum_rabi_rate,\n",
    "    lamb_dicke_parameters,\n",
    "    relative_detunings,\n",
    "    target_phases,\n",
    "    sample_times,\n",
    "    robust,\n",
    "):\n",
    "    graph = bo.Graph()\n",
    "\n",
    "    # Specification of free variables and combination with reflected signal.\n",
    "    if robust:\n",
    "        free_segment_count = (segment_count + 1) // 2\n",
    "    else:\n",
    "        free_segment_count = segment_count\n",
    "\n",
    "    # The drive moduli and phases are free variables here.\n",
    "    # They could also be restricted or fixed.\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",
    "\n",
    "    if robust:\n",
    "        moduli, phases = reflect_signal(graph, moduli, phases, segment_count % 2 == 0)\n",
    "\n",
    "    ion_drive = graph.complex_pwc_signal(\n",
    "        moduli=moduli, phases=phases, duration=duration, name=\"ion_drive\"\n",
    "    )\n",
    "\n",
    "    drives = [ion_drive] * ion_count\n",
    "\n",
    "    ms_phases = graph.ions.ms_phases(\n",
    "        drives=drives,\n",
    "        lamb_dicke_parameters=lamb_dicke_parameters,\n",
    "        relative_detunings=relative_detunings,\n",
    "        sample_times=sample_times,\n",
    "        name=\"phases\",\n",
    "    )\n",
    "\n",
    "    ms_displacements = graph.ions.ms_displacements(\n",
    "        drives=drives,\n",
    "        lamb_dicke_parameters=lamb_dicke_parameters,\n",
    "        relative_detunings=relative_detunings,\n",
    "        sample_times=sample_times,\n",
    "        name=\"displacements\",\n",
    "    )\n",
    "\n",
    "    infidelities = graph.ions.ms_infidelity(\n",
    "        phases=ms_phases,\n",
    "        displacements=ms_displacements,\n",
    "        target_phases=target_phases,\n",
    "        name=\"infidelities\",\n",
    "    )\n",
    "\n",
    "    infidelity = infidelities[-1]\n",
    "    infidelity.name = \"infidelity\"\n",
    "\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=relative_detunings,\n",
    "        )\n",
    "        cost = infidelity + robust_cost_term\n",
    "    else:\n",
    "        cost = infidelity + 0\n",
    "\n",
    "    cost.name = \"cost\"\n",
    "\n",
    "    return bo.run_optimization(\n",
    "        graph=graph,\n",
    "        optimization_count=4,\n",
    "        cost_node_name=\"cost\",\n",
    "        output_node_names=[\n",
    "            \"ion_drive\",\n",
    "            \"phases\",\n",
    "            \"displacements\",\n",
    "            \"infidelities\",\n",
    "            \"infidelity\",\n",
    "        ],\n",
    "    )\n",
    "\n",
    "\n",
    "# Helper function for optimization with different drives for each ion.\n",
    "def optimization_with_different_drives(\n",
    "    segment_count,\n",
    "    duration,\n",
    "    maximum_rabi_rate,\n",
    "    lamb_dicke_parameters,\n",
    "    relative_detunings,\n",
    "    target_phases,\n",
    "    sample_times,\n",
    "    robust,\n",
    "    drive_names,\n",
    "):\n",
    "    graph = bo.Graph()\n",
    "\n",
    "    # Specification of free variables and combination with reflected signal.\n",
    "    if robust:\n",
    "        free_segment_count = (segment_count + 1) // 2\n",
    "    else:\n",
    "        free_segment_count = segment_count\n",
    "\n",
    "    drives = []\n",
    "    for drive_name in drive_names:\n",
    "        # The drive moduli and phases are free variables here.\n",
    "        # They could also be restricted or fixed.\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",
    "\n",
    "        if robust:\n",
    "            moduli, phases = reflect_signal(\n",
    "                graph, moduli, phases, segment_count % 2 == 0\n",
    "            )\n",
    "\n",
    "        drives.append(\n",
    "            graph.complex_pwc_signal(\n",
    "                moduli=moduli, phases=phases, duration=duration, name=drive_name\n",
    "            )\n",
    "        )\n",
    "\n",
    "    ms_phases = graph.ions.ms_phases(\n",
    "        drives=drives,\n",
    "        lamb_dicke_parameters=lamb_dicke_parameters,\n",
    "        relative_detunings=relative_detunings,\n",
    "        sample_times=sample_times,\n",
    "        name=\"phases\",\n",
    "    )\n",
    "\n",
    "    ms_displacements = graph.ions.ms_displacements(\n",
    "        drives=drives,\n",
    "        lamb_dicke_parameters=lamb_dicke_parameters,\n",
    "        relative_detunings=relative_detunings,\n",
    "        sample_times=sample_times,\n",
    "        name=\"displacements\",\n",
    "    )\n",
    "\n",
    "    infidelities = graph.ions.ms_infidelity(\n",
    "        phases=ms_phases,\n",
    "        displacements=ms_displacements,\n",
    "        target_phases=target_phases,\n",
    "        name=\"infidelities\",\n",
    "    )\n",
    "\n",
    "    infidelity = infidelities[-1]\n",
    "    infidelity.name = \"infidelity\"\n",
    "\n",
    "    if not robust:\n",
    "        cost = infidelity + 0\n",
    "    else:\n",
    "        robust_cost_term = graph.ions.ms_dephasing_robust_cost(\n",
    "            drives=drives,\n",
    "            lamb_dicke_parameters=lamb_dicke_parameters,\n",
    "            relative_detunings=relative_detunings,\n",
    "        )\n",
    "        cost = infidelity + robust_cost_term\n",
    "\n",
    "    cost.name = \"cost\"\n",
    "\n",
    "    return bo.run_optimization(\n",
    "        graph=graph,\n",
    "        optimization_count=4,\n",
    "        cost_node_name=\"cost\",\n",
    "        output_node_names=[\"phases\", \"displacements\", \"infidelities\", \"infidelity\"]\n",
    "        + drive_names,\n",
    "    )"
   ]
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   "source": [
    "### Optimize drives (optimal control solution)\n",
    "\n",
    "Next, specify the control pulses according to the degrees of freedom in the hardware.\n",
    "In this optimization, the complex drive $\\gamma (t) = \\Omega e^{i \\phi}$ jointly addresses both ions in the chain.\n",
    "Alternatively, separate drives can be optimized to individually address each ion; graph encodings for both approaches are provided above.\n",
    "Here, the drives are defined by optimizable moduli and phases for individual segments (but note that, in general, you can define the drives using any of the techniques covered in the [Design robust single qubit gates using computational graphs](https://docs.q-ctrl.com/boulder-opal/toolkit/design/design-error-robust-quantum-logic-gates/learn-to-design-robust-single-qubit-gates-using-computational-graphs) tutorial).\n",
    "\n",
    "The optimizer attempts to minimize a particular cost.\n",
    "The cost is specified in the below cell\n",
    "to quantify the distances of the trajectory end-points from the origin, and the differences between the realized and target phases.\n",
    "The infidelity also quantifies the solution performance."
   ]
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     "name": "stdout",
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     "text": [
      "Your task (action_id=\"1828729\") has started.\n",
      "Your task (action_id=\"1828729\") has completed.\n",
      "Cost = Infidelity = 1.493e-11\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "segment_count = 64\n",
    "sample_times = np.linspace(0, duration, segment_count)\n",
    "\n",
    "result_optimal = optimization_with_identical_drives(\n",
    "    ion_count=ion_count,\n",
    "    segment_count=segment_count,\n",
    "    duration=duration,\n",
    "    maximum_rabi_rate=maximum_rabi_rate,\n",
    "    lamb_dicke_parameters=lamb_dicke_parameters,\n",
    "    relative_detunings=relative_detunings,\n",
    "    target_phases=target_phases,\n",
    "    sample_times=sample_times,\n",
    "    robust=False,\n",
    ")\n",
    "\n",
    "print(f\"Cost = Infidelity = {result_optimal['output']['infidelity']['value']:.3e}\")\n",
    "qv.plot_controls({\"$\\\\gamma$\": result_optimal[\"output\"][\"ion_drive\"]})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above figure displays the optimized pulse modulus $\\Omega (t)$ and phase $\\phi (t)$ that addresses both ions.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Optimize drives (robust control solution)\n",
    "\n",
    "Next we show how to optimize the drives to obtain the maximally-entangled state for two ions affected by dephasing noise, which is common in experiments due to various noise sources, for example frequency fluctuations in control lasers.\n",
    "The optimization procedure is extended by adding the robustness symmetry constraint and the calculation of `graph.ions.ms_dephasing_robust_cost` in the graph-based optimization; this is enabled in the helper function defined above by setting `robust=True`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your task (action_id=\"1828730\") has started.\n",
      "Your task (action_id=\"1828730\") has completed.\n",
      "Cost = 9.159e-12\n",
      "Infidelity = 3.539e-12\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "result_robust = optimization_with_identical_drives(\n",
    "    ion_count=ion_count,\n",
    "    segment_count=segment_count,\n",
    "    duration=duration,\n",
    "    maximum_rabi_rate=maximum_rabi_rate,\n",
    "    lamb_dicke_parameters=lamb_dicke_parameters,\n",
    "    relative_detunings=relative_detunings,\n",
    "    target_phases=target_phases,\n",
    "    sample_times=sample_times,\n",
    "    robust=True,\n",
    ")\n",
    "\n",
    "print(f\"Cost = {result_robust['cost']:.3e}\")\n",
    "print(f\"Infidelity = {result_robust['output']['infidelity']['value']:.3e}\")\n",
    "qv.plot_controls({\"$\\\\gamma$\": result_robust[\"output\"][\"ion_drive\"]})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above figure displays the dynamics of the modulus and angle of the robust optimized pulse.\n",
    "The symmetry in time of the modulus values can be observed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Validate performance\n",
    "#### Calculate phase space trajectories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_phase_space_trajectories(total_displacement, title):\n",
    "    fig, axs = plt.subplots(1, 2, figsize=(10, 5))\n",
    "    plot_range = 1.1 * np.max(np.abs(total_displacement))\n",
    "    fig.suptitle(f\"Phase space trajectories ({title})\", y=1.1)\n",
    "\n",
    "    for k in range(2):\n",
    "        for mode in range(ion_count):\n",
    "            axs[k].plot(\n",
    "                np.real(total_displacement[:, k, mode]),\n",
    "                np.imag(total_displacement[:, k, mode]),\n",
    "                label=f\"mode {mode % 2}\",\n",
    "            )\n",
    "            axs[k].plot(\n",
    "                np.real(total_displacement[-1, k, mode]),\n",
    "                np.imag(total_displacement[-1, k, mode]),\n",
    "                \"kx\",\n",
    "                markersize=15,\n",
    "            )\n",
    "\n",
    "        axs[k].set_xlim(-plot_range, plot_range)\n",
    "        axs[k].set_ylim(-plot_range, plot_range)\n",
    "        axs[k].set_aspect(\"equal\")\n",
    "        axs[k].set_xlabel(\"q\")\n",
    "\n",
    "    axs[0].set_title(\"$x$-axis\")\n",
    "    axs[0].set_ylabel(\"p\")\n",
    "    axs[1].set_title(\"$y$-axis\")\n",
    "    axs[1].yaxis.set_ticklabels([])\n",
    "\n",
    "    hs, ls = axs[0].get_legend_handles_labels()\n",
    "    fig.legend(\n",
    "        handles=hs, labels=ls, loc=\"center\", bbox_to_anchor=(0.5, 1), ncol=ion_count\n",
    "    )\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x360 with 2 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_optimal[\"output\"][\"displacements\"][\"value\"], axis=3),\n",
    "    \"optimal controls\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "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 $m$.\n",
    "The black cross marks the final displacement for each mode.\n",
    "These are overlapping at zero, indicating no residual state-motional entanglement and no motional heating caused by the operations.\n",
    "The z-axis modes are not addressed, and thus have no excursions in phase space.\n",
    "\n",
    "Next visualize the phase space trajectories for the robust optimized control."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_phase_space_trajectories(\n",
    "    np.sum(result_robust[\"output\"][\"displacements\"][\"value\"], axis=3), \"robust controls\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, the center of mass (the value of the integrated trajectory) for each mode is optimized to be close to the origin.\n",
    "Again, the black crosses at the origin indicate no residual state-motional entanglement, which arises from satisfying the center of mass and symmetry conditions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Calculate relative phase dynamics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "times = sample_times * 1e3\n",
    "phases = result_optimal[\"output\"][\"phases\"][\"value\"]\n",
    "\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.suptitle(\"Relative phase dynamics (optimal controls)\")\n",
    "plt.plot(times, phases[:, 1, 0], label=\"(0, 1)\")\n",
    "plt.plot([0, times[-1]], [np.pi / 4, np.pi / 4], \"k--\")\n",
    "plt.yticks([0, np.pi / 4], [\"0\", r\"$\\frac{\\pi}{4}$\"])\n",
    "plt.xlabel(\"Time (ms)\")\n",
    "plt.ylabel(\"Relative phase\")\n",
    "plt.legend(title=\"Ion pair\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The figure shows the dynamics of the relative phase between the two ions, over the duration of the gate.\n",
    "Under the optimized control, this evolves from 0 to the target maximally-entangled phase of $\\pi/4$ at the end of the operation.\n",
    "The target phase is marked by the horizontal dashed black line.\n",
    "\n",
    "Next obtain the phase accumulation for the robust optimized control."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "times = sample_times * 1e3\n",
    "phases = result_robust[\"output\"][\"phases\"][\"value\"]\n",
    "\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.suptitle(\"Relative phase dynamics (robust controls)\")\n",
    "plt.plot(times, phases[:, 1, 0], label=\"(0, 1)\")\n",
    "plt.plot([0, times[-1]], [np.pi / 4, np.pi / 4], \"k--\")\n",
    "plt.yticks([0, np.pi / 4], [\"0\", r\"$\\frac{\\pi}{4}$\"])\n",
    "plt.xlabel(\"Time (ms)\")\n",
    "plt.ylabel(\"Relative phase\")\n",
    "plt.legend(title=\"Ion pair\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By the end of the robust operation, the relative phase of the ions has reached the target value of $\\pi/4$, marked by the dashed black line."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Calculate robustness to dephasing and pulse timing errors with quasi-static scans\n",
    "\n",
    "You can assess the robustness of the 'Robust' optimized pulses in comparison to the 'Standard' optimized pulses using 1D quasi-static scans.\n",
    "First consider a scan of scaling factors for the pulse timings.\n",
    "The scaling factors are applied uniformly: they scale the timing for the entire operation by the same factor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your task (action_id=\"1828731\") has started.\n",
      "Your task (action_id=\"1828731\") has completed.\n"
     ]
    }
   ],
   "source": [
    "timing_scan_points = 21\n",
    "max_timing_shift = 0.02\n",
    "time_shifts = np.linspace(\n",
    "    1 - max_timing_shift, 1 + max_timing_shift, timing_scan_points\n",
    ")\n",
    "\n",
    "optimal_infidelity_names = [\n",
    "    f\"infidelity_{number}\" for number in range(timing_scan_points)\n",
    "]\n",
    "robust_infidelity_names = [\n",
    "    f\"robust_infidelity_{number}\" for number in range(timing_scan_points)\n",
    "]\n",
    "\n",
    "graph = bo.Graph()\n",
    "\n",
    "for result, infidelity_names in [\n",
    "    (result_optimal, optimal_infidelity_names),\n",
    "    (result_robust, robust_infidelity_names),\n",
    "]:\n",
    "    for time_shift, infidelity_name in zip(time_shifts, infidelity_names):\n",
    "        ion_drive = graph.pwc_signal(\n",
    "            values=result[\"output\"][\"ion_drive\"][\"values\"],\n",
    "            duration=duration * time_shift,\n",
    "        )\n",
    "\n",
    "        drives = [ion_drive] * 2\n",
    "\n",
    "        ms_phases = graph.ions.ms_phases(\n",
    "            drives=drives,\n",
    "            lamb_dicke_parameters=lamb_dicke_parameters,\n",
    "            relative_detunings=relative_detunings,\n",
    "        )\n",
    "\n",
    "        ms_displacements = graph.ions.ms_displacements(\n",
    "            drives=drives,\n",
    "            lamb_dicke_parameters=lamb_dicke_parameters,\n",
    "            relative_detunings=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",
    "timing_scan = bo.execute_graph(\n",
    "    graph=graph, output_node_names=optimal_infidelity_names + robust_infidelity_names\n",
    ")\n",
    "\n",
    "optimal_infidelities_timing = [\n",
    "    timing_scan[\"output\"][name][\"value\"] for name in optimal_infidelity_names\n",
    "]\n",
    "robust_infidelities_timing = [\n",
    "    timing_scan[\"output\"][name][\"value\"] for name in robust_infidelity_names\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, calculate the robustness of the optimized pulses using a systematic scan of the relative detunings (which corresponds to shifting the laser detuning)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your task (action_id=\"1828732\") has started.\n",
      "Your task (action_id=\"1828732\") has completed.\n"
     ]
    }
   ],
   "source": [
    "dephasing_scan_points = 21\n",
    "max_dephasing_shift = 0.4 * np.min(np.abs(relative_detunings))\n",
    "dephasing_shifts = np.linspace(\n",
    "    -max_dephasing_shift, max_dephasing_shift, dephasing_scan_points\n",
    ")\n",
    "\n",
    "optimal_infidelity_names = [\n",
    "    f\"infidelity_{number}\" for number in range(dephasing_scan_points)\n",
    "]\n",
    "robust_infidelity_names = [\n",
    "    f\"robust_infidelity_{number}\" for number in range(dephasing_scan_points)\n",
    "]\n",
    "\n",
    "graph = bo.Graph()\n",
    "\n",
    "for result, infidelity_names in [\n",
    "    (result_optimal, optimal_infidelity_names),\n",
    "    (result_robust, robust_infidelity_names),\n",
    "]:\n",
    "    for dephasing_shift, infidelity_name in zip(dephasing_shifts, infidelity_names):\n",
    "        ion_drive = graph.pwc(**result[\"output\"][\"ion_drive\"])\n",
    "        drives = [ion_drive] * 2\n",
    "\n",
    "        ms_phases = graph.ions.ms_phases(\n",
    "            drives=drives,\n",
    "            lamb_dicke_parameters=lamb_dicke_parameters,\n",
    "            relative_detunings=relative_detunings + dephasing_shift,\n",
    "        )\n",
    "\n",
    "        ms_displacements = graph.ions.ms_displacements(\n",
    "            drives=drives,\n",
    "            lamb_dicke_parameters=lamb_dicke_parameters,\n",
    "            relative_detunings=relative_detunings + dephasing_shift,\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",
    "dephasing_scan = bo.execute_graph(\n",
    "    graph=graph, output_node_names=optimal_infidelity_names + robust_infidelity_names\n",
    ")\n",
    "\n",
    "optimal_infidelities_dephasing = [\n",
    "    dephasing_scan[\"output\"][name][\"value\"] for name in optimal_infidelity_names\n",
    "]\n",
    "robust_infidelities_dephasing = [\n",
    "    dephasing_scan[\"output\"][name][\"value\"] for name in robust_infidelity_names\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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 scans\", y=1.1)\n",
    "\n",
    "axs[0].set_title(\"Timing noise\")\n",
    "axs[0].plot(time_shifts, robust_infidelities_timing, label=\"Robust\")\n",
    "axs[0].plot(time_shifts, optimal_infidelities_timing, label=\"Standard\")\n",
    "axs[0].set_xlabel(\"Pulse timing scaling factor\")\n",
    "axs[0].set_ylabel(\"Infidelity\")\n",
    "\n",
    "axs[1].set_title(\"Dephasing noise\")\n",
    "axs[1].plot(dephasing_shifts / 1e3, robust_infidelities_dephasing, label=\"Robust\")\n",
    "axs[1].plot(dephasing_shifts / 1e3, optimal_infidelities_dephasing, label=\"Standard\")\n",
    "axs[1].set_xlabel(\"Laser detuning shift $\\\\Delta \\\\delta$ (kHz)\")\n",
    "axs[1].set_ylabel(\"Infidelity\")\n",
    "\n",
    "hs, ls = axs[0].get_legend_handles_labels()\n",
    "fig.legend(handles=hs, labels=ls, loc=\"center\", bbox_to_anchor=(0.5, 1.0), ncol=2)\n",
    "\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 or noise in the control pulse timings, demonstrating the utility of the robust solution."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Example: GHZ state on a chain of three ions\n",
    "\n",
    "In this example we consider a chain of three ions (${}^{171}{\\rm Yb}^{+}$), and the goal is to find a set of drives such that, at the end of the drives, the relative phase between each ion pair is $\\pi/4$.\n",
    "Note that you can perform $X_{\\pi/2}$ rotations for all ions after applying the optimal drives to create the GHZ state, as shown in [Kim et al. (2009)](https://doi.org/10.1103/PhysRevLett.103.120502).\n",
    "\n",
    "### Define and calculate ion properties\n",
    "We begin by calculating ion properties based on inputs relevant to the system under examination."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-08T02:25:01.290374Z",
     "start_time": "2021-07-08T02:25:01.286023Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your task (action_id=\"1828733\") has completed.\n"
     ]
    }
   ],
   "source": [
    "# Specify ion trap parameters.\n",
    "ion_count = 3\n",
    "atomic_mass = 171\n",
    "center_of_mass_frequencies = [1.6e6, 1.5e6, 0.3e6]\n",
    "\n",
    "# Laser characteristics.\n",
    "wavevector = [1.8e7, 1.8e7, 0]\n",
    "laser_detuning = 1.6047e6\n",
    "maximum_rabi_rate = 2 * np.pi * 100e3\n",
    "\n",
    "# Calculate ion chain properties.\n",
    "ion_chain_properties = bo.ions.obtain_ion_chain_properties(\n",
    "    atomic_mass=atomic_mass,\n",
    "    ion_count=ion_count,\n",
    "    center_of_mass_frequencies=center_of_mass_frequencies,\n",
    "    wavevector=wavevector,\n",
    "    laser_detuning=laser_detuning,\n",
    ")\n",
    "lamb_dicke_parameters = ion_chain_properties[\"lamb_dicke_parameters\"]\n",
    "relative_detunings = ion_chain_properties[\"relative_detunings\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Optimize drives\n",
    "\n",
    "In this example, the drive for each ion is defined by optimizable moduli and phases for individual segments (but note that, in general, you can define the drives using any of the techniques covered in the [Design robust single qubit gates using computational graphs](https://docs.q-ctrl.com/boulder-opal/toolkit/design/design-error-robust-quantum-logic-gates/learn-to-design-robust-single-qubit-gates-using-computational-graphs) tutorials).\n",
    "In this example we only seek an optimal solution rather than a robust solution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your task (action_id=\"1828734\") has started.\n",
      "Your task (action_id=\"1828734\") has completed.\n"
     ]
    }
   ],
   "source": [
    "# Set up the target phases as a strictly lower triangular matrix.\n",
    "target_phases = (np.pi / 4) * np.tri(ion_count, k=-1)\n",
    "\n",
    "# Number of optimizable PWC segments in the drives.\n",
    "segment_count = 128\n",
    "\n",
    "# Total durations of the drives.\n",
    "duration = 2e-4\n",
    "\n",
    "# Drive node names for retrieving the results.\n",
    "drive_names = [\"drive_\" + str(i) for i in range(ion_count)]\n",
    "\n",
    "sample_times = np.linspace(0, duration, 300)\n",
    "\n",
    "optimization_result = optimization_with_different_drives(\n",
    "    segment_count=segment_count,\n",
    "    duration=duration,\n",
    "    maximum_rabi_rate=maximum_rabi_rate,\n",
    "    lamb_dicke_parameters=lamb_dicke_parameters,\n",
    "    relative_detunings=relative_detunings,\n",
    "    target_phases=target_phases,\n",
    "    sample_times=sample_times,\n",
    "    robust=False,\n",
    "    drive_names=drive_names,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Resulting phases: \n",
      " [[0.         0.         0.        ]\n",
      " [0.7853925  0.         0.        ]\n",
      " [0.78541861 0.78539406 0.        ]]\n",
      "Target phases: \n",
      " [[0.         0.         0.        ]\n",
      " [0.78539816 0.         0.        ]\n",
      " [0.78539816 0.78539816 0.        ]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(f\"Resulting phases: \\n {optimization_result['output']['phases']['value'][-1]}\")\n",
    "print(f\"Target phases: \\n {target_phases}\")\n",
    "qv.plot_controls({\"drive_0\": optimization_result[\"output\"][\"drive_0\"]})\n",
    "plt.suptitle(\"Optimal control for ion 0\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot gate dynamics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 2, figsize=(15, 5))\n",
    "times = sample_times * 1e3\n",
    "\n",
    "axes[0].plot(times, optimization_result[\"output\"][\"infidelities\"][\"value\"])\n",
    "axes[0].set_title(\"Infidelity dynamics\")\n",
    "axes[0].set_xlabel(\"Sample times (ms)\")\n",
    "axes[0].set_ylabel(\"Infidelity\")\n",
    "\n",
    "# Recall that the phases are stored as a strictly lower triangular matrix.\n",
    "# See the notes part of the ms_phases graph operation.\n",
    "phases = optimization_result[\"output\"][\"phases\"][\"value\"]\n",
    "for ion_1 in range(3):\n",
    "    for ion_2 in range(ion_1):\n",
    "        axes[1].plot(times, phases[:, ion_1, ion_2], label=f\"{ion_2, ion_1}\")\n",
    "axes[1].plot([0, times[-1]], [np.pi / 4, np.pi / 4], \"k--\")\n",
    "axes[1].set_title(\"Relative phase dynamics\")\n",
    "axes[1].set_xlabel(\"Sample times (ms)\")\n",
    "axes[1].set_ylabel(\"Relative phase\")\n",
    "axes[1].legend(title=\"Ion pair\")\n",
    "\n",
    "# Mode displacement trajectories for radial dimensions.\n",
    "plot_phase_space_trajectories(\n",
    "    np.sum(optimization_result[\"output\"][\"displacements\"][\"value\"], axis=-1),\n",
    "    \"individual addressing\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above figure shows the infidelity and relative phases over the gate duration (top), as well as the motional phase space trajectories (bottom), which are closed loops indicating no final displacement for each mode."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summary\n",
    "\n",
    "In these examples, you have seen how Boulder Opal can easily calculate the important physical properties for your ion trap, efficiently find optimal or robust drives for state preparation, and track the ion states in phase space at any sample points during the gate operational time.\n",
    "\n",
    "You can apply the same optimization strategy to large systems involving tens of qubits.\n",
    "This process is demonstrated 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, where you will learn about how the Boulder Opal optimization engine enables you to efficiently find optimal and noise-robust control pulses for large trapped ion systems."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
